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The Mind Explained in One Page or Less

I’m not going to lead you through 300 pages of counterintuitive double-talk that never quite seems to get to the point. In fact, I’ll explain how the mind works right here on the first page. And then I’m going to do it again in a bit more detail over a few pages, and then across a few chapters, and then in the rest of the book. Each iteration will go into more detail, will be better supported, and will expand our understanding of the mind further. But it will all be intuitive and you will say, “Sure, now tell me something I didn’t already know.” Because it should be intuitive and we already know the answers; we just don’t quite realize it. So here goes.

From a high level, it is easy to understand what the mind does. But you have to understand evolution first. Fortunately, evolution is even easier to understand. Evolution works by induction, which means trial and error. It keeps trying. It makes errors. It detects the errors and uses that feedback to try another way that will avoid the earlier mistakes. It’s pretty much the same approach machine learning uses, using feedback to improve future responses. The mind evolved as a high-level control center of the brain, which is the control center of the body. Unlike evolution, which gathers information slowly from natural selection, brains and minds gather real-time information from experience. Their basic strategy for doing that is also inductive trial and error. But minds, especially human minds, also use deduction. Where induction works from the bottom up (from specifics to generalities), deduction worked from the top down (generalities to specifics). Understanding and knowledge come from joining the two together. Most of the brain’s work is inductive and outside conscious control, producing senses, feelings, common sense and intuition, while deduction happens under conscious control, along with more induction and blending the two. Consciousness is just the product of connecting inductive and deductive frameworks together to construct an imaginary but practical inner realm. We think of our perspective as being ineffable, but it is only the logical consequence of applying logical models to target circumstances. A computer program with the same sort of inputs and meld of inductive and deductive logic would “feel” conscious as well. Our inner world is not magic, it is computed. But, just to be clear, such programs are not even on the horizon; it is not for nothing evolution needed billions of years to pull this off. So that is the mind in a nutshell.

Approaching the Mind Scientifically

“You unlock this door with the key of imagination. Beyond it is another dimension: a dimension of sound, a dimension of sight, a dimension of mind. You’re moving into a land of both shadow and substance, of things and ideas. You’ve just crossed over into… the Twilight Zone.” — Rod Serling

Many others before me have attempted to explain the functional basis of the mind. They’ve gotten a lot of the details right, but taken as a whole, no theory presented to date adequately explains the mind as we experience it. Our deeply held intuitions about the special character of the mind are quite true, but science has found no purchase to get at them. My premise is that nearly everything we think we know about the mind is true and that science needs to catch up with common sense. The conventional approach of science is to write off all our intuitions as fantasy, the biased illusions of wishful thinking that evolved to lead us down adaptive garden paths rather than to understand the mind. But this is misguided; minds are not designed to be deluded, they are designed to collect useful knowledge, and we each already have encyclopedic knowledge about how our mind works. This is not to say minds are immune to delusion or bias, which can happen for many practical reasons. But with a considered approach we can get past such gullibility.

Our most considered approach to figuring things out is science. The cornerstone quality of science is objectivity. What objective exactly means is a topic I will discuss in greater detail later, but from a high level it means a perspective that is independent of each of our subjective perspectives. Knowledge that can be shown to be outside of us is the same for everyone and can count as scientific truth. For this reason, I am developing a scientific perspective here, not a spiritual or fantastic one. But is it even possible to develop an objective way of studying the mind, which seems to be an entirely subjective phenomenon? We only know about minds because we have them and can think about what they are up to. We can’t see what they are doing using instruments. Or, rather, we can see through brain scans that areas of the brain are active when our minds are active, and we can even approximately tell what areas of the brain are related to what aspects of the mind by correlating what people report is happening in their mind to what is happening in their brain. But science seems to be inadequately equipped to make sense of mental states in a way that makes sense to us. I’m going to dig into the philosophy of science to sort this out. We will have to consider more closely the nature of the object under study and what we are expecting science to accomplish. And we will find that deriving an appropriate philosophy of science is closely related to understanding the mind because both search for the nature of knowledge and truth. As we move into this twilight zone, we should remain cognizant of Richard Feynman’s injunction against cargo cult science, which he said could only be avoided by “scientific integrity, which he described as, “a kind of leaning over backwards” to make sure scientists do not fool themselves or others.” What I am proposing here is especially at risk of this because I am playing with the very structure of science and its implications at the highest levels. I’ve tried to review everything I have written to ferret out any overreach, but reach in this area still has many subjective qualities. Still, I believe that a coherent, unified theory is now possible and I hope my approach helps pave the way.

Science has been fighting some pitched philosophical debates in recent decades which have reached a standstill and left it on pretty shaky ground. These skirmishes don’t affect most scientific fields because they can make local progress without a perfect overall view, but the science of mind can go nowhere without a firm foundation. So I’m going to establish that first and then start to draw out the logical implications, decomposing the mind from the top down in a general way. I am going to have to make some guesses. A scientific hypothesis is a guess which gets promoted to a theory once it has the backing of rigorous experimentation and evidence. I’m not doing field research here, and my investigation will encompass many fields, so I will mostly look to well-established theories to support my hypothesis. Where theory has not been adequately established, I will have to hypothesize, but I will also cite some credible published hypotheses. I will adjust and consolidate these theories and hypotheses to form a unified hypothesis. Because I am using an iterative approach to present my ideas in increasingly more detail, I have to ask you to bear with me. Each iteration can only go so deep, but I will try to get to all the issues and provide sufficient support. If you accept the underlying theories, then you should find my conclusions to be well-supported and relatively non-controversial. That said, the fields of science involved are works in progress, so recent thinking is inherently unsettled and controversial. But my goal is to stay within the bounds of the conclusions of science and common sense, even though I will be reframing our conception of the scope of both.

First, the major theories from which I plan to draw support:

  1. Physicalism, the idea that only physical entities comprised of matter and energy exist. The predominant paradigm sees these entities’ behavior governed by four fundamental forces, which include gravity, the electromagnetic force, and the strong and weak nuclear forces. The latter three are nicely wrapped up into the Standard Model of particle physics, and gravity by general relativity. Although a grand unified theory remains elusive, physicalists recognize that even if no exception to such a theory could be found, it would not prove it was correct and would not reveal why the universe behaves as it does.

  2. Evolution, the idea that inanimate matter become animate over time through a succession of heritable changes. The paradigm Darwin introduced in 1859 itself evolved during the first half of the 20th century into the Modern Synthesis to incorporated genetic traits and rules of recombination and population genetics. Watson and Crick’s discovery of DNA in 1953 as the source of the genetic code provided the molecular basis for this theory. Since that time, however, our knowledge of molecular mechanisms has exploded, undermining much of that paradigm. “In 2009, the evolutionary biologist Eugene Koonin stated that while “the edifice of the [early 20th century] Modern Synthesis has crumbled, apparently, beyond repair”, a new 21st-century synthesis could be glimpsed.”1 Most notably, we see a bigger picture in which the biochemistry and evolutionary mechanisms shared by all existing organisms took perhaps 0.5 to 1 billion years to evolve, and probably another billion years of refinements before before the eukaryotes (organisms whose cells have a nucleus) appeared about 2 billion years ago. Although we now know that genes encoded by DNA produce all the proteins that in turn manage cellular metabolism, we have been quite surprised to discover in recent decades that this only explains about 2% of our DNA. Most of the remaining DNA regulates when proteins are deployed and acts as building blocks for new genes. In the early days of evolution, forms that could build genes that were more likely to be useful outcompeted forms without this kind of adaptive foresight. So genetic change is not only not at the whim of random mutations, it is a carefully orchestrated cellular function, and it responds to pressures of natural selection in ways we can only guess at for now.23

  3. Computational Theory of Mind, the idea that the human mind is an information processing system and that both cognition and consciousness result from this computation. According to this theory, computation is generalized to be a transformation of inputs and internal states using rules to produce outputs. Where mechanical computers use symbolic states stored in digital memory and manipulated electronically, neural computers use neurochemical inputs, states, outputs, and rules. This theory, more than any other, has guided my thinking in this book. It is considered by many, including me, to be the only scientific theory that appears capable of providing a natural explanation for the much if not all of the mind’s capabilities. However, I largely reject the ideas of the representational theory of mind and especially the language of thought, as they unnecessarily and incorrectly go too far in proposing a rigid algorithmic approach when a more generalized solution is needed. Note that whenever I use the word “process” in this book, I mean a computational information process, unless I preface it with a differentiating adjective, e.g. biological process.

While the scientific community would broadly agree that these are the leading paradigms in their respective areas, they would not agree on any one version of each theory. They are still evolving and in cases have parallel, contradictory lines of development. I will cite appropriate sources that are representative of these theories as needed.

Information is Fundamental

“Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like”
― Thomas S. Kuhn, The Structure of Scientific Revolutions

Physical scientists have become increasingly committed to physicalism over the past four centuries. Physicalism is intentionally a closed-minded philosophy: it says that only physical things exist, where physical includes matter and energy in spacetime. It seems, at first glance, to be obviously true given our modern perspective: there are no ghosts, and if there were, we should reasonably expect to see some physical evidence of them. Therefore, all that is left is physical. But this attitude is woefully blind; it completely misses the better part our existence, the world of ideas. Of course, physicalism has an answer for that — thought is physical. But are we really supposed to believe that concepts like three, red, hockey, pride, and concept are physical? They aren’t. But the physicalists are not deterred. They simply say that, sure, these things can exist in a free-floating, hypothetical sense, but that isn’t anything “real”; what “really” exists for any of these things in our minds is just a physical configuration of neurons and their associated neurochemistry. To which I would say, it is all very easy for you to declare that concepts and ideas we use every day only exist as physical configurations, but if you adopt this view you will never understand life, the brain, or the mind, or, for that matter, what understanding is. Studying by ignoring is not very illuminating.

Now, it is certainly quite true that the physicalist perspective has been amazingly successful for studying many physical things, including everything unrelated to life. However, once life enters the picture, philosophical quandaries arise around these three problems:

(a) the origin of life,
(b) the mind-body problem and
(c) the explanatory gap.

In 1859, Charles Darwin proposed an apparent solution to (a) the origin of life in On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. His answer was that life evolved naturally through small incremental changes made possible by competitive natural selection between individuals. The idea of evolution is now nearly universally endorsed by the scientific community because a vast and ever-growing body of evidence supports it while no convincing evidence refutes it. Exactly how small incremental changes can be selected was not understood in Darwin’s time, and even today’s models are superficial and miss much of the bigger picture. Two big unresolved problems in Darwin’s time were the inadequate upper limit of 100 million years for the age of the Earth and the great similarity of animals from different continents. It was nearly a century before the earth was found to be 4.5 billion years old (with life originating at least 4 billion years ago) and plate tectonics explained the separation of the continents. By the mid-20th century, evolutionary theory had developed into a paradigm known as the Modern Synthesis that standardized notions of how traits are inherited. This now classical view of vertical-descent speciation by natural selection from random mutations has been challenged by a massive increase in our knowledge of molecular biology. Some of that new knowledge will directly impact my explanation of the mind, but for now it is sufficient to recognize that life and the mind evolved over billions of years from incremental changes.

Science still draws a blank trying to solve (b) the mind-body problem. In 1637, René Descartes, after thinking about his own thoughts, concluded “that I, who was thinking them, had to be something; and observing this truth, I am thinking therefore I exist”1, which is popularly shortened to Cogito ergo sum or I think, therefore I am. Now, we still know that “three” is something that exists that is persistent and can be shared among us regardless of the myriad ways we might use our brain’s neurochemistry to hold it as an idea, so intuitively we know that Descartes was right. But officially, under the inflexible auspices of physicalism, three doesn’t exist at all. Descartes saw that ideas were a wholly different kind of thing than physical objects and that somehow the two “interacted” in the brain. The idea that two kinds of things exist at a fundamental level and that they can interact is called interactionist dualism. And I will demonstrate that interactionist dualism is the correct ontology of the natural world (an ontology is a philosophy itemizing what kinds of things exist), but not, as it turns out, the brand that Descartes devised. Descartes famously, but incorrectly, proposed that a special mental substance existed that interacted with the physical substance of the brain in the pineal gland. He presumed his mental substance occupied a realm of existence independent from our physical world which had some kind of extent in time and possibly its own kind of space, which made it similar to physical substance. We call his dualism substance dualism. We know now substance dualism is incorrect because the substance of our brains alone is sufficient to create thought.

Physicalism is an ontological monism that says only one kind of thing, physical things, exist. But what is existence? Something that exists can be discriminated on some basis or another as being distinct from other things that exist and is able to interact with them in various ways. Physical things certainly qualify, but I am claiming that concepts also qualify. They can certainly be discriminated and have their own logic of interactions. This doesn’t quite get us down to their fundamental nature, but bear with me I and I will get there soon. Physicalism sees the mind is an activity of the brain, and activities are physical events in spacetime, so it just another way of talking about the same thing. At a low level, the mind/brain consists of neurons connected in some kind of web. Physicalism endorses the idea that one can model higher levels as convenient, aggregated ways of describing lower levels with fewer words. In principle, though, higher levels can always be “reduced” to lower levels incrementally by breaking them down in enough detail. So we may see cells and organs and thoughts as conveniences of higher-level perspectives which arise from purely physical forms. I am going to demonstrate that this is false, and that cells, organs, and thoughts do not fully reduce to physical existence. The physicalists are partly right. The mind is a computational process of the brain like digestion is a biological process of the gastrointestinal system. Just as computers bundle data into variables, thinking bundles data into thoughts and concepts which may be stored as memories in neurons. Computers are clearly physical machines, so physicalists conclude that brains are also just physical machines with a “mind” process that is set up to “experience” things. This view misses the forest for the trees because neither computers nor brains are just physical machines… something more is going on that physical laws alone don’t explain.

This brings us to the third problem, (c) the explanatory gap. The explanatory gap is “the difficulty that physicalist theories have in explaining how physical properties give rise to the way things feel when they are experienced.” In the prototypical example, Joseph Levine said, “Pain is the firing of C fibers”, which provides the neurological basis but doesn’t explain the feeling of pain. Of course, we know, independently of how it feels, that the function of pain is to inform the brain that something is happening that threatens the body’s physical integrity. That the brain should have feedback loops that can assist with the maintenance of health sounds analogous to physical feedback mechanisms, so a physical explanation seems sufficient to explain pain. But why things feel the way they do, or why we should have any subjective experience at all, does not seem to follow from physical laws. Bridging this gap is called the hard problem of consciousness because no physical solution seems possible. However, once we recognize that certain nonphysical things exist as well, this problem will go away.

We can resolve these three philosophical quandaries by correcting the underlying mistake of physicalism. That mistake is in assuming that information is physical, or, alternatively, that it doesn’t exist. But information is not physical and it does exist. Ideas are information, but information is much more than just ideas. The ontology of science needs to be reframed to encompass information. I am going to iterate on this idea from several directions to build my case, but let’s start with how life, and later minds, expanded the playing field of ordinary physics. Living systems are a privileged class of physical objects that act as information processing systems, or information processors (IPs) for short. They manage heritable information using DNA as their information repository. While the information resides in the DNA, its meaning is only revealed when it is translated into biological functions via biochemical processes. Most famously, the genetic code of DNA uses four nucleotide letters to spell 64 3-letter words that map to twenty amino acids (plus start and stop). A string of amino acids forms a protein, and proteins do most of the heavy lifting of cell maintenance. But only two percent of the DNA in humans codes for proteins. Much of the rest regulates when proteins get translated, which most critically controls cell differentiation and specialization in multicellular organisms. Though we can only speculate for now about additional functions of non-coding DNA, these functions are likely to transform our understanding of genetics completely, so I will discuss some hypotheses about them later on. But, back to the storage of information, just knowing the sequence of DNA and proteins doesn’t reveal their functions; only careful study of their effects can do that. We have sequenced the human genome, but we still have a rather superficial understanding of what it does.

Animals take information a step further by processing and storing real-time information using neurochemistry in brains2. While non-animals are reactive to their environments, their reactions are either at the cellular level or are slow and rather inflexible. Animals need to vary their behavior and prioritize their actions across a much wider range of activities than plants, so an ability to assess their current circumstances and apply generalized logic to select the best course of action is critical to their survival and success. And animals go further still by using agent-centric processes within brains called minds that represent the external world to them through sensory information that is felt or experienced, They then use experienced cognition processes with cognitive information (i.e. ideas) to make decisions. This seems to fit uncontroversially into current thinking until we start asking what information really is. So long as we take it for granted as something we all understand, we can’t see it for what it is. This oversight allows physicalists to either ignore its nonphysical aspects or say that timeless features of information don’t exist in any meaningful sense. But it does exist and in a very meaningful way. In fact, meaning itself only exists nonphysically as information. So I propose that information has an entirely different kind of existence, which I generically call functional existence for reasons I will soon make apparent.

Colloquially, information is facts (as opposed to opinions) that provide reliable details about things. More formally, information is “something that exists that provides the answer to a question of some kind or resolves uncertainty.” But provides answers to who? I’m going to use the word “knowledge” to describe information within our minds’ awareness. So knowledge provides answers to us in a form we can think about. I’m going to refer to knowledge and information separately going forward so we can keep things happening in our minds distinct from things happening outside them, but keep in mind that knowledge is a kind of information. Let’s consider what questions we can answer with physical information. Suppose we have one gram of pure water at 40 degrees Fahrenheit at sea level at 41°20’N 70°0’W (which is in Nantucket Harbor). This information tells us everything knowable by our science about that gram of matter, and so could be used to answer any question or resolve any uncertainty we might have about it. Of course, the universe doesn’t represent that gram of water using the above sentence, it uses molecules, of which there are sextillions in that gram. Although it seems like this would produce astronomically complex behavior, the prevailing paradigm of physics claims a uniformity of nature in which all water molecules behave the same. Chemistry and materials science then provide many macroscopic properties that work with great uniformity as well. Materials science reduces to chemistry, and chemistry to physics, so these properties are just conveniences and not fundamentally different. So, in principle, physical laws can be used to predict the behavior of any physical thing. Quantity, temperature, pressure, and location provide the local details and the laws of the universe take care of the rest. Our knowledge of those laws is incomplete but can make quite accurate predictions in almost any situation where we have enough physical information. However, we don’t know enough to predict what living things will do.

Living things are complicated because they have microstructure down to the molecular level. Cells are pretty small but still big enough to contain trillions of molecules, all potentially doing different things, which is a lot of complexity. We can’t collect all that information and project what each molecule will do using either physics or chemistry alone. But we have found many important biochemical reactions that illuminate considerably how living things collect and use energy and matter. And physicalism maintains that given a complete enough picture of such reactions we can completely understand how life works. But this is not true at all. Perfect knowledge of the biochemistry involved would still leave us unable to predict almost anything about how a living thing will behave. Physical laws alone provide essentially no insight at all. Our understanding of biological systems depends mostly on theories of macroscopic properties that don’t reduce to physical laws. We theorize that living things are somehow internally organized so as to maintain cell walls and multicellular structure, and further to behave so as to bring in energy and materials for growth and to eliminate them as waste. From a high level, we know quite a bit about how this internal structure works, and we know it was created across billions of years of incremental changes, but we don’t have a detailed idea of how those changes came about. How did we arrive at the organizing principles of biology that suggest organisms “persist” and “replicate” using cells? “Persists” suggests a continuity analogous to rocks, and “replicate” is perhaps analogous to growth and division, like growth of a crystal and division when part of it shears off. No, it’s not that; nobody has ever seriously thought these analogies were helpful. The principles of biology came from the assumption that biological structures have functions. That life is about the survival and replication of bodies that protect their integrity has been abundantly obvious since ancient times, and our grasp of biological things also has an instinctive basis. We are living and we relate to living things. But this assumption of function is not supported by physical theory at all. Nothing about our physical theories foresaw life as a likely consequence, and we would never have even guessed machinery of this complexity could even be possible if we didn’t have it staring us in the face every day. But we see intuitively see function everywhere in life, and we freely cite it in our scientific explanations. So we can, for example, see bodies as complex machines that change over time, bringing in new material for growth and expelling old waste material, because that is their function. But how do we justify that on a physical basis if life is too complex to foresee as a physical consequence?

The justification comes from Darwin. Darwin proposed that biological function was a consequence of inductive trial and error. Ways of doing things, or functions, could become favored over time by natural selection. This at first seems counterintuitive to us, because our personal experience in life suggests that function only arises from intent, where intent is an effect or goal reached by a deductive cause. Goals reached by intent are purposes, and strategies that accomplish purposes are designs. I call actions with planning or intent maneuvers. Intent, cause, effect, goal, deduction, purpose, strategy, design, planning and maneuver are all roughly synonymous mental constructs whose substructure I will be investigating in much more detail further on, but none of them have any place or corollary in evolutionary processes. My point, for now, is only that Darwin says that function can exist without intent, and thus biology can freely cite function as a foundational principle. To the extent they ever use intent-based terminology, it is understood that this is not meant to be taken literally and inductive function is not really the same as deductive purpose. But wait, something small but important was overlooked while we were being careful to keep trial and error distinct from cause and effect. We assumed function is a physical consequence of a large number of purely physical events, and it isn’t. Function is something altogether different from the physical events that can help create it. Yes, it is true that we can now see the physical events down below, and it is tempting to reduce function to those physical events, but it doesn’t and wishful thinking won’t help.

The universe and its internal laws manage physical information in universally consistent ways. But living things collect a different kind of information at a higher level and preserve it and pass it down to succeeding generations. This biological information is derived from the feedback loops of natural selection and encoded in DNA. All information thus breaks down into physical information and derived information. For the purposes of this book, I will not need to refer again to physical information, which the universe manages quite well, so unless I say physical information explicitly all my references to information will henceforth refer to derived information. Beyond biological information, brains derive information in real-time and humans derive specific kinds of information according to many schemes. In fact, we have become accustomed to think of information in this digital age as just being the part that is encoded, but that encoded part takes for granted the existence of an IP that can use it. For organisms overall, the IP is the body and its metabolism with information stored in DNA, for the top-level control of organisms it is the brain and its processes with information stored neurochemically, and for computers it is their hardware and software and digital information. But information is not just the incremental capacity to answer a specific question, it is the whole capacity to process and act on information. The part that is encoded is usually the more interesting part because we can usually take the rest of the IP for granted, but if our goal is to understand how the IP works (in this case, the mind), then we can’t focus on just the incremental part.

Consider the following incremental piece of biological information. Bees can see ultraviolet light and we can’t. This fact builds on prevailing biological paradigms, e.g. that bees and people see light with eyes. Taken as an incremental amount of information, we knew before that animals could see, and now we know that some animals see ultraviolet as well. This fact extends what we knew, which seems simple enough. If we are a child who only knows that animals can see and bees are small flying animals that like flowers, we can now understand that bees see things in flowers that we can’t. This implicitly endorses a paradigm of animals with a functional imperative to stay alive with the help of sensory information. If we are a biologist working on bee vision, this same paradigm is sufficient for our purposes. We don’t need to know where bees came from or why they stay alive; we can just focus on the incremental information of our specialty. But if our goal is to explain bees or minds in general, we have to think about the underlying IP.

Our biological paradigm needs to define what animals and sight are, but the three philosophical quandaries of life cited above stand in the way of a detailed answer. Physicalists would say that lifeforms are just like clocks but more intricate. That is true; they are intricate machines, but, like clocks, an explanation of all their pieces, interconnections, and enabling physical forces says nothing about why they have the form they do. Living things, unlike glaciers, are shaped by feedback processes that gradually make them a better fit for what they are doing. Everything that happened to them back to their earliest ancestors about four billion years ago has contributed. It wasn’t just a series of events, but feedback events that created biological information which, I will show, can only be explained using laws of function, although information processors do this by leveraging physical laws. What is it, exactly, about the feedback processes that created life that creates this new kind of entity called information and what is the substance of the information? The answer to both questions is actually the same definition given for information above, the reduction of uncertainty, which can also be phrased as an ability to predict the future with better odds than random chance. One could only know the future in advance with certainty given perfect knowledge of the present and a perfectly deterministic universe. But we can never get perfect knowledge because we can’t measure everything and because quantum uncertainty limits what we can know about how it will behave. But biological information is not about perfect predictions, only approximate ones. A prediction that is right more than it is wrong can arise in a physical system if it can use feedback from a set of situations to make generalized guesses about future situations that can be deemed similar. That similarity, measured any way you like, carries predictive information by exploiting the uniformity of nature, which makes situations that are sufficiently similar usually behave similarly. It’s not magic, but it seems like magic relative to conventional laws of physics, which have no framework for measuring similarity. Such a framework can only arise from generalizations made from feedback, and those generalizations are the biological information. A physical system with this capacity is exceptionally nontrivial — living systems took billions of years to evolve into impressive IPs that now centrally manage their heritable information using DNA. Animals then spent hundreds of millions of years evolving minds that manage real-time information using neurochemistry. Finally, we humans have built IPs that manage small sets of information (relative to living things and minds) using either standardized practices (e.g. by institutions) or computers.

A functional entity has the capacity to do something useful, where useful means to be able to engage in the sorts of actions that will result in outcomes substantially similar to outcomes seen previously. A function can apply information to predict the future which can then be used to change the future, but the function itself is the capacity to do these things, regardless of whether they are done. Physical matter and energy are comprised of a vast number of small pieces whose behavior is relatively well-understood using physical laws. Functional entities are comprised of capacities. Both are natural phenomena. Until information processing came along through life, there was no function on earth. But now life has introduced an uncountable number of functions in the form of biological traits. As Eugene Koonin of the National Center for Biotechnology Information puts it, “The biologically relevant concept of information has to do with ‘meaning’, i.e. encoding various biological functions with various degrees of evolutionary conservation.”3 The mechanism behind each trait is itself purely physical, but the fact that the trait “works” across a certain “range” of circumstances is because “works” and “range” generalize an abstract capacity, which one could call the reason for the trait or its function. The traits don’t know why they work, because knowledge is a function of minds, but utility across a generalized range of situations causes them to form. That why is not a physical property of the DNA, it is a functional property.

The moment functional existence starts to arise independent of physical existence is the moment that feedback from prior situations starts to be applied to new situations, which necessarily employs generalized, statistical approaches. Being able to use this experience, captured somehow as information, constitutes a functional performance rather than merely a physical action. The act of generalizing creates abstractions, which can loosely be thought of as categories, that are about something else that is not directly connected to them. This act of indirection is the point of detachment where functional existence arises and (in a sense) leaves physical existence behind. This generalized, indirect “link” is actually a capacity to make a connection or correlation in the future based on similarity. Note that this kind of indirect information can arise from inductive trial-and-error or deductive cause-and-effect. I am not suggesting that all information is representational; that is too strong a position. Information is necessarily “about” something else, but only in the sense that its application does go from a generality to something specific. The defining characteristic of information is only that it is useful, where useful means that it can help lead to performances that have a better than random chance of producing expected results.

To clarify further, we can now see that all functional entities are necessarily generalities and all physical entities are necessarily specifics. We can break a generality down into increasingly specific subcategories, but they still always be generalities because they are still categories that could potentially refer to multiple physical things. Even proper nouns are still generalities… a given quark detected in a particle accelerator, or my left foot, or Paris refer to specific physical things, but are generalized across time and subject to general properties of how we model them. But those things themselves do have specific parts.

Biological change results from small physical changes to DNA which impact its functions, but natural selection is focused entirely on the functions and not the physical changes, so that is what it captures. You could say that information and function piggyback off physical mechanisms. Sometimes the physical nature closely aligns with the functional nature and we can speak confidently about two together without significant fear of inaccuracy or confusion. Hearts pump blood because bodies need circulation. The form and function line up very closely in an obvious way. We can pretty confidently expect that all animals with hearts will continue to have them in future designs to fulfill their need for circulation. On the other hand, the genetic trigger that results in an organism being male or female has shifted in some gene lines to a completely different physical mechanism using entirely different chromosomes (volumes of DNA in each cell), but males and females still exist as before to fulfill the necessary function of sex. For example, two species of mole voles, small rodents of Asia, have no Y chromosome, so males have XX chromosomes like females. We don’t know what trigger creates male mole voles, but this change is not entirely unexpected because Y chromosomes shrink and will eventually fail.456 A physical mechanism is necessary, and so only possible physical mechanisms can be employed, but the selection between physical mechanisms has nothing to do with their physical merits and only to do with their functional contribution. As we move into the area of mental functions, the link between physical mechanisms and mental functions becomes increasingly abstracted, effectively making the prediction of animal behavior based on physical knowledge alone impossible.

One can’t define or count generalities with perfect precision, but that doesn’t mean they don’t exist and can’t be delineated. It just means that our study of traits and functions in general is itself a generalized exercise, subject to its own constraints of similarity of matching. That said, we can achieve a very high level of confidence that we have correctly described certain traits, even though our information is still incomplete. For example, we know that bee eyes have cone cells with a photopigment that is most sensitive to 344nm light in the ultraviolet range and the human eyes do not. We know bees need to be good at discriminating flowers, and so we can be fairly certain that the function of seeing ultraviolet light is a rather discrete function that is preserved in bees for this reason.78 Again, physical mechanisms are limiting factors, but not nearly so limiting as the constraint by natural selection to useful functions.

It is important to note that evolution creates capacities, which are functional entities whose defining characteristics relate to their utility, but it does not record the reasons it creates the capacities. As I just noted above, we can sometimes infer reasons for these capacities that are highly predictive and which we therefore may be inclined to think of as “right”. But we have to keep in mind that our attempts to explain evolutionary systems using deductive cause-and-effect is not the way they were formed. They were formed with inductive trial-and-error. Feedback from many trials and errors and successes will produce a solution that has been subtly influenced by a much longer list of factors than any deductive model could ever enumerate. Our feeling of understanding depends on deductive models that are supported by inductive information, but biological information below the upper levels of minds are all inductive, not deductive, and so can’t really be fully understood. Understanding is inherently an approximate venture. It characterizes things into buckets which may usually hold but won’t always hold. Or, rather, deductive models can be internally perfect, but once we imply them to circumstances not governed strictly by their perfect logic, we have to expect that the match will only be approximate.

Let’s review where we are. An information processor or IP is physical construction like a living thing or a computer that manages information. Information consists of functional entities that are nonphysical generalizations that are abstracted away from any physical referent by virtue of not necessarily referring to anything specific. Something exists if it can be distinctly discriminated from other things that exist and can interact with them in various ways. Physical things exist specifically and functional things exist generally, and both of these kinds of existence can be distinguished and can form interactions. Consequently, we can conclude that interactionist dualism is true after all. The idea that something’s existence can be defined in terms of the value it produces is called functionalism. For this reason, I call my brand of interactionist dualism form and function dualism, in which physical substance is “form” and information is “function”. As an interactionist, I hold that form and function somehow interact in the mind. To clarify that interaction, I am going to further subdivide function into five groups, the first two of which apply to form as well:

Noumenon – the thing-in-itself. Daniel Dennett calls a functional entity’s noumenon its “free-floating rationale”9

Phenomenon – that which can be observed about a noumenon

Perception – first-order information from the reception of phenomena by an information processor (IP) and their conversion into information

Comprehension – second-order information from deductive models with the inductive information supporting them

Reflection – third-order information from steering information processing in potentially any direction in an exploratory way, including back on itself

I’m going to discuss these briefly in turn before returning to the three philosophical quandaries of life.

We believe that physical matter and energy exist, but we can’t know for sure because they are in one place and we are in another. The term for the existence of a physical object completely independent of us is noumenon, or thing-in-itself (what Kant called das Ding an sich). The only way we can ever come to know anything about noumena is through phenomena, which are emanations from or interactions with the noumenon. Features of noumena that exhibit no phenomena are completely unknowable outside the things themselves. Example phenomena include light or sound bouncing off an object, but can also include matter and energy interactions like touch, smell, and temperature. Since all our knowledge of the world must be contained in our minds, it is all indirect knowledge based on phenomena. But because of the uniformity of nature, we can be quite confident of many things about the noumena to which they refer, even though we can never be certain of their real nature. Now, for my purposes here, if a tree falls in the forest and there is nobody to hear it, there was a phenomenon but no perception. Perception is the receipt of a phenomenon by a sensor and sufficient concomitant information processing to create information about it. Perception creates first-order information, which is based on inductive trial-and-error. Inductive reasoning or “bottom-up logic” generalizes conclusions from multiple experiences based on similarities. Although the term perception is usually restricted to a mental activity, I have generalized the definition here to say that all inherited genetic information is created through perception.

Comprehension is qualitatively more elaborate than perception because it invokes deductive models based on causes and effects. A model is a system of information that can be followed or imitated based on similarities. Models are therefore necessarily representational, but are not necessarily deductive as the mind creates models inductively as well. Deductive reasoning or “top-down logic” involves use of a model with clear logical rules that link premises to conclusions or causes to effects. Those premises and conclusions in the model are then frequently mapped to sufficiently similar circumstances in the physical world using inductive mapping techniques. Usually, but not always, a substantial part of our understanding of a deductive model depends on our inductive sense of the meaning of the premises and conclusions. This inductive sense is based on our senses, feelings, common sense and intuitions about things, which do not themselves give us a comprehension of them. While can’t say we comprehend these inductive senses because we don’t know why we have them, but we will sometimes say we understand them, which is a lower bar than comprehension that can include knowing how to use information without knowing why. The premises and conclusions of deductive models are called concepts, which are the self-contained building blocks of deductive logic. I will go into more detail on concepts later on.

Reflection is a term I have coined for which I know no better common name, though free association comes close. Reflection is the ability to think about what we are thinking about so as to steer our thought in promising directions. It builds on comprehension because in order to manipulate thoughts we have to compartmentalize them into concepts first, which requires deductive modeling. But this is not to say all thinking is deductive; we can think about our senses, feelings, common sense and intuitions as well and form concepts about them which we can reflect on. Reflection is arguably the cognitive trait that most separates humans from other animals. We are both capable and inclined to let our thoughts move in directions that we feel are likely to lead to useful solutions. Just why humans are more inclined is a subject I will explore later.

Both physical things and functional things that leverage physical IPs can produce physical effects in the physical world. I call purely physical effects “actions” and physical effects caused by functional things “performances” to indicate their functional origin. Functional things can also produce functional effects without any physical effects. They can do this on a hypothetical basis, in which case nothing physical happens at all, we just recognize that a given functional system has the capacity to produce certain effects in a “free-floating” way. Or, they can do it using simulations, which are physical processes that project functional implications without acting on them further.

Physical things have noumena and phenomena, but never have perception, comprehension or reflection as they contain no derived information. Functional things, of course, include perception, comprehension, and reflection, but can also be said to have noumena and phenomena. Daniel Dennett calls the explanations behind evolutionary designs “free-floating rationales”, because they don’t exist in time or space or require anyone to understand them or even know they are there. Like the number of corners on a cube, the underlying value of an evolutionary design has a claim to existence whether anyone knows it or not.10 This is a good way of putting it, though we have to remember that evolutionary rationales are based on inductive trial-and-error while the corners of a cube are based on deductive cause-and-effect, because cubes and their corners are concepts in certain mathematical deductive models. Unlike physical noumena, which are apart from us and hence not directly knowable, deductive functional noumena are defined by us and hence fully knowable. But inductive functional noumena that were created from the subtle effects of many events are also apart from us and so our knowledge of them must be gathered from their phenomena. Functional noumena have phenomena which we observe by considering the effects of performances produced by them. In other words, the inductive functional noumena themselves are capacities which we can’t know directly, but when they perform we can evaluate their effects to gain insight into their underlying function. These functions define the functional noumena, because information is defined in terms of the value it produces. We learn evolutionary functions by studying living things looking for patterns and then use reverse-engineering to propose cause and effect explanations that approximate the functional noumena that are really based on trial and error. Although we can’t truly know these inductive noumena directly, our deductive explanations of them can approximate them to a high degree of effectiveness. In a similar way, the laws of physics and chemistry are deductive explanations of inductive systems that approximate them very effectively but don’t really reveal the underlying fabric of matter or energy. In both cases, the uniformity of nature is our best friend in getting deductive models to actually align with inductive observations.

We generally don’t need to contemplate phenomena of deductive noumena because we know the rules the things-themselves follow. However, sometimes rules get complicated and it can be hard to work out all their implications, and for these cases it can be helpful to run simulations and evaluate the generated phenomena to get a better idea how the model behaves. This creates a composite deductive/inductive model because the information we glean from the phenomena is inductive, at least until we can figure out a way to prove it.

Let’s review our three quandaries in the light of form and function dualism. First, the origin of life. Phenomena naturally occur and their effects comprise the canon of the laws of physics, chemistry, materials science and all physical sciences. These sciences work out rules that describe the interactions of matter and energy. They essentially define matter and energy in terms of their interactions without really concerning themselves with their noumenal nature. A new kind of existence, functional in nature, arises with perception, which finds patterns in nature that can be exploited in “useful’ ways. The use that concerns them is survival, or the propagation of function for its own sake, but that use is sufficient to drive functional change. But perception forms its own rules transcendent to physical laws because it uses patterns to learn new patterns. It exploits that fact that natural systems configured to do so can be shaped by functional objectives and not just physical destiny.

Next, let’s consider the mind-body problem. The essence of this problem is the perception that what is happening in the mind is of an entirely different quality than the physical events of the external world. Form and function dualism tells us that they do have an entirely different quality, because the mind is entirely concerned with functional entities and the external world is largely concerned with physical entities, although a particularly relevant part is external functional entities like other people, living things, and artifacts. This division is not reducible at all, as physicalists would have us believe, because function is concerned with and defined by what is possible and physical things have no concerns. Concern and purpose only exist in a hypothetical sense, but that sense is a legitimate form of existence because our minds have the physical means to manipulate these abstractions and apply them to the world. One could fairly call our functional essence the “soul”, not as a supernatural entity but as a natural entity comprised of a complex of functional capacities implemented using the physical machinery of the brain.

Finally, let’s look at the explanatory gap. I said this gap would evaporate with an expanded ontology. By recognizing functional existence for what it is, we can see that it opens up a vastly richer space that physical existence because it can relate anything to anything in any number of ways. The world of imagination is unbounded, while the physical world is closely ruled by seemingly very rigid laws. The creation of IPs that can generalize first inductively and then deductively gave them the capacity to access this unbounded world. Physical circumstances are always finite, but through finite access one can gain unlimited capacities because capacities are not yet constrained to specific circumstances. So to close the explanatory gap and explain what it is like to feel something, we should first recognize that the target range of experience and understanding was never physical, it was functional. Next, it stand to reason that things will “feel” like what they can do, their capacity. This makes no sense from a physical perspective, but it is entirely what we should expect from function at things, which are defined by what they can do. But what would that look like in practice? Keeping in mind that I haven’t yet broached the question of why brains have minds that experience things consciously, if we accept that they do and that minds help brains with their task of controlling the body, and brains develop and use information relevant to that control, then it follows that if minds had a subjective perspective (which they do), it would center around developing control information. If the computational theory of mind is true that the mind is a process running in the brain, then its whole reality is information processing in our heads and is not our bodies or the external world, although it seems to us that our minds have direct access to those things. This means that feelings are just information, and since information is just the capacity to do things, we can conclude that feelings are a translation of what that information empowers us to do.

Let’s take a look at our world of experience to see how it feels like what it can do. Senses seem at first indifferent to what we might do with the information. Colors and sounds are pretty or interesting but don’t seem to feel like what they can do. But that isn’t really true. Yes, most of the colors and sounds we see and hear don’t carry information that we need to act on. We still automatically categorize them into thousands or millions of varieties. Those colors and sounds can trigger any number of instinctive or learned associations and associated behaviors, based on the raw sensory impressions themselves or on objects or experiences recognized from them. Blues and greens are famously considered to feel more safe and calming than reds and yellows, presumably because both instinct and experience are likely to suggest that blues and greens are harmless background colors but reds and yellows represent dangerous or helpful items. Sudden loud noises feel scary because both instinct and experience suggest they are dangerous, while continuous noises are more likely to be harmless features of the background. Extrapolating further, all our emotions inspire us to act in ways that use their implied information in helpful ways. Arguably, the brain could come up with other ways to lead it to act in helpful ways without having a subprocess of mind “feel” functionality in a visceral way, but what if this approach just worked a lot better than any other alternative. I will argue later that this is exactly what is happening. For now though, just to show that the explanatory gap can be closed, we only have to recognize that minds have subjective experiences because it is a way of processing the functions relevant to them in an effective way. Although the feelings seem somewhat amazing to us, this is just an artifact of how the brain runs the specific process we call consciousness to implement high-level functions in the brain. States of mind can thus be seen to be analogous to functions of the bodily organs: the organs perform distinct physical functions in a physical environment, while the mind performs distinct functional functions in a functional environment. That functions can be segregated this way is therefore not surprising. Later I will provide a more detailed argument as to why subjectivity is necessary and why feelings feel precisely as they do.

To summarize this initial defense of dualism, I propose that form and function, also called physical and functional existence, encompass the totality of possible existence. We have evidence of physical things in our natural universe. We could potentially someday acquire evidence of other kinds of physical things from other universes, and they would still be physical, just not in the way we know. Functional existence needs no time or space, but for physical creatures to benefit from it there must be a way for functional existence to manifest in a physical universe. Fortunately, the feedback loops necessary for that to happen are physically possible and have arisen through evolution, and have then gone further to develop minds which can not only perceive, but can also comprehend and reflect. Note that this naturalistic view is entirely scientific, provided one expands the ontology of science to include functional things (which I will discuss more later), and yet it is entirely consistent with both common sense and conventional wisdom, which hold that “life force” is something fundamentally lacking in inanimate matter. That “life force” is also evident in artifacts because what we are really sensing is the presence of function through derived information. It isn’t magic, but some of its noumenal mystery is intrinsically beyond complete understanding. But our understanding of life and the mind through closer and closer approximation from deductive cause and effect models will continue to grow until it rivals our understanding of things not shaped by life.

Hey, Science, We’re Over Here

Scientists don’t know quite what to make of the mind. They are torn between two extremes, the physical view that the brain is a machine and the mind is a process running in it, and the ideal view that the mind is non-physical or trans-physical, despite operating in a physical brain. The first camp, called eliminative materialism (or just eliminativism, materialism or reductionism), holds that only the physical exists, as supported by the hard scientific theories and evidence of physics, chemistry, and biology. At the far end of the latter camp are the solipsists, who hold that only one’s own mind exists, and a larger group of idealists, who hold that one or more minds exist but the physical world does not. Most members of the second camp, however, acknowledge physical existence but think something about our mental life can never be reduced to purely physical terms, though they can’t quite put their finger on what it is. Social science and our own intuition both assume that mental existence is something beyond physical existence. A materialist will hold either that (a) there are no mental states, just brain states (eliminativism), or that (b) there are mental states, but they can be viewed as (reduced to) brain states (reductionism). An idealist will hold that mental states can’t be reduced, which means they have some kind of existence beyond the physical, and that the physical might exist but if so it can never be conclusively proven.

I propose to resolve all these issues by reintroducing dualism, the idea of a separate kind of existence for mind and matter, but I will do so in a way that is compatible with materialist science. While I agree that there is a physical basis for everything physical, I don’t agree that all physical things are only physical. Some physical things (namely living things and artifacts they create) are also functional, which is a distinct kind of existence. While one can explain these things in physical terms, physical explanations can’t address function, which is a meaningless concept from a physical perspective. I will argue that functional existence transcends physical existence and is hence independent of it, even though our awareness of functional existence and use of it are mediated physically. This is because function is indirect; while it can be applied to physical circumstances, functional existence is essentially the realm of possibility, not actuality. The eliminativist idea that everything that exists is physical, aka physical monism, is only true so long as one is referring only to physical things that are not functional. But physical systems can become functional by managing information, which is patterns in data that can be used to predict what will happen with better than even odds. Once physical things start to manage information, physical monism breaks down because information isn’t physical, though it can be stored physically. Function is an additional kind of existence that characterizes a system’s capabilities rather than its substance. Functional systems can be physical or imaginary, but if they exist physically then their functional reach has physical limitations and their physical reach depends on their functional strength. In nonfunctional physical systems, events unfold in direct accordance with physical laws, but functional physical systems predict and influence events using information created using feedback. A capability can be thought of as the “power” to do something, which is distinct from the underlying physical mechanisms that make it possible. Living things principally exist functionally and only secondarily physically because natural selection selects functions, not forms. Consequently, any attempt to understand living things must focus first on function and then on form. Brains are organs responsible for managing dynamical information, and the mind is that functional capacity itself. We are not always precise in how we use the words brain and mind, but I will use brain only to refer to physical aspects of the organ, while mind refers to functional aspects independent of our knowledge of the brain. Understanding the brain necessarily depends on the physical study of it, while understanding the mind, I contend, depends on studying its functions. The two work together, and so the study of each can help inform the other, but at all times function and form are existentially independent. How the brain manages function (i.e. as the mind) is so abstracted from the physical mechanisms that make it possible that we only understand in broad terms how the joint venture of form and function works. It is my job here to establish those broad terms better and to clarify them as much as possible.

Reductionists reject downward causation1 as a nonsensical emergent power of complex systems. The idea is that some mysterious, nonphysical power working at a “higher” level causes changes at a “lower” level. For example, downward causation might be taken to imply that a snowflake’s overall hexagonal shape causes individual water molecules to attach to certain places to maintain the symmetry of the whole crystal. But this is patently untrue; only the local conditions each molecule encounters affects its placement. Each new water molecule will most likely attach to spots on the crystal most favored by current conditions encountered by that snowflake, which constantly change as the snowflake forms. But those favored spots at any given instant during its formation are hexagonally symmetric, making symmetrical growth most likely. The symmetry only reflects the snowflake’s history, not an organizing force. But just because downward causation doesn’t exist in purely physical systems, that doesn’t mean it doesn’t exist in functional systems like living things. Any system capable of leveraging feedback can make adjustments to reach an objective, hence “causing” it, if it also has an evaluative mechanism to prefer one objective over another. Mechanisms that record such objectives and have preferences about reaching them are called information management systems, and living things use several varieties of these systems to use feedback to bring about downward causation. It is a misnomer to call this capacity of life an “emergent” property because it doesn’t appear from nothing; it is just that certain physical systems can manage information and apply feedback. So we can see that the “higher” levels of organization are these information management systems and the “lower” levels are the things under management. Living things use at least two and arguably four or more information management systems at different levels (more on these levels later). Reductionists hold that causation is entirely a function of subatomic forces, and that “causes” at the atomic, molecular, or substance level are reducible to subatomic forces. They further conclude that, since life is natural with nothing added (e.g. no mystical “spirit”), the mind is an epiphenomenon or an illusion, and in either case, is certainly not causing anything to happen but is merely observing. Although this strongly contradicts our experience, reductionists will just say that our perception of time and logic is biased, so we should ignore our feelings and just accept that our existence as agents in the world is only a convenient way of describing matters really managed at the subatomic level.

But the reductionists underestimated the potential side-effects of the uniformity of nature. The fact that all subatomic particles of a certain type behave the same way all the time, similarly causing uniform behavior among atoms, molecules and substances, has implications about the kind of reality that will emerge. Without uniformity, information would be impossible and function could not be achieved because they work by collecting and applying feedback from past events to predict future events. With uniformity, feedback loops can discover and exploit patterns that repeat themselves. Given enough time these loops can create arbitrarily complex systems that are potentially capable of doing anything physically possible in that universe. You might think of them as trial and error machines: they start out trying things at random, but will launch an arms race that produces ever more systematic methods to deliver ever more relative functionality, until they are ultimately capable of doing potentially everything physically possible in that universe. So, ironically, information doesn’t emerge from complexity; it emerges from uniformity. Uniformity is akin to a superpower or supernatural force that, if present in a universe, enables the extra dimension of functional existence to manifest if feedback loops can flourish.

Bob Doyle (the Information Philosopher) explains it like this:


Some biologists (e.g., Ernst Mayr) have argued that biology is not reducible to physics and chemistry, although it is completely consistent with the laws of physics. Even the apparent violation of the second law of thermodynamics has been explained because living beings are open systems exchanging matter, energy, and especially information with their environment. In particular, biological systems have a history that physical systems do not, they store knowledge that allows them to be cognitive systems, and they process information at a very fine (atomic/molecular) level.

Information is neither matter nor energy, but it needs matter for its embodiment and energy for its communication.

A living being is a form through which passes a flow of matter and energy (with low or “negative” entropy, the physical equivalent of information). Genetic information is used to build the information-rich matter into an overall information structure that contains a very large number of hierarchically organized information structures. Emergent higher levels exert downward causation on the contents of the lower levels.2

I wrote most of this book before I discovered Bob Doyle’s work, so I did not know that anyone else had proposed the full-fledged existence of function/information independent of physical matter. But I’m glad to see that someone else thinks along the same lines as me. Doyle’s mission is to expose the larger role of information in philosophy, while my mission is to explain the mind. While doing that, I came to see function as a primary force and information as the medium that makes function possible. They are different: function is directly connected to purpose while information is only indirectly connected. Function is the capacity to do something, and information may or may not make it possible. Minds are functional entities that employ information.

I am not proposing property dualism, the idea that everything is physical but has both physical properties and informational properties. No physical thing has an informational property. Rather, I agree with eliminative materialists that physical things are just physical so far as physical existence goes. But physical systems called information management systems can arise that can exploit feedback to arbitrary degrees by recording that feedback as information, and this information can be viewed from a different perspective as having a distinct kind of existence. It doesn’t exist if your perspective is physical, but it is useful to have more than one perspective about what constitutes existence if your goal is to explain things. Ironically, explanation itself is not physical but is a functional kind of thing, so eliminative materialists can only argue their case by ignoring the very medium they are using to do so. But functional existence is a bit harder to put your finger on because function and information are abstractions which can never be perfectly described. Perfect descriptions are impossible because they are ultimately constructed out of feedback loops, which can reveal likelihoods but not certainties. Tautological information can be considered to be perfect, but only because it is true by definition. Tautological information doesn’t actually become functional until it is applied in a useful way, and “use” implies that at least something about the system is not known by definition. So some closed formal systems, e.g. some math and computer languages, can be perfectly deductive and within them information and functions are entirely predictable. But in all other systems, including the physical universe, some induction is necessary to predict what will happen, which will only work if those systems exhibit some uniformity. Function is a generalization about patterns that feedback has revealed, and a generalization is a nonphysical perspective, which is to say a way of explaining relationships between things. While function is strictly nonphysical, information can be thought of as having a physical manifestation through data stored in a physical medium like a brain, computer, or book. That the information is actually functional and not physical can be demonstrated through a simple thought experiment. Any information, e.g. the integer seventeen, can be stored in different ways in a person’s mind or in a computer, and can also be imagined to exist in a hypothetical sense without any physical manifestation. What it means to be “seventeen” is independent of any physical medium used to store it and above and beyond that physical form; it is what I call its function. One can argue that the “true” meaning of seventeen differs depends on how we define numbers and operations on them, but these things are themselves nonphysical functional concepts and not dependent on how or whether we store them in our brains or elsewhere.

Though all functional things must take a physical form in a physical universe such as the one we are in, this doesn’t mean function “reduces” to the physical. The quest for reductionism is misguided and has been holding science back for entirely too long. We have to get past it before we can make meaningful progress in subjects where functional existence is paramount. To do that, let’s take a closer look at where and how function arises in a physical universe. The only place function has appeared is in living organisms, who achieved it through evolution, which applies feedback from current situations to improve the chances of survival in future situations. The biochemical mechanisms they employ matter more from a functional standpoint than a physical standpoint because they are only selected for what they can do, giving them a reason to exist, and not for how they do it. In the nonliving world, things don’t happen for a reason, they just happen. We can predict subatomic and atomic interactions using physics, and molecular interactions using chemistry. Linus Pauling’s 1931 paper “On the Nature of the Chemical Bond” showed that chemistry could in principle be reduced to physics34. Geology and earth science generalize physics and chemistry to a higher level but reduce fully to them. However, while physical laws work well to predict the behavior of simple physical systems, they are not enough to help us predict complex physical systems, where complexity refers to chaotic, complex, or functional factors or a combination of them. Chaos is when small changes in starting conditions have butterfly effects that eventually change the whole system. Complex factors are those whose intricate interactions exceed the predictive range of our available models, which necessarily simplify. We see both chaos and complexity in weather patterns, and yet we have devised models that are pretty helpful at predicting them. These models are based on physical laws but use heuristics to approximate how systems will behave over time. But the weather and all other nonliving systems don’t control their own behavior; they are reactive and not proactive. Living things introduce functional factors, aka capabilities. Organisms are complex adaptive systems (CAS) that exploit positive feedback to perpetuate changes through DNA. I can’t prove that complex adaptive systems are the only way functionality could arise in a physical universe, but I don’t see how a system could get there without leveraging cycles of positive and negative feedback. Over time, a CAS creates an abstract quantity called information, which is a pattern that has occurred before and so is likely to occur again. The system then exploits the information to alter its destiny. Information can never reveal the future, but it does help identify patterns that are more likely to happen than random chance, and everything better than random chance constitutes useful predictive power.

Functional systems, i.e. information management systems, must be physical in a physical universe. But because the mechanisms that control them are organized around what they can do (their capability) instead of how they do it (their physical mechanism), we must speak of their functional existence in addition to their physical existence. By leveraging feedback, these systems acquire a capacity to refer to something else, to be about something other than themselves that is not directly connected to them. This indirection is the point of detachment where functional existence arises and (in a sense) leaves physical existence behind. At this point, the specific link is broken and a general “link” is established. Such an indirect link refers only to the fact that the information can be applied appropriately, not that any link is stored in any way. Just how a functional system can use information about something else to influence it can be implemented in many ways physically, but understanding those ways is not relevant to understanding the information or the function it makes possible. At the point the information detaches it gains existential independence; it is about something without it particularly mattering how it accomplishes it. It has a physical basis, but that won’t help us explain its functional capabilities (though it does place some constraints on those functions). While every brain and computer has different processing powers (memory, speed, I/O channels, etc.), in principle they can manage (approximately) the same information in completely different ways because the measure of information is the function it makes possible, not how it is implemented. That said, in practice, physical strengths and limitations make each kind of brain and computer stronger or weaker at different tasks and so must be taken into consideration.

I call the new brand of dualism I am proposing form and function dualism. This stance says that while everything physical is strictly physical, everything functional is strictly functional. Physical configurations can act in functional ways via information management systems, and these systems can only be understood from a functional perspective (because understanding is functional itself). Consequently, both physical and functional things can be said to exist, never as different aspects of each other but as a completely independent kind of existence. Functional existence can be discussed on a theoretical basis independent of physical information management systems to implement them. So, in this sense, mathematics exists functionally whether we know it or not. More abstractly, even functional entities entirely dependent on physical implementations for their physical existence, like the functional aspect of people, could potentially be replicated to a sufficient level of functional detail on another physical platform, e.g. a computer, or it could be spoken of on a theoretical basis. In fact, when we speak of other people, we are implicitly referring to their functional existence and not their physical bodies (or, at least, their bodies are secondary). So, entirely aside from religious connotations, we generally recognize this immaterial aspect of existence in humans and other organisms as the “soul.”

Information management systems that do physically exist include:

  • biological organisms, which store information in genes using DNA,
  • minds, which store information in brains neurochemically,
  • civilizations, which store information in institutions (rule-based practices) and artifacts (e.g. books), and
  • software, which stores information in computer memory devices.

    Organisms, minds, civilizations, and software can be said to have functions, and it is meaningful and necessary to discuss such functions independently of the underlying physical systems they run on. Also note that minds heavily leverage the information managed by organisms, so one can’t deeply understand them without understanding organism function as well. Civilizations and software manage information using appropriately customized systems, but they are direct creations of minds and their ultimate purposes are only to serve the purposes of the mind. If we do learn to build artificial minds or, more abstractly, information management systems with their own purposes and reasons for attaining them independent of our own, then they could be considered an independent class beyond organisms and minds.

Joscha Bach says, “We are not organisms, we are the side-effects of organisms that need to do information processing to regulate interaction with the environment.” 5 This statement presumes we define organisms as strictly physical and “we” as strictly functional, which are the senses in which we usually use these words. But saying that we are the side-effects is a bit of a joke because it is really the other way around: the information processing (us) is primary and physical organisms are the side-effects. Bach points out that mind starts with pleasure and pain. This is the first inkling of the mind subprocess, a process within the brain, separate from all lower-level information processing, whose objective is to make top-level decisions. By summarizing low-level information into an abstract form, the behavior of the brain can be controlled more abstractly, specifically: “Pleasure tells you, do more of what you are currently doing; pain tells you, do less of what you are currently doing.” All pleasure and pain are connected to needs: social, physiological and cognitive. In higher brains like ours, consciousness is like an orchestra conductor who coordinates a variety of activities by attending to them, prioritizing them, and then integrating them into a coherent overall strategy (his “conductor theory of consciousness”). Bach identifies the dorsolateral prefrontal cortex as the brain region that is most likely acting as the conductor of consciousness, coordinating the features of consciousness together to make them goal oriented. This part of the brain can help us run simulations of possibilities through our memories with an eye to aligning them with desired objectives. Our memories are encoded in the same parts of the brain that originally experienced them, e.g. the visual cortex, motor cortex, or language centers, etc., and recalling them reactivates the neural areas that generated that initial encoding.6 Bach theorizes that the conductor is off when people sleepwalk. They can still go to the fridge or in some cases even make dinner or answer simple questions, but there is “nobody home”. Similarly, whenever we engage in any habitual behavior, our conscious conductor has ceded most of its control to other brain areas that specialize in that behavior. While the conductor can step in and micromanage, it usually won’t, and if it does step in where it has not been for a long time, it will often make things worse because it doesn’t remember how shoes are actually tied or how sentences are actually formed. Bach’s theory proposes that “You are not your brain, you are a story that your brain tells itself,” which is correct except for its humorous use of the word “itself” — the brain doesn’t have a self; you do. More accurately the sentiment should go, “You are your mind, but your mind is not your brain; the mind is a story the brain produces that you star in.”

I’m not going to go too deeply into the mechanisms of the brain, both because we only know superficial things about them and because my thesis is that function drives form, but I would like to talk for a moment about the default mode network (DMN). This set of interacting brain regions has been found to be highly active when people are not focused on the world around them, either because they are resting or because they are daydreaming, reflecting, or planning, but in any case not engaged in a task. It is analogous to a running car engine when the clutch is engaged and power isn’t going to the wheels. Probably more than any other animal, people maintain a large body of information associated with their sense of self, their sense of others (theory of mind), and planning, and so need to be able to comfortably balance using their mind for these activities versus using them for active tasks. We like to think we naturally maintain a balance between the two that is healthy for us, but we now know that our culture has prioritized task-oriented thinking over reflection. Excessive focus on tasks is stressful, and more engagement of the default mode network is the solution. This can be achieved through rest and relaxation, meditation and mindfulness exercises, and, most effectively of all, via psychotropic drugs like psilocybin and LSD. Even a single experience with psychedelic drugs can permanently improve the balance, potentially curing depression or improving one’s outlook, though more research needs to be done to establish good guidelines (and they also need to be decriminalized!)789

Socrates and Plato recognized that function stands qualitatively apart from the material world and explained it using teleology. Teleology is the idea that in addition to a form or material cause things also have a function or purpose, their final cause. They understood that while material causes were always present and hence necessary, they were not sufficient or final to explain why many things were they way they were. Purposes humans imposed on objects like forks were called extrinsic, while purposes inherent to objects, like an acorn’s purpose to become a tree, were called intrinsic. Aristotle listed four causes to explain change in the world, not just two: material, formal, efficient, and final. The formal cause attaches meaning to the shape something will have, essentially a generalization or classification of it from an informational perspective. While this was an intrinsic property to the Greeks, nowadays we recognize that classification is extrinsically assigned for our convenience. The efficient cause is what we usually mean by cause today, i.e. cause and effect. Physicalism sees the material, formal and efficient causes as the physical substance, how we classify it, and what changes it. However, physicalism rejects the final, teleological cause because it sees no mechanism. After all, objects don’t sink to lower points because it is their final cause, but simply because of gravity. While I agree that this is true of ordinary physical things, I hold that teleology is both intuitively true and actually true for functional systems, and that the mechanism that makes it possible is information management. Physicalists consider the matter closed — teleology has been disproven because there is no “force” pulling things toward their purpose. But if one can see that function as real, then one can see that it exists to pull things toward purposes. Teleology is so far out of favor that Wikipedia is hesitant to admit that the life sciences might require teleological explanation: “Some disciplines, in particular within evolutionary biology, continue to use language that appears teleological when they describe natural tendencies towards certain end conditions.[citation needed] While some argue that these arguments can be rephrased in non-teleological forms, others[who?] hold that teleological language cannot be expunged from descriptions in the life sciences.” It goes on, “Some biology courses have incorporated exercises requiring students to rephrase such sentences so that they do not read teleologically.” I would make an analogy to trying to efforts to convert homosexuals to heterosexuality. Although evolution is, as Richard Dawkins says, a blind watchmaker, the information management systems of life nevertheless have purpose. The purpose of lungs is to provide oxygen, of the heart to circulate blood, and of limbs to provide mobility. They can also have additional purposes. The body is not aware of these purposes, and our attempts to summarize them through theory and explanation can only be approximately right, but their approximate truth is undeniable. Lungs, hearts and limbs that do not fulfill these purposes will seriously jeopardize survival. Although there is no actual watchmaker and the function of the lungs results from many chance feedback events, animals need oxygen and lungs help provide it. This is their design purpose whether the design was intentional or not.

Although Aristotle had put science on a firm footing by recognizing the need for teleological causes, he failed to recognize the source of purpose in natural systems. I contend that information management systems are the source; they accomplish purposes and functions whenever they apply information to guide their actions. The Scientific Revolution picked up four hundred years ago where Aristotle left off, but information as such would not be discovered until 194810, which then led into systems theory11, also called cybernetics, in the following decades. Complex adaptive systems are complex systems that evolve, and living organisms are complex adaptive systems with autopoiesis, the ability to maintain and replicate themselves. Brains are dynamic information management systems that create and manage information in real-time. Minds are subprocesses running in brains that create a first-person perspective to facilitate top-level decisions. Civilizations and software are human-designed information management systems that depend on people or computers to run them.

Gilbert Ryle famously put the nail in the coffin of Cartesian dualism in The Concept of Mind12 in 1949. We know (and knew then) that the proposed mental “thinking substance” of Descartes that interacted with the brain in the pineal gland does not exist as a physical substance, but Ryle felt it still had tacit if not explicit “official” support. He felt we officially or implicitly accepted that two independent arenas in which we live our lives, one of “inner” mental happenings and one of “outer” physical happenings. This view goes all the way down to the structure of language, which has a distinct vocabulary for mental things (using abstract nouns which denote ideas or qualities) and physical things (using concrete nouns which connect to the physical world through senses). As Ryle put it, we have “assumed that there are two different kinds of existence or status. What exists or happens may have the status of physical existence, or it may have the status of mental existence.” He disagreed with this view, contending that the mind is not a “ghost in the machine,” something independent from the brain that happens to interact with it. To explain why, he introduced the term “category mistake” to describe a situation where one inadvertently assumes something to be a member of a category when it is actually of a different sort of category. His examples focused on parts not being the same sort of thing as wholes, e.g. someone expecting to see a forest but being shown some trees might ask, “But where is the forest?”. In this sort of example, he identified the mistake as arising from a failure to understand that forest has a different scope than tree.13 He then contended that the way we isolate our mental existence from our physical existence was just a much larger category mistake which happens because we speak and think of the physical and the mental with two non-intersecting vocabularies and conceptual frameworks, yet we assume it makes sense to compare them with each other. As he put it, “The belief that there is a polar opposition between Mind and Matter is the belief that they are terms of the same logical type.” Ryle advocated the eliminativist stance: if we understood neurochemistry well enough, we could describe the mechanical processes by which the mind operates instead of saying things like think and feel.

But Ryle was more mistaken than Descartes. His mistake was in thinking that the whole problem was a category mistake, when actually only a superficial aspect of it was. Yes, it is true, the mechanics of what happens mentally can be explained in physical terms because the brain is a physical mechanism like a clock. But that is not the whole problem, and it is not the part that interested Descartes or that interests us, because saying how the clock works is not really the interesting part. The interesting part is the purpose of the clock: to tell time. The function of the brain cannot be explained physically because purpose has no physical corollary. The brain and the mind have a purpose — to control the body — but that function cannot be deduced from a physical examination. One can tell that nerves from the brain animate hands, but one must invoke the concept of purpose to see why. Ryle saw the superficial category mistake (forgetting that the brain is a machine) but missed the significant categorical difference (that function is not form). Function can never be reduced to form, even though it can only occur in a physical system by leveraging form. When we talk about the mind, we now know and appreciate that it is the product of processes running in the brain, but talking about the mind is not the same as talking about those processes any more than talking about cogs is the same as caring what time it is. The subject matter of the brain and mind is functional and never the same as the physical means used to think about them. Ryle concluded, “It is perfectly proper to say, in one logical tone of voice, that there exist minds and to say, in another logical tone of voice, that there exist bodies. But these expressions do not indicate two different types of existence, for ‘existence’ is not a generic word like ‘colored’ or ‘sexed.'” But he was wrong. In this case they really do indicate two different types of existence. Yes, the mind has a physical manifestation as a subprocess of the brain, so it is physical in that sense. But our primary sense of the word mind refers to what it does, which is entirely functional. This is the kind of dualism Descartes was grasping for. While Descartes overstepped by providing an incorrect physical explanation, we can be more careful. The true explanation is that functional things can have physical implementations, and they must for function to impact the physical world, but function and information are not physical and their existence is not dependent on space or time. Function and information abstractly characterize relationships and possibilities, and any concrete implementations of them are merely exemplary.

The path of scientific progress has influenced our perspective. The scientific method, which used observation, measurement, and experiment to validate hypotheses about the natural world, split the empirical sciences from the formal sciences like mathematics, which deal in immaterial abstractions. The empirical sciences then divided into natural sciences and social sciences because progress in the latter was only possible by making some irreducible assumptions about human nature, chiefly that we have minds and know how to use them. These assumptions implicitly acknowledge the existence of function in the life of the mind without having to spell it out as such. Darwin’s discovery of evolution then split the natural sciences into physical and biological sciences. Until that point, scientists considered living organisms to be complex machines operating under physical laws, but now they could only be explained through the general-purpose advantages of inherited traits. This shift to general from specific is the foundation of information and what distinguishes it from the physical. So both the biological and social sciences tacitly build on a foundation of the teleological existence of function, but they are reluctant to admit it because material science has rejected teleology as mystical. But a physical science that ignores the existence of natural information management systems can’t explain all of nature.

The social sciences presume the existence of states of mind which we understand subjectively but which objectively arise from neural activity. The idea that mental states are not entirely reducible to brain activity is called emergentism. An emergent behavior is one for which the whole is somehow more than the parts. Emergence is real, but what is actually “emerging” in information management systems is functionality. From a physical perspective the system is not doing some “new” kind of thing it could not do before; it is still essentially a set of cogs and wheels spinning. All that has changed is that feedback is being collected to let the system affect itself, a capacity I call functionality. The behavior that results builds on a vastly higher level of complexity which can only be understood or explained through paradigms like information and functionality. While there are an infinite number of ways one could characterize or describe information and functionality, all these ways have in common that they are detecting patterns to predict more patterns. Because one must look to information and function to explain these systems and not only to physical causes, it is as if something new emerged in organisms and the brain. Viewed abstractly, one could say that the simplistic causal chains of physical laws are broken and replaced by functional chains in functional systems. This is because in a system driven by feedback, cause itself is more of a two-way street in which many interactions between before-events and after-events yield functional relationships which the underlying physical system leverages to achieve functional ends. The physical system is somewhat irrelevant to the capabilities of a functional system, which is in many ways independent of it. Ironically, the functional system could thus equally claim the physical system emerges from it, which is the claim of idealism. All of language, including this discussion and everything the mind does, are functional constructs realized with the assistance of physical mechanisms but not “emerging” from them so much as from information and information management processes. A job does not emerge from a tool, but, through feedback, a tool can come to be designed to perform the job better. Thus, from an ideal perspective, function begets form.

Before life came along, the world was entirely physical; particles hit particles following natural physical and chemical rules. While we don’t know how the feedback loops necessary for life were first bootstrapped, we know conditions must have existed that allowed feedback and competition to take hold. I will discuss a scenario for this later, but the upshot is that DNA became an information store for all the chemicals of life, and it became embedded in single and later multicellular organisms that could replicate themselves. According to form and function dualism, the information in DNA is a nonphysical aspect that confers capabilities to the organism. We characterize those capabilities in biology as genetic traits that confer adaptive advantages or disadvantages relative to alternative versions (alleles) of the same genes. Chemically, a gene either codes for a protein or regulates other genes. It doesn’t matter to the feedback loops of natural selection what a given gene does chemically, just whether the organism survives. Survival or death is the way the function of a gene is measured. Chemistry is necessary for function, but survival is indifferent to chemistry. In many cases, the chemical basis of a genetic advantage seems clear, while in others the “purpose” of the gene can be hard to identify. It seems likely that each protein would fulfill one biological function (e.g. catalyzing a given chemical reaction) because heritability derives from selection events on one function at a time, so multiple functions would be challenging for natural selection to maintain because it seems mutations would be unlikely to be mutually beneficial to two functions. However, cases of protein moonlighting, in which the same protein performs unrelated functions, are now well-documented. In the best-known case, different sequences in the DNA for crystallins code either for enzyme function or transparency (as the protein is used to make lenses). A majority of proteins may moonlight, but, in any case, it is very hard to unravel all the effects of even a primary protein function. So any causal model of gene function will necessarily gloss over subtle benefits and costs. Its real purpose is a consolidated sum of the role it played in facilitating life and averting death in every moment since it first appeared. The gene’s functionality is real but has a deep complexity that can only be partially understood. Even so, approximating that function through generalized traits works pretty well in most cases. Although evolution is not directed, competition preferentially selects effective mechanisms, which is such a strong pressure that it tends to make genes very good at what they do. Mutations create opportunities for new functionality, but can also disable genes and their traits when niches change. To recap, genes collect information using DNA from feedback that predicts that a competitive advantage for a forebear will yield a competitive advantage for a descendant. It is a slow way to collect information, but evolution has had plenty of time and it has proven effective.

Beyond the information managed by the genes that form the blueprint of the body, organisms need to manage some information in real time, and instinct is a tool they all possess to do this. The mechanisms that regulate the flow of metabolites in plants via xylem and phloem are not called instincts because this feedback is applied directly without information processing (which only happens in animals via brains). Instinct covers all behavior based on information processing that doesn’t leverage experience or reasoning. Without experience or reasoning, an instinct will work the same “hardwired” way from birth to death. Instincts present themselves consciously as urges, covering all our hardwired inclinations for things like eating, mating, emotions, and drives. Instinct also includes sensory processing, such as smell, touch, body sense and most notably vision, which creates high-fidelity 2D images and transforms them into 3D objects which are further recognized as objects, either specific objects or types of objects.

Instincts take ages to evolve and solve only the most frequently-encountered problems, but creatures face challenges every day where novel solutions would help. A real-time system that could tailor the solution to the problem at hand would provide a tremendous competitive advantage. Two approaches evolved to do this, and each makes use of experience and reasoning. I call the first subconceptual thinking, which, as the name implies, is defined in terms of being below the other approach, conceptual thinking, which is also called logical reasoning. Logical reasoning starts with premises, which are statements (predicates about subjects and objects) taken to be true, and draws consequences from them. Subjects, objects, and the premises and predicates about them are all concepts, meaning discrete, expressed ideas. The ideas in a conceptual bucket can be defined with significant clarity, e.g. as dictionaries define words (which are examples of concepts). Subconceptual thinking is more commonly known as common sense or intuition. The memory supporting it is called experience, but I prefer the more specific term subconcepts. While concepts are well-defined, subconcepts are just impressions, but impressions based on lots of experience. Subconcepts form naturally from experience with no conscious effort to give us a gist of the way things are. Our brains automatically match new events against prior experience to find patterns, and this matching creates subconceptual knowledge. Any two or more interpretations of events that share features will be grouped as a subconcept. We can never name subconcepts because that would make them concepts, which are more exact but are weaker in breadth, detail, and context, because all our subconcepts connect to each other through the web of experience. The lowest level of subconcepts develop from sensory feedback and can even have an instinctive component. For example, we instinctively avoid pain, but we build a subconceptual database that generalizes from situations that were painful in the past to the kind of situations that should be avoided. All of our memory of sensations and emotions is subconceptual, which means that how we think about senses and feelings is a combination of current sensations and subconceptual impressions. These impressions or hunches have no basis in cause and effect or logic, though we may have impressions about what their causes might be. By comparison, concepts are distinct and identifiable based on a specific set of traits. Where a subconcept only creates impressions, really comprised of innumerable associated subconcepts (since no one subconcept can be distinguished as such), a concept specifically comes to mind from the appropriate triggering stimuli. But concepts have an inherent fuzziness about them that is quite different from subconcepts. A concept is a bucket that holds a generalization about a class of things or other concepts. Each bucket is distinct in our minds even though its definition is based on an approximate and not an exact match. A generalization can never be so precise that every match fits perfectly because its power derives from its statistical nature — it is about the kind of thing that will match, not the matches themselves. In other words, it is about the functionality of matching and not the form of the match. But carving out these conceptual buckets of distinct kinds of matches opens the door to performing logical thinking with ideas instead of just forming impressions about them. And logical thinking leads to chains of entailment — causes and effects — which has much more potential to make strong predictions when it is done well. Instincts urge us toward fixed reactions and subconcepts promote simple reactions based on experience, but conceptual thinking can produce multi-step solutions beyond the reach of either.

Although many concepts are built on other concepts, ultimately all concepts are built on underlying instinctive and subconceptual knowledge. To be “built” on them ultimately means that their functional value to us depends on their connections to instinct and subconcepts, but concepts also have a sense in which they stand completely apart from this foundation. If one takes concepts as axiomatic, one can view all the logic that follows from rules operating on them as a self-contained system of knowledge. So conceptual thinking can be viewed as an information management process that combines information gathered instinctively and subconceptually from the bottom up with information created conceptually from the top down so that they meet in the middle. This process works smoothly in real time because internal brain processes are constantly at work at all levels trying to align top to bottom using best-fit algorithms. The requirement from the top down is ultimately to make discrete decisions, and conceptual approaches are well-suited to this, but we can make top-down decisions using just experience and instinct alone or just instinct alone, and more primitive animals lacking concepts or both concepts and subconcepts make all their decisions this way. All three of these approaches have in common that they are leveraging information, which means they are making predictions based on best fits to prior patterns, which is the essence of what information is. It is quite remarkable that we can seamlessly integrate these kinds of information and join top to bottom because these are more complex approaches to information management than we have even contemplated programming into a computer, but they have always been central design criteria for brains and so naturally evolved that way from the beginning.

Most of the information our brains gather about the world is subconceptual and can be trusted to guide us where we have little or no underlying conceptual understanding. While all our experiences create impressions that are used to form both subconcepts and concepts, concepts create an explicit link between a concept’s bucket or name to its definition (even though that definition itself has fuzziness as described). We know the approximate boundaries of the definition of a concept independent of any attempt to describe that definition. It is implied by supporting examples and connections to underlying traits. Any one example will carry more detail than the encompassing concept, but the traits the examples have in common help define it, even allowing for some traits that are optional to it. An object instance is a specialized kind of concept that associates to a specific thing or idea rather than a general type of thing as generic concepts do. While this makes instances functionally distinct from concepts at large, our mental machinery for managing instances can be thought of as treating them as special kinds of concepts. Like a concept, an instance is a bucket with defining characteristics, but, in the case of an instance, existence (physical or functional) is one of the characteristics. We can name a concept by attaching a word or phrase to it, and we can name an instance by using a proper noun. Many words are attached to multiple concepts, each of which will have its own entry in the dictionary. Words provide the most versatile anchors for referencing concepts, but we manage many concepts contextually without having specific or entirely accurate words to name them, for example for familiar parts of larger objects. Thinking about concepts, instances, and subconcepts to find new patterns and reach conclusions is called reasoning. Reasoning is the conscious capacity to “make sense” of things by producing useful information linking them together.

The most primitive subconcepts, percepts, are drawn from the senses using internal processes to create a large pool of information akin to big data in computers. Subconcepts and big data are data that is collected without explicit consideration of the data’s purpose. It is the sort of data that has been helpful in the past, so it is likely to be useful again. Over time we develop algorithms that mine subconcepts or big data to find useful patterns that lead to helpful actions, still without having a clear idea about what the data “means.” We don’t have to understand common sense, intuition or music to be talented at them. Concepts, on the other hand, are akin to structured data in computers. A concept is an idealization of a pattern found in subconcepts into a generalized element with specific associated properties. While the patterns are primarily subconceptual, a network of relationships to other concepts also forms. A concept is a pure abstraction (i.e. having no corollary in the physical world) that is defined by its subconceptual properties and its relationships to other concepts. The patterns are frequently chosen so that the concept can be reliably correlated to a generalized class of entities in the physical world, but this connection is indirect and does not make the concept itself physical. Basic reasoning uses subconceptual pattern analysis, including recognition, intuition, induction (weight of evidence) and abduction (finding the simplest explanation). But deduction (aka entailment or cause and effect) cannot be done subconceptually, because by construction entailment requires discrete premises and not diffuse, unstructured data. Basic reasoning can also leverage concepts, but deduction specifically requires them. Logical reasoning principally means deduction, though it arguably also includes logical treatments of induction and abduction, but I will use the term logical reasoning to refer specifically to our conscious conceptual thinking capacity.

Science differentiates itself from other schools of thought by only embracing conceptual explanations. This is not to mitigate the value of instinctive and subconceptual knowledge, but to emphasize the role of science in providing logically-supported underlying explanations, which knowledge based on instinct and subconcept can’t do. While all of us will at times accept and act on instinctive and subconceptual knowledge, we set a higher bar for science because we have learned from experience that conceptual approaches, and especially those embraced by science, are both more reliable and versatile predictors of future events. So I will be making a very carefully reasoned argument rather than using my feelings and experiences to appeal to yours, but first I need to speak more about the powers and limitations of instincts and subconcepts. We have a conscious bias toward conceptual thinking because are aware of all or most of our logical reasoning but are unaware of most of our instinctive or subconceptual thinking. Thinking, or, more accurately, neural processing, that happens outside our conscious awareness is “nonconscious” thought performed by the nonconscious mind14. Up to 90 to 95 percent of mental processing is nonconscious, though it is arguably not meaningful to divide processing in this way given that the conscious mind is more a supervisor than a worker. Before going further, let me contrast nonconscious with the more familiar term subconscious:

nonconscious: mental activity that is not conscious and cannot be brought into conscious awareness because it is outside the realm of conscious experience

subconscious: mental activity just below the level of consciousness that influences conscious thoughts which can potentially be brought into conscious awareness because it is inside the realm of conscious experience

Freud coined the term subconscious in 1893 but abandoned it because he felt it could be misunderstood to mean an alternate “subterranean” consciousness. Though Freud gave up on it, it is used to refer to the well-known factors that influence our conscious thoughts and feelings that are at the “tip of the tongue”, as it were. Freud’s own model was based on the idea of a conscious mind and an unconscious mind. Freud’s unconscious mind was the union of repressed conscious thoughts that are no longer accessible (at least not without psychoanalytic assistance) and the nonconscious mind. He saw the preconscious, which is quite similar to what we now call the subconscious, as the mediator between them:

Freud described the unconscious section as a big room that was extremely full with thoughts milling around while the conscious was more like a reception area (small room) with fewer thoughts. The preconscious functioned as the guard between the two spaces and would let only some thoughts pass into the conscious area of thoughts. The ideas that are stuck in the unconscious are called “repressed” and are therefore unable to be “seen” by any conscious level. The preconscious allows this transition from repression to conscious thought to happen.15

Going forward I will only use the terms nonconscious and conscious, although I would just point out that although the two are strictly separate, the conscious mind receives lots of help from the nonconscious. Arguably, the main job of the nonconscious mind is to prepare information for conscious consumption. But consciousness can only process information in a specific way, namely from the perspective of awareness. Only a limited amount of information can be held in awareness at any given time, including some from each sensory channel and some under the focus of attention. The value of restricting the information in this way is that all the items under conscious awareness can be weighed, considered, and prioritized to help facilitate decision-making. It is a top-down approach for gathering all relevant information in one place to review it before acting on it. These restrictions suggest that information must be “presented” to consciousness in an appropriate form that let it perform just this high-level review. Conscious thinking performs operations on these items under focus in just one stream or sequence at a time. Conscious streams of thought eventually result in a single stream of conscious decisions. Note that we can all time-share different streams of thought at once by holding them in memory to produce an interleaved sequence of decisions supporting different streams, but we will only be thinking of one of them at a time. Some people with dissociative identity disorder (DID, formerly called multiple personality disorder) can run multiple consciousnesses or alters at the same time, but, having only one body, they must find a way to share the body to execute decisions. This suggests that while our capacity for consciousness is innate, a single, unified consciousness happens most of the time mostly because it is works better for most people. In fact, DID seems to nearly always result from extreme childhood trauma and starts with the main personality “going away” to protect itself, allowing alters to arise to fill the gap.

While awareness is a restricted lens that views information from custom channels through one stream of consciousness at a time, nonconscious processes can process information in parallel without such limitations. All this parallel processing, probably over 99% of the processing in the brain, ultimately creates the “theater of consciousness”, a simplified view of the world that unifies sensory inputs, memory, and thought into a seamless conscious experience. For example, to create sight the photoreceptor cells of the retina build a pixelated image to a given resolution, brightness, and color which is further processed using edge detection into similarly-colored regions and then grouped into objects which are then recognized by comparisons with memory. Consciously, we receive input from each of these levels, including the image, the objects, and what they are, but they are smoothly joined up for us. Emotion is a nonconscious process that accesses information created consciously and delivers emotional sensations back to consciousness. Subconceptual processing nonconsciously surveys all of memory simultaneously to find popular matches between current conditions and past experience and delivers impressional sensations back to consciousness. These impressions can carry instinctive overtones which affect how inclined we are to attend to them, and memory overtones which we can probe further to recall additional subconcepts or concepts. Finally, conceptual thinking itself depends on considerable nonconscious assistance which we take for granted. All our mental abilities need support processing which our brains do for us outside our awareness. This includes memory, mental modeling, math, spatial thinking, episodic thinking, language, and theory of mind (the ability to attribute mental states — beliefs, intents, desires, pretending, knowledge, etc. — to oneself and others), to name the most apparent of our talents.

Most of memory processing is nonconscious. Consciously, we know that memories come to us when they are appropriate, either from recognition or by lookup. We have a pretty good sense of what we know, which is like an index, so we expect that when we “look up something” that we believe know we will recall the full memory. Sometimes our memory fails us and we can’t recall something we used to know. But the storage and recall of memory are nonconscious processes for which consciousness is just an interface. We have no conscious understanding of how we do it. We all have enough memory to form an episodic record of every event in our lives, but this doesn’t mean we commit every event to long-term memory or that we could recall it if we did. Memories that the brain deems superfluous will tend to become less accessible to most people over time, and the most frequently-accessed memories become the strongest. When we forget, it is typically more because we have can’t access the memory than that we have lost it altogether, and with further effort we may recall it. Whether we can access an event depends on how unique it is, because as we store episodic memories we nonconsciously link them up to related memories both subconceptually and conceptually. Memories that overlap a lot with others in their links can seem to “bleed” into each other and become indistinguishable except for truly unique moments. When we do visit an unusual place just once, we are more likely to pay attention to the details and be able to recall it specifically long into the future.

Language processing is also mostly nonconscious. Although a language itself is an artificial construction that people develop over time, we know that our ability to use language depends on support from specialized brain areas for particular language functions. Damage to these areas can disrupt language ability without affecting other mental abilities. While some have speculated that these areas shape language through a genetically-based universal grammar (UG), I think it is more likely that it is pretty plastic and is mostly a kind of specialized memory. Linguistic memory is very good at linking concepts to representations which can be vocalized, signed, or written, and can learn and internalize grammar rules in a way that is analogous to motor memory. That we see universality in language elements is more a sign of the effectiveness of those elements than being “hardwired” to speak only certain kinds of grammars. This is a very controversial area, and I am not going to defend this position in detail, but I will guess that the commonalities in language stem more from commonalities in thinking processes than from a genetic universal grammar. All people think in terms of actors, actions, and objects acted upon, and all languages facilitate communicating these conceptual thoughts using vocabulary chained together into propositions. People also have emotions and impressions, and languages help us communicate these thoughts through non-propositional content, which can either employ vocabulary, intonation, or nonvocal means. All languages can add words and have the potential to express any concept, but all have native constructions that facilitate or hinder certain expressions. For example, some languages always reveal number, sex, time, location, etc., while others can be vague on these matters. Linguistic relativism, aka the Sapir-Whorf Hypothesis, suggests that the structure of a language affects and perhaps constrains its speakers’ worldview. Some effects are probably undeniable, e.g. the use of masculine pronouns for mixed or indeterminate gender in nearly all Indo-European languages creates a discriminatory bias in the minds of their speakers. I think the idea that language structure can limit what people can think goes too far since thought happens at a lower level than language, but it does establish paradigms that can influence thought. For example, Germans may experience more schadenfreude, the pleasure derived by someone from another person’s misfortune, than English speakers because they have a word for the concept.
It is a fair analogy to say that consciousness is produced for us, like a movie, by the nonconscious mind. Consciousness makes us feel like we are the interactive stars in a continuous movie. The difference is that the observer of this internal movie is not another person, but just the consciousness of a person, and their nonconsciousness is the producer. Also, this partnership creates a much richer experience than any movie. I call our live conscious experience that is based on our sensory inputs the mind’s real-world feed, and from it, we create an overall model of the current state of the physical world that I call the mind’s real world. We can also simulate “worlds” in our minds by “constructing” sensory information from memory. I call these simulated-world feeds and simulated worlds. A daydream or a story creates a detailed simulated world through a feed into it. Projected real worlds constitute a large subclass of simulated worlds. Projected real worlds attempt to realistically project what might happen in the mind’s real world. The ability to anticipate what will happen is instrumental to controlling the body, which is the mind’s purpose.

Simulated worlds themselves are a subclass of mental models, which are frameworks of subconceptual and conceptual thoughts bound together by a set of rules. Mental models may engage our conscious awareness as simulated worlds do, or they may be any abstract way we devise for thinking about something. As we mature we build a vast catalog of mental models to help us navigate the world. Our whole conception of the physical world comes to us through the mind’s real world, which is a simulation. It is very detailed and very reliable, but it is ultimately only a description of the real world with finite granularity. The implication of this is that while we only have one mind’s real-world feed, we model the mind’s real world using countless mental models to which we attach a greater or lesser degree of confidence. The mind’s real world is thus a constellation of simulated worlds and parts of worlds, many of which may take different perspectives on the same things. We don’t really know which is right or what “right” really means; we only know each model is effective in its own way. We aspire for our models to embody truth, meaning that they will always hold up when applied correctly, but we recognize that knowledge is always a compromise and truth is always contextual. Within a well-formed mental model, truth can be absolute, but the application of that model always introduces the possibility of alignment errors. Also note that the alignment from senses up to mental models is a continuous, two-way street. With substantial nonconscious support, we are constantly testing and adjusting the mental models that comprise the mind’s real world to bring their predictions more in line with the real world. As Andy Clark puts it in Surfing Uncertainty, “We alter our predictions to fit the world, and alter the world to fit our predictions.”

Our most effective mental models use a consistent and logical conceptual framework, because logical reasoning is better for solving problems than pattern recognition, common sense, and intuition, which can’t work out the implications that novel problems present. Logical reasoning gives us an open-ended capacity to chain causes and effects. It is important to remember that concepts and mental models build on other concepts, mental models and ultimately on instincts and subconcepts. This deeper framework is an essential component of understanding. John von Neumann once said, “Young man, in mathematics you don’t understand things. You just get used to them.”16 But it’s not just mathematics; all of understanding is really a matter of getting used to things. A conceptual model can be internally consistent, but its larger meaning depends on its alignment to simulated worlds, the mind’s real world, and the physical world, which we sense through its deeper connections to all our mental models. And good alignment is ultimately the basis of functionality, meaning news you can use.

In summary, all thought and behavior result from instinct, subconceptual thinking, and conceptual thinking. Our mental models combine these approaches to leverage the strengths of each. Genes collect and refine the first level of information across an evolutionary timescale. Instincts (senses, drives, and emotions) create the second level of information from patterns processed in real time but whose explicit utility has been established by evolution. Subconcepts form a third level of information whose specific use has not been predetermined by evolution, but which can be counted on to be useful over time. Finally, concepts are a fourth level of information which is representational or symbolic. Like subconcepts, conceptual thinking is innate and its adaptive value is established only by reproductive success. Evolution doesn’t know why collecting real-time information helps animals because genes are selected based only on their success and not the reasons for their success. But from our perspective, we can see the distinct purposes that instincts, subconcepts, and concepts fulfill. These three talents are distinct but can be hard to cleanly separate. It is hard to tell where instinct leaves off and real-time learning begins because they integrate so well. All our motivation derives from instincts, so even our most complex behaviors are partly instinct and partly learned. In principle we can always distinguish concepts from subconcepts because concepts are conscious collection points of information based on commonalities and subconcepts are only diffuse impressions, our mind doesn’t distinguish them for us. We group related bits of information together conceptually in countless ways for very transient purposes, and even common concepts have many variations and connotations, so it is hard for us to pin any concept down. It is even harder to identify subconcepts because to do so would conceptualize them, so we have to be content with inferring their existence from the realization that much of our experience is not bucketed conceptually. And although a concept only formally differs from a subconcept because it represents a grouping of information through an internal symbol or bucket, that symbol can participate in logical forms, which abstract logical operations from their content, making it possible to devise internally consistent logical models within which everything is necessarily true. We then extend necessity to possibility using modal logic, which splits a necessary world into a series of possible worlds. While the mind’s real-world feed results in just one necessary past, we view the future as a set of possible worlds and use information to find the most probable.

Reasoning and especially logical reasoning can only be done consciously. Reasoning is considered a conscious activity even though some parts of reasoning, e.g. intuition, happen nonconsciously. All our top-level decisions are controlled consciously, which means we can control everything above the level of a reflex. But, ironically, none of our top-level decisions are executed consciously. What I mean by this is that the conscious mind delegates the execution of every decision to nonconscious processing. We take it for granted that our wish to blink our eyes will cause the appropriate muscles to contract, but we have no idea how that happens. Much more than that, we frequently delegate the decision to nonconscious control on a “preapproved” basis. Blinking is preapproved before we even know we have eyes or why we blink them, but we can take that preapproval away and control it consciously (though we won’t do it for long). Almost all the details of walking and moving are preapproved, and we don’t take much conscious notice unless we suspect we might want to override habit. This delegation of authority is necessary because the role of the conscious mind is to make top-level decisions, and it only has a single, narrow stream of conscious thought (or a handful, in the case of DID) with which to consider all relevant factors, so the more habitual behavior can be automated the better. Non-habitual behavior can’t be delegated to nonconscious processes because the nonconscious mind can’t reason through solutions logically, but it can remember strategies that have worked before subconceptually and will present good fits almost instantly through intuition, which constitutes much of the power behind snap judgments. While it is true that experiments have proven the brain can know what decision we will make up to ten seconds before we make it, this only demonstrates our power to “unconsciously prime” or preapprove decisions within operating parameters we have consciously established.17

Dam building is a complex behavior in beavers that seems like it needed to be reasoned out and taught from generation to generation, and yet “young beavers, who had never seen or built a dam before, built a similar dam to the adult beavers on their first try.”18 So it is entirely instinctive, and results mostly from an innate desire to suppress the sound of running water. We also know language acquisition in humans is substantially innate because humans with no language will create one19. But we know that all the built artifacts of civilization (except perhaps the hand axe, which may have been innate), including all its formal institutions, are primarily the products of thinking, both subconceptual and conceptual. Our experience of our own minds is both natural (i.e. instinctive) and artificial (i.e. extended by thinking and experience), but these aspects become so intertwined with feedback that they can be difficult to impossible to distinguish in many cases. For example, we know our sense of color is innate, and yet our reactions to colors are heavily influenced by culture. Or, we sometimes have the impression we have made a logically reasoned argument when all we have done is rationalize an instinctive impulse or an intuitive hunch. But although the three capacities always work together and overlap in their coverage, I believe they arise from fundamentally different kinds of cognitive processes that can be examined separately.

While nearly all animals learn from experience, demonstrating subconceptual thought, not all can think conceptually. Birds and mammals (let’s call them advanced animals for short) demonstrate problem-solving behavior including novel planning and tool use that goes far enough in some cases to indicate use of concepts to plan causes and effects. Non-advanced animals can’t do this and seem to lack the brain areas we associate with conceptual thought, so I believe only advanced animals have any capacity for it. We only know we are conscious and that our logical reasoning is conscious from introspection, so we can’t prove it in advanced animals, but observations and shared evolution makes it very likely for mammals and pretty likely for birds as well. Still, we know humans are “smarter,” but what is it that distinguishes us? We know our thinking skills derive substantially from the size of our cerebral cortex, which is largely a function of how wrinkled it is. The number of cortical neurons correlates fairly well to how we might rank animal intelligence, with humans far outstripping other mammals20 (though not quite equaling some whales). But what do those neurons do to make us smarter? Over 500 genes contribute to intelligence21, and though we don’t know what they do, I would say that our greater capacity for abstract logical reasoning is the most defining trait of human intelligence. Abstraction is disassociation from specific instances, also called generalizing, and logical reasoning draws conclusions from generalized concepts. While other advanced animals have some capacity for this, humans engage in directed abstract thinking, which means we use models and simulations to solve problems independent of the here and now. When humans became able to decouple simulations to an arbitrary degree from the mind’s real-world feed, it expanded our intellectual potential arbitrarily as well. Any topic is now within our reach, even if we are limited by our brains in how efficiently we can address it. Our capacity for abstraction coevolved with improved abilities to think spatially, temporally, logically and especially linguistically because each new addition to our mental arsenals quickly spread from competition. As we became capable with tools, our ecological niche expanded to potentially include all niches. Conceptual thinking combined with tools let us develop and act on causal chains of reasoning that outperform instinctive and subconceptual thinking.

Abstraction opens the door to an unlimited range of possibilities, but evolution has kept this facility practical. The range of what is functionally possible is the domain of philosophy, and the range of what is functionally actual (and consequently practical) is the domain of psychology, the study of mental life. My explanation of the mind starts by enlarging our philosophical framework to include functional existence and moves from there to explain our psychology. Psychology spans neuropsychology, behavioral psychology, evolutionary psychology, cognitive psychology, psychoanalysis, humanistic psychology, introspection, and cognitive science (or at least some of it), and so brings a lot of perspectives to bear on the problem. They each constrain the possible to the actual in a different way depending on their functional objectives. Before I look at what psychology tells us, I’d like to develop a philosophy of information management. I described above how information is managed in the mind through instinct and thinking. Instinct imposes ingrained behaviors while thinking customizes behaviors in novel circumstances while leveraging learning and memory. Instance is not divisible into explanatory parts; natural selection essentially allows the ends to justify the means. Subconceptual thinking is also unconcerned with the means; it evolved because real-time pattern storage, analysis and lookup was useful to survival. While the same could be said about conceptual thinking, the means are more relevant both because the algorithms are open-ended and because we are conscious of them. Conceptual thinking (logical reasoning) establishes its own criteria, where a criterion is a functional entity, a “standard, ideal, rule or test by which something may be judged.” This implies that reasoning depends both on representation (which brings that “something” into functional existence) and entailment (so rules can be applied). Philosophically, reasoning can’t work in a gestalt way as instinct and subconcepts do; it requires that the pool of data be broken down into generalized elements called concepts that interact according to logical rules. Logical reasoning operates in self-contained logical models, which lets it be perfectly objective (repeatable), whereas subconceptual thinking is a subjective gestalt and hence may not be repeatable. Objective, repeatable models can build on each other endlessly, creating ever more powerful explanatory frameworks, while subjective models can’t. There may be other ways to manage information in real time beyond instinct and thinking, but I believe they are sufficient to explain minds. To summarize, functional existence arises in some complex physical systems through feedback loops to create information, which is a pattern that has predictive power over the system. The feedback loops of instinct use natural selection over millennia to create gestalt mechanisms that “work because they work” and not because we can explain how they work. The feedback loops of thinking use neural processing over seconds to minutes. Subconceptual thinking works because life is repetitive, so we have developed generalized nonconscious skills to find patterns and benefit from them. Conceptual thinking adds more power because self-contained logical models are internally true by design and can build on each other to explain and control the world better.

I’ve made a case for the existence of functional things, which can either be holistic in the case of genetic traits and subconceptual thinking or differentiated in the case of the elements of reason. But let’s consider physical things, whose existence we take for granted. Do physical entities also have a valid claim to existence? It may be that we can only be sure our own minds exist, but our minds cling pretty strongly to the idea of a physical world. Sensory feedback and accurate scientific measurement and experimentation of that world seem almost certainly to establish that it exists independent of our imagination. So we have adequate reason to grant the status of existence to physical things, but we have to keep in mind that our knowledge of the physical world is indirect and our understanding of it is mediated through thoughts and concepts. Ironically, considering it has a secondary claim to existence, physical science has made much more definitive, precise, and arguably useful claims about the world than biology and the social sciences. And even worse for the cause of the empirical functional sciences is that the existence of function has (inadvertently) been discredited. Once an idea, like phlogiston or a flat earth, has been cast out of the pantheon of scientific respectability, it is very hard to bring it back. So it is the case that dualism, the idea of a separate existence of mind and matter, has acquired the taint of the mystical or even the supernatural. But dualism is correct, scientific, and not at all mystical when one formulates function as a distinct kind of existence, one that becomes possible in a physical universe given enough feedback.

The laws of physical science provide reliable explanations for physical phenomena. But even though living systems obey physical laws, those laws can’t explain functionality. Brains employ very complex networks of neural connections. From a purely physical standpoint, we could never guess what they were up to no matter how perfectly we understood the neurochemistry. And yet, the mind arises as a consequence of brain activity, which is to say it is a process in the brain. The success of physical science coupled with the physical nature of the brain has led many to jump to the conclusion that the mind is physical, but the mind is just the functional aspect of the brain and not physical itself per se. Pursuing an eliminativist stance, the neurophilosopher Paul Churchland says the activities of the mind are just the “dynamical features of a massively recurrent neural network”22. From a physical perspective, this is entirely true, provided one takes the phrase “massively recurrent neural network” as a simplified representation of the brain’s overall architecture. The problem lies in the word “features,” which is an inherently non-physical concept. Features are ideas, packets, or groupings of abstract relationships about other ideas, which, as I have been saying, are the very essence of non-physical, mental existence. These features are not part of the mechanism of the neural network; they are signals or information that travel through it. This “traveling” is a consequence of complex feedback loops in the brain that capture patterns as information to guide future behavior. Any one feature can be thought about in different ways at different times by different people but will still fundamentally refer to the same feature, which means the functions it makes possible.

Philosophers have at times over the ages proposed different categories of being than form and function, but I contend they were misguided. Attempts to conceive categories of being all revolve around ways of capturing the existence of functional aspects of things. Aristotle’s Organon listed ten categories of being: substance, quantity, quality, relationship, place, time, posture, condition, action, and recipient of action. But these are not fundamental, and they conflate intrinsic properties with relational ones. Physically, we might say all particles have substance, place, and time (to the extent we buy into particles and spacetime), so these are inherent aspects of physical objects. All the other categories characterize aspects of aggregates of particles. But we only know particles have substance, place and time based on theories that interpret observed phenomena of them. Independent of observations, we can posit that a particle or object itself exists as a noumenon, or thing-in-itself. Any information about the object is a phenomenon, or thing-as-sensed. We have no direct knowledge of noumena; we only know them through their phenomena. Noumena, then, are what I call form, while the interpretation of phenomena is what I call function. Some aspects of noumena are internal and can never be known, while others interact with other noumena to propagate information about them, called phenomena. We can only learn about noumena by analyzing their phenomena using information management processes. We further need to break phenomena down into three aspects. A tree that falls in a forest produces a sound, being a shockwave of compressed air, that is the transmitted phenomenon. If we hear it, that is the received phenomenon. If we interpret that sound into information, that is the true phenomenon or thing-as-sensed, since sensing means “making sense of” or interpreting. The transmitted phenomenon and received phenomenon are actually noumena, being a shockwave or vibration of eardrums in this case, so I will reserve the word phenomenon for the interpretation or information processing itself. Interpretation is strictly functional, so all phenomena are strictly functional. Similarly, all function is strictly phenomenal in the sense that information is based on patterns and so is about them, because patterns are received phenomena. Summarizing, form and function dualism could also be called noumenon and phenomenon dualism. This implies that phenomena are not simply the observed aspects of noumena but are functional constructs in their own right and are consequently not ontologically dependent on their noumena. One could also so that all knowledge is phenomenal/functional while the objects of all knowledge are noumena/forms. Finally, I’d like to note that while form usually refers to physical form, when we make a functional entity the object of observation or description, it becomes a functional (non-physical) noumenon. For example, “justice” is an abstract concept about which we can make observations and develop descriptions. Our interpretations or understanding of it are phenomenal and functional, but justice itself (to the extent it can abstractly exist by itself) is just noumenal. While we can’t know anything for certain about noumena, a convenient way to think about them is that if we had exhaustive, detailed phenomenal knowledge of a noumenon would reveal the noumenon to us. It’s not the same, because it is descriptive and not the thing itself, but it would eliminate mysteries about the noumenon. Put another way, our knowledge of (noumenal) nature grows more accurate all the time through (phenomenal) theories. We like to think we know our own minds, but our conscious stream can only access small parts at a time, which gives a phenomenal view into our own mind, whose noumena are only partially known to us. In other words, we know our minds have functions, but our knowledge of them is indirect and imperfect. But we tend not to think that way because we can study our minds indefinitely until we are quite confident we have gotten sufficiently “close” to the noumena. So abstract nouns like “apple” about physical noumena and like “justice” about functional noumena both define noumenal concepts which we feel we understand well even though we can only define and describe them approximately using words. While the full noumenal nature of “apple” and “justice” varies from person to person and depends on all their experience with the concepts and assessments they have made about them, our phenomenal understanding of them at a high level intersect well enough that we can agree on basic definitions and applications in conversation.

Because information management systems physically exist, one can easily overlook that something more is happening. Let’s take a closer look at the features of functional existence. It is not that the functional exists as a separate substance in the brain as Descartes proposed or that it is even anywhere in the brain, because only physical things have a location. Instead, any thought I might have simultaneously exists in two ways, physically and functionally. The physical form of a thought is the set of neurons thinking the thought, including the neurochemical processes they employ. While we can pinpoint certain neurons or brain areas that are more active when thinking certain thoughts, we also know that the whole brain (and all the control systems of the body) participates in forming the thought because everything is connected. Everything is connected because instinct and subconceptual thinking are gestalts that draws on all our knowledge (including concepts), and logical reasoning uses closed models based on concepts, which are in turn built on instincts and subconcepts. The functional form of a thought is the role or purpose it serves. When we reflect on this purpose logically we form a concept of it that can be the subject or the predicate of a proposition with features that relate it to all other concepts. Functionality in minds has a practical purpose (even function in mathematics must be practical for some context, but here I refer to evolutionary practicality). A thought’s purpose is to assist the brain in its overall function, which is to control the body in ways that stand to further the survival and reproduction of the gene line. “Control” broadly refers to predictive strategies that can be used to guide actions, hopefully to achieve desired outcomes more often than chance alone would. And even seemingly purposeless thoughts participate in maintaining the overall performance of the mind and can be thought of as practice thoughts if nothing else. For a predictive strategy to work better than chance, it needs to gather information, i.e. to correlate past situations to the current situation based on similarities. The relationship between information and the circumstances in which it can be employed is inherently indirect because information is indirect; it is necessarily about something else. So we can conclude that function acts in our physical world to control the actions of living things, or, by extension, in information management systems we create to control whatever we like.

The Science of Function

Contents

The Essence of Function
Function in Concepts
Function in the Sciences
Function in Non-living Things
Function in Living Things
Function in the Mind
Natural and Artificial Information

The Essence of Function

I’ve made a case for functional existence being independent of physical existence and said that it came into existence when living things began managing information through DNA. But speaking more abstractly, what is the essential character of a functional entity? The essence of a thing is also called the thing itself or its noumenon. The noumenon of a physical thing is its form in spacetime. The noumenon of a functional thing is its function, which is what it can do. Functional things are independent of space or time, but information management systems make it possible for some physical things to perform functions. I need to talk a bit more about those systems before we can talk about the functional entities they manage. Two different classes of information management systems exist that achieve functionality in two different ways. Biological organisms collect information in DNA. DNA can’t effect permanent functional changes more than once per generation because the whole organism uses a single set of DNA replicated from its first cell, the zygote. So DNA function is, in this sense, static over an individual’s lifetime (though how and when it is expressed varies over time). Animals also manage information using brains. The brain is built using genetic plans and is able to perform many skills innately. But using these skills, it also collects real-time information and stores it in memory, which lets the brain’s function grow from experience dynamically over an individual’s lifetime. This real-time identification and storage of patterns that improve functionality is called learning. In traditional evolutionary theory, DNA doesn’t learn but instead depends on random mutations to improve function by pure chance. I will review a new theory further down that proposes that DNA must learn and does, albeit slowly. But the more significant point regarding minds is that brains learn. Information is dynamically assessed neurochemically via different kinds of internal feedback loops, producing better quality information for use going forward. Sometimes these loops take the form of conscious simulations in mental models, and these comprise what we call an understanding of the underlying information. And, not incidentally, determining the essential character of functional entities means gaining an understanding of them from mental models. So we can conclude that biological organisms manage information genetically over multigenerational time and brains manage information neurochemically during a single generation.

We will use this dynamic capacity of our minds to learn to develop an understanding of physical and functional noumena. Our understanding is itself a functional entity, not to be confused with the objects under study. Understandings are models that describe aspects of the objects under study that are knowable from observations or considerations of those objects but are not the objects themselves. In other words, understanding can be said to be a phenomenal or observational account of a noumenal entity that characterizes it without being it. Understanding is fundamentally a phenomenal, indirect, “about” kind of functional entity that refers to another entity.

Functional entities that can work independently of other function entities are autonomous, and those that are component functions of autonomous entities are dependent. Only organisms are completely autonomous entities, though in the long run they need an ecosystem including other organisms. Brains, minds, civilizations, and computer programs are also autonomous entities, though they build on each other and ultimately on organisms. The dependent functions of organisms include the jobs of everything from proteins to cell organelles to organs. For brains, it includes a variety of nonconscious bodily functions such as heart rate, digestion, and rate of respiration, as well as the many nonconscious support functions needed to support the mind, such as 3-D vision processing and our knack for language. For minds, it includes the capacities created by feelings, subconcepts, and concepts. For civilizations, it includes the functions served by laws, institutions, and conventions. And for computer programs, it includes whatever functions the programs define. Any discussion we have about the dependent functions of any of these systems will itself be conducted using dynamic functions of our minds, which is to say ideas, which are composed of feelings, subconcepts, and concepts. So although function is not itself physical, functions do manifest physically in these kinds of information management systems by exploiting the uniformity of nature to gather information using feedback to achieve functional goals in an otherwise indifferent universe. This natural and perhaps inevitable consequence of the laws of nature means that both physical and functional existence are parts of the natural world, and not just the more visible and measurable physical existence. While it is a given that information management systems are functional entities, most of my discussions will concern the dependent functions they perform. Regardless of how much we know about the dependent functions of organisms, brains, minds, civilizations or computer programs, we can at least say that they are functional and that our understanding of them is functional, while rocks, streams and weather patterns are inanimate, are not under information management, and are not functional entities.

Our understanding of physical noumena is necessarily indirect because all knowledge of the physical world is mediated through our senses. Any understanding we develop of the mind as a natural phenomenon is also necessarily indirect because it must be mediated through our cognitive sense of it. Consequently, we can never know the “true” nature of natural noumena; we only know them through their phenomena. Physical phenomena can be observed using our senses, but can also be measured with much greater objectivity and accuracy using instruments. We can describe functional entities, too, by “observing” their phenomena, by which I mean considering their functions by thinking about them rather than using our senses. Since both kinds of observation are performed by our minds and hence are functional exercises, we do them and record them in much the same way. Specifically, the process involves collecting data and looking for patterns in the data that have been demonstrated by feedback to help in making useful predictions. The process has two feedback loops, one for forming hypotheses or generalizations and one for testing them. This generalized approach to understanding things is also the basis of the scientific method. The data and patterns we discern can be said to characterize or represent the object under observation but are quite different from it, even though we usually think of them as being the same thing. We do fully understand that our internal representation of something is a wholly different kind of thing than what it refers to, but references like this are only useful to us if we act on them as being equivalent, so we usually ignore the distinction. We have an innate capacity, called belief, for managing our degree of trust in this relationship, which I will discuss later.

Not all functional noumena are natural entities that can be understood only through observation. Some can be known directly, namely those that are true by definition or tautological. If one names a concept with a word or phrase, then the fact that the name references the concept is true by definition. When we define a formal system and the rules that drive it we have established a number of necessary truths. The definitions and rules are true by definition, and conclusions we can logically prove within these systems are necessarily true by deduction, which gives us direct access to noumenal knowledge, unlike the inductive knowledge we create from observations or considerations. Our conceptual understanding of the world is much more powerful than our subconceptual understanding because it organizes it into a hierarchy or network of causal models that give us much deeper and more accurate expectations. But to work, it means we need to conjecture conceptual models that approximately describe how the world works and then align those models with observed phenomena. Our knowledge of our own models is direct and deductive, but it only approximates reality itself inductively. So when we think about dynamic functions, we need to distinguish whether we are talking about our deductive, conceptual models or about the way we apply deduction and induction to the physical world, which is a necessary step for function to be realized physically. It is probably most fair to say that the functional entities of the mind have both conceptual and subconceptual aspects. Conceptually, they are bound to definitions and logical models that clearly delineate relationships, and subconceptually they are bound to a large pool of impressions formed from experience that qualify the kinds of circumstances the entities are likely to be useful. Once we have established a conceptual framework that is appropriately anchored to the world with subconceptual intuitions, we can reason with it deductively, quite abstracted from events in the physical world. We employ subconceptual and conceptual heuristics to keep our models aligned to physical-world circumstances to ensure our conclusions are relevant. For example, we consider how similar examples fared, and we look at how well each of our concepts fits (both on a subconceptual and conceptual level) to the situation at hand.

Let’s get back to our underlying question, “What is the essential character of a functional entity?,” by which we mean a dependent functional entity managed by an information management system. Most fundamentally, we know it has the character of employing information to achieve a function, and information is patterns in data that can be used to predict what will happen with better than even odds. But secondarily, and most critically to our calling it a function, a function must achieve a purpose. Functional things must have a telos, meaning a purpose or “final cause”. Aristotle rightly concluded that the purpose of an acorn is to grow into an oak tree. We now also know that the purpose of the oak tree is to create acorns, completing the cycle. Oak genes manage the information that propagates them forward in time by building trees to metabolize matter and energy and acorns to multiply. The trunk of the tree is tall and strong to provide a competitive edge over other plants. This and many other dependent functions are served by the oak organism. These purposes are intrinsic to the informational process that created the oak gene line. That genetic information confers many functional features to oak trees that have general-purpose utility across the range of challenges oaks have faced over time. Function or purpose is always general-purpose because function is a probabilistic enterprise. It is not about any one action, but about a general capability to do certain kinds of actions. Both the actual functions served (noumenally) and our understandings of them (phenomenally) only reflect likelihoods and have no ultimate meaning beyond that. However, although function is always general, that generality can become focused over a very small domain, which makes it very specific within that domain. For example, if we know a gene codes a certain protein, and we know a function that protein performs, we can say we understand the function of the gene. It doesn’t prove the function we found is the only function, but most proteins probably do serve one biological function, though this function might be used in a variety of ways. This is because proteins are broadly classified as fibrous, globular or membrane, which indicates their likely function. Fibrous proteins, like collagen, generally provide structural support. Globular proteins generally engage in chemical reactions like catalyzing, transporting, and regulating. Membrane proteins are embedded or attach to membranes, typically to serve as receptors. A protein that doesn’t benefit the organism will quickly be lost because its gene won’t be preserved if it mutates. My point is that biological functions do become very specific, even though viewed closer they are necessarily still general.

Living things are principally functional entities for which their bodies are the physical manifestations. Although DNA is physical and the proteins it codes are physical, their functions are not physical; functions are nonphysical strategies organisms use to survive well. The functions are produced and preserved by genes, which either encode functional proteins or control proteins. Traditionally, control genes were thought to be limited to regulating gene expression, which, akin to the executive branch of government, enforces a fixed set of regulating rules to increase or decrease the production of functional proteins. More recent thinking suggests they may also create new genes for future generations, akin to a legislative branch. Natural selection is the judicial branch that evaluates how well the other branches do. Both functional and control proteins will typically perform one chemical activity which typically reveals the gene’s primary purpose. Functional proteins are either structural building blocks like collagen or intermediaries that facilitate other chemical reactions like enzymes. We have identified control proteins that modulate many steps of gene expression, and we are inclined to consider such an identified control function as the gene’s primary purpose. We can’t rule out secondary or tertiary useful chemical activities for any protein, and until we discover such additional functions, we can’t be sure we know all the purposes of the gene. Much more significantly, control proteins form highly interdependent regulatory networks, which creates a combinatorial explosion of possible functions or functional effects from each gene. Functions can still be identified, but as the models become more complex and interdependent, our grasp or degree of certainty over all the implications necessarily goes down. But an understanding is always possible, forming a link between physical mechanisms and functional consequences, even though its predictive range is limited by our knowledge of the physical and functional phenomena and the models we constructed to join them.

Consider locomotion, a strategy animals commonly need. Locomotion is complex enough that it can’t be explained simply by studying genes and proteins. Yes, the proteins that underlie the mechanics of movement can be explained, but the reason certain ways of moving evolve and others don’t depend on macro-level functional forces, that is, the strategic value of moving. A paramecium is covered with simple cilia, hairlike organelles which act like tiny oars to move the organism in one direction.1 A model to explain how and why the paramecium moves the way it does could be well-informed by knowledge of every gene and protein involved and knowledge of the whole life cycle, but it would still be approximate because no model can capture all the interdependent effects that created its genome. Those effects selected for movement capabilities that worked well across the range of situations paramecia have encountered. Evolution essentially operated directly on the movement strategy or function for which the underlying mechanisms were just carriers. While any one feedback event is physical, its impact is to modify the mechanism in ways that only make sense in terms of their impact on generalized outcomes (functions). This has the effect of creating organizational structures out of physical matter that capture and use feedback loops in increasingly elaborate ways to produce ever more functional outcomes. Perhaps you can accept that feedback creates increasingly complex structures by providing a self-referential way of building on complexity, but don’t see it as being purposeful. But the way we see purposeful as being goal-oriented is anthropocentric. Really, purposeful means using information in ways that are likely to produce results similar to results seen before. One never actually reaches a “goal”, one only achieves a state that is similar to a goal. So purposeful really just means using heuristics with the expectation that they will tend to help. So I define function and purpose in terms of a process that uses information to predict and achieve useful outcomes, and whatever solutions result constitute the noumenal function. The ways we understand the noumenal function are phenomenal, both because we only see it through its phenomena and because understanding itself is a phenomenon. To develop a conceptual understanding, we group components into categories called kinds and we ascribe categories of behavior called causes and effects to these kinds of similar components and behaviors. It is all just a grand approximation, but reasoning this way using concepts is frequently so accurate that we tend to forget that it is just educated guesswork. We come to think our logical interpretation of things is simply true: legs were designed for walking, eyes were designed for seeing. But they were actually only designed to give us a good chance of walking or seeing, and doing a few other things. Walking and seeing are concepts or kinds that groups behaviors. Legs also help us stand and eyes help others recognize us. Additional or alternate categorizations always remain. The important point here is that functions exist, and our understanding of them through their phenomena reveals them to varying degrees.

Evolution would be impressive enough if it could only fashion organisms that accomplished everything through innate skills, i.e. using static, instinctive functions. But evolution promotes competition, so if ways to outperform instinct were possible, then they would probably evolve. Innate behavior has had millions of years to evolve, so it has been fine-tuned to cover all the routine challenges organisms face (if one takes routine to mean covered by instinct). But animals face many non-routine challenges because they move about a continually changing landscape and must compete with other animals that are always evolving more adaptive strategies. Instinctive strategies are quick and dependable, but are not as flexible as strategies customized to the circumstances. Some animals started to collect real-time feedback to make on-the-fly decisions based on experience rather than instinct. Learning is this capacity to dynamically acquire, assess, and act on patterns rather than waiting many generations for that feedback to be incorporated into DNA. Also, while some very elaborate behaviors are instinctive in many animals, this approach puts limits on their ability to adapt to new situations. Learning gives an animal a way to develop a dynamic store of customized strategies during a single lifetime. Where DNA gives a common set of abilities to all individuals in a species, learning gives each individual the ability to develop abilities to handle the novel circumstances it encounters. Each learned function evolves from feedback provided by cognitive assessment of the function’s success, but this feedback can generalize so well that even a single experience can teach a lesson that can improve predictive power. But more experience helps: “practice makes perfect”. Learning is dynamic, but the capacity to learn is genetic. The benefits for learning are so great that probably all plants and animals have some capacity for it (more on this later), but learning is mostly managed by brains, with higher animals (birds and mammals) showing substantially more capacity to learn. The conventional wisdom says that all organisms are equally evolved, having had the same amount of time, but have simply gone down different paths. By this view, we are no “better” or “worse” than amoebas. While it is true that all organisms are highly evolved, having had plenty of time to develop a near perfect fit to their environment, it is not true that evolution is equal or directionless. First, the rate of evolution becomes very slow sometimes, seeming even stopping based on the fossil record. And second, as a consequence of the first point, some species develop more functional capacities than others. Although it is premature to make too many specific conclusions at this stage because we are only beginning to understand the functions of the genes, we can safely conclude that brains open the door to a range of functional capacities that plants don’t have, that higher animals enjoy a range of cognitive flexibility beyond that of lower animals, and that humans have a uniquely generalized degree of cognitive flexibility.

Function in Concepts

My goal is to explain how consciousness and intelligence work in our minds, but explanations are products of minds themselves, so I must, to some degree, use the solution to explain itself. I have been doing that, and I will continue to do it, but I will also keep circling back to fill in holes. Let’s take a closer look at concepts to see how we can use them to understand functional entities better. As I noted above, concepts carry more explanatory power than feelings and subconcepts because they are well-organized, they form causal chains of reasoning, and they are not inherently subjective. Let’s take a look at some concepts to get a better idea of how they work. The concept APPLE (capitalization means we are referring to apple as a concept) refers to the generic idea of an apple and not to any specific apple. APPLE is not about the word “apple” or thinks like or analogous to apples, but about actual apples that meet our standard for being sufficiently apple-like to fall within our internal definition of the concept. We each arrive at our understanding of what APPLE means from our experience with apples. Even though we each have distinct apple experiences, our concept of what APPLE means is functionally the same for most purposes. How can this be? APPLE is generalized from all the objects we encounter which we learn are called apples. For example, we may come to know that the things we call apples are the fruit of the apple tree, are typically red, yellow or green, are about the size of a fist, have a core that should not be eaten, and/or are sliced up and baked into apple pies. Although each of us has an entirely unique set of interactions with apples, our functional understanding, namely that they are white-fleshed fruits of a certain size eaten in certain ways, holds for nearly all our experiences with apples. Some of us may think of them as sweet and others as sour or tart, but the functional interactions commonly associated with apples are about the same. That these interactions center around eating them is clearly an anthropomorphic perspective, and yet that perspective is generally what matters to us, and anyway, fruits appear to have evolved expressly to appeal to animal appetites, lending further credence to this notion of their function. Most of us realize apples come in different varieties, but none of us have seen them all (about 7500 cultivars), so we allow for variations within the concept. Some of us may know that apples are defined to be the fruit of a single species of tree, Malus pumila, and some may not, but this has little impact on most functional uses. The person who thinks that pears or apple pears are also apples is quite mistaken relative to the broadly accepted standard, but their overly generalized concept still overlaps with the “correct” one and probably serves their needs well enough. One can endlessly debate the exact standard for any concept, but exactness is immaterial in most cases because only certain general features are usually relevant to the functions that typically come under consideration. The fact that a given word associates with a given concept in a given context, and that our conception of the concept is functionally equivalent for most intents and purposes, makes communication possible. Many words have multiple definitions and each can fairly be called a distinct concept. We each develop temporary or permanent concepts for many things through our experience, but only common concepts are named with words or phrases, so until this happens, or some other cultural reference embodies the concept, we can’t share these concepts unless we explain them. A wax apple is not an APPLE, but we will generally call a look-alike by the same name if it is understood to be an imitation. Borrowing a word in this way often leads to the creation of additional definitions for the word. It is ok for a word to have many definitions provided the context reveals the definition of interest, e.g. mouse is usually fine as a short form of computer mouse.

If we look closer at our concepts of physical things, we start to see how much they are colored by functional distinctions. For starters, all man-made artifacts are fashioned the way they are to serve a purpose, and so while they have a physical composition, their function is always paramount in our minds. Other living things have their own innate goals, but we also view them and the physical world as natural resources we can turn to our own purposes. This isn’t surprising; the role of concepts is to increase our capacity for functional interactions with the world. Even the purest theories of physics, aside from not being physical themselves, defines physical spacetime from the perspective of what we can predict about it, which is the definition of functional, not physical. So all our concepts are not just colored by functional distinctions but actually are functional distinctions, whether they are about physical things or functional things.

About half the concepts we hold are about physical things, but that leaves half that are about functional things. Prototypically, nouns are physical forms and verbs, adjectives, etc., are functions. It is not quite that simple. Nouns that are strictly physical are called concrete; functional nouns are called abstract nouns. Many verbs, adjectives, etc., are concrete as well, e.g. TO ORBIT and TO RAIN or FASTER/SLOWER if they refer to purely physical processes. Most abstract concepts in verb form also have noun forms, like the HUNT or the TALK, or, using gerunds, the HUNTING or the TALKING. Noun-forming suffixes change DECIDE, COMPLETE, and MANAGE into DECISION, COMPLETION, and MANAGEMENT. Adjectives and adverbs let us characterize concepts by attaching a single trait to a noun or verb respectively that varies along an implied scale, e.g. BAD/GOOD, BIG/SMALL, SHARP/DULL, IMPORTANT/INSIGNIFICANT, GENTLY/ROUGHLY that puts the concept in relation to other concepts. We can discuss adjectives using nouns via the noun-forming suffix “-ness”, e.g. GOODNESS. Prepositions juxtapose concepts with each other with a relationship, like WITH, FROM, and INTO. We tend not to characterize prepositional relationships using nouns unless they have some permanence, like MARRIAGE, CANADIAN, and AUDIOPHILE.

Parts of speech aside, then, what is the range of things for which we have functional concepts? First are the elements and rules of formal systems, i.e. logic and math. Numbers, subjects, objects, propositions, and rules for counting or deducing are all functional. They are completely abstracted from biological functions, but they still fall within the definition of function because they relate to capabilities for using information to achieve purposes. The purposes are implications within the systems, but, because of the uniformity of nature, math and logic are used as foundations of the physical sciences, allowing us to apply formal systems to the physical world to understand and control them better than we otherwise could.

The remaining functional concepts are all biological. As I described in the prior chapter, organisms capture information at four levels: genetic, instinctive (senses, drives, and emotions), subconceptual, and conceptual. While we can discuss genetic functions, we can’t feel them because they are not part of conscious awareness, but the other three levels are. SIGHT, HUNGER, and JOY are examples of concepts with instinctive support, and HUNCH and PREMONITION have subconceptual support. Our conceptual grasp of our instincts and subconcepts lets us talk about them and include them in our logical reasoning. We also have abstract concepts about concepts, e.g. THOUGHT, IMAGINATION, PLAN, and DECISION. Many abstract concepts express actions that are strategic or functional, e.g. CATCH, JUDGE, STEAL, LAUGHTER. Others express functional states, e.g. CHAOS, DEFEAT, FREEDOM, TRUTH, WEALTH. Others are qualities, either in people or behaviors, like BEAUTY, COMPASSION, and CURIOSITY. Some are high-level generalizations or actions, states, or qualities, like ADVENTURE, CRIME, LUCK. And others are schools of thought encompassing formal or informal models, like CULTURE, DEMOCRACY, EDUCATION, HISTORY, MUSIC, RELIGION, SCIENCE. There are no exact boundaries between the different kinds of abstractions.

Function in the Sciences

Function has been part of language and thought from the beginning, but it hasn’t been overlooked in science, either, even if science has not yet granted it existential status. Viewed most abstractly, science divides into two branches, the formal and experimental sciences. Formal science is entirely theoretical but provides mathematical and logical tools for the experimental sciences, which study the physical world using a combination of hypotheses and testing. Testing gathers evidence from observations and correlates it with hypotheses to support or refute them. Superficially, the formal sciences are all creativity while the experimental sciences are all discovery, but in practice, most formal sciences need to provide some real-world value, and most experimental sciences require creative hypotheses, which are themselves wholly formal. Experimental science further divides into fundamental physics, which studies irreducible fields and/or particles, and the special sciences (all other natural and social sciences), which conceive aggregate properties of spacetime which are presumed by materialists to be reducible in principle to fundamental physics. Experimental science is studied using the scientific method, which is a loop in which one proposes a hypothesis, then tests it, and then refines and tests it again ad infinitum.

Alternately, we can separate the sciences based on whether they study form or function. Physical forms are more objectively observable, given that we can use instruments, but as noted above we can claim to observe functional entities through reflections and considerations of their functional claims. Consider the formal sciences, which establish functional entities through premises and rules from which one draws implications (which are functional). The formal sciences include logic, mathematics, statistics, theoretical computer science, information theory, game theory, systems theory, decision theory, and theoretical linguistics. They are named after formal systems, in which “form” means well-defined or having a well-specified nature, scope or meaning. However, that definition of form actually means function, because definition, specification, and meaning are functional. I will restrict my use of “form” to physical substance, in which form means a noumenon that can only be known through observed phenomena (though that knowledge itself is functional). While our knowledge of physical things must be mediated through phenomena, we know many functional noumena directly because we create them via definitions and logic. We don’t always immediately know the logical implications of a given formal system, but we can deduce proofs that demonstrate logical necessities which further unveil the underlying functional noumena. We can study formal systems inductively by running simulations that test more complex implications than we can deduce from the rules. Some formal sciences (e.g. weather modeling) are arguably more experimental than formal because they depend so much on inductive simulations. The implications of a formal system are either provable deductively or likely inductively. But what is the correct foundation of formal systems, e.g. the right foundation of mathematics. In Mathematics Form and Function, Saunders MacLane proposed six possible foundations: Logicism, Set Theory, Platonism, Formalism, Intuitionism, and Empiricism. The correct answer is that formal systems are not about correct or incorrect; they lay out assumptions and rules, which can be arbitrary, and work out implications from them. Their function or utility depends on how effective they are at solving problems. So any set of assumptions and rules make a good foundation, but some will be more effective than others. The most effective tend to be those which maximize simplicity, consistency, and applicability. In practice, set theory delivered these best and so most of mathematics now sits on a set-theoretic foundation, and on ZFC (Zermelo–Fraenkel set theory) in particular. Other set theories and systems from logicism and formalism can make good foundations for specific purposes. Platonism, Intuitionism and Empiricism are weak foundations for formal systems because they lack clear assumptions and rules.

While the experimental sciences, being physical, life, social, and applied science, are concerned with physical form, they are more focused on function. The physical sciences study form alone, specifically physical forms in our universe. However, formal models constitute the theoretical framework of the physical sciences, so its explanations are functional constructs. The life sciences principally study function, specifically functions that result from the theory of evolution. Of course, lifeforms have physical bodies as well, which must also be studied, but nearly all understanding of life derives from function and not form, so looking at the form is basically only helpful in furthering the understanding of function. The distinctly identifiable functions that living things perform gradually evolve through complex, interwoven, and layered feedback that gradually causes physical mechanisms that further functional goals to “emerge”. Functional existence leverages physical existence, and depends on it to continue to exist physically, but is not the same as physical existence. The social sciences also focus on function, specifically functions produced by the mind, despite the lack of a theory that explains the mind itself. Nearly all the understandings of the social sciences were built by observing patterns in human behavior. Behavior is a complex consequence of how minds function. Finally, applied science is principally concerned with functions that help us live better, but uses both form and function to develop technologies to do so. All the experimental sciences depend heavily on the formal sciences for mathematical and logical rigor. To summarize, understanding physical forms is central to physical science but is only incidental to the other experimental sciences and is irrelevant to the formal sciences. Understanding function, however, is central to the formal, life, social, and applied sciences, but it also underlies the theories of the physical sciences. After all, the reason we try to understand physical forms is to gain functional mastery over them (via laws of physics).

Function in Non-living Things

Experimental science has had its greatest success in the physical sciences because (a) nature is very uniform, (b) we can measure it with impartial instruments of great accuracy and precision, and, most importantly, (c) relatively simple theories can predict what will happen. That nature is uniform and measurable is, thankfully, a given, but the last point is a subtle consequence of conceptual thinking. I mentioned above that all concepts are generalizations. A generalization is an identification of patterns shared by two or more things. Categorizing them as the same kind of thing lets us make predictions based on our knowledge of that kind. But there is a hidden challenge: every pattern has many features, and the number of combinations of features grows exponentially, resulting in an almost infinite number of kinds. How do we avoid a runaway proliferation of categories? The answer is Occam’s razor, which is both a deeply seated innate skill and a rule of thumb we can apply consciously. Occam’s razor says that “when different models of varying complexity can account for the data equally well, the simplest one should be selected”2. Or, as Thales of Miletus originally put it, “maximum of phenomena should be explained by a minimum of hypotheses”. Simplicity trumps complexity for two reasons. First, the universe seems to follow a small set of fixed laws. While we can never know what they are with complete certainty, we can approximate them most effectively by trying to build as small a consistent set of laws as we can. And second, as the complexity of a pattern increases, it becomes less general and so works in fewer situations. Complexity is great for specific situations but bad for general situations. So again, to build a small but consistent set of laws, we should simplify wherever possible.

Consider an example. Theories to explain the motions of celestial bodies across the sky have been refined numerous times with the help of Occam’s razor. Ancient astronomers attached moving celestial bodies to geocentric (Earth at the center) celestial spheres. Ptolemy then explained the apparent retrograde motion of Mars as seen from Earth by saying a point on Mars’ sphere revolves around the Earth, and Mars revolves around that point along a smaller circle called an epicycle. (Note that additional epicycles were used to make small corrections for what were later learned to be elliptical orbits.) Copernicus realized that by using heliocentric (Sun at the center) celestial spheres, the epicycles used by Mars and the other planets to explain their retrograde motion would be unnecessary because this illusion is just a side effect of looking from one planet to another in a heliocentric model. Fewer epicycles were simpler to calculate and also made the model simpler to understand. Kepler’s discovery that orbits were ellipses replaced the celestial spheres with celestial ellipsoids and eliminated all the epicycles, again simplifying calculations and comprehension. Newton eliminated the need for celestial ellipsoids by introducing the universal law of gravitation, which both reduced all matter interactions to one equation but also explained gravity on Earth. However, Newton’s gravity depended on action at a distance, which seemed supernatural to Newton, who felt that interactions should require direct contact. The influence of gravity creates an exception to the rule that objects in motion move in a straight line. Einstein’s theory of general relativity eliminated that exception, allowing planets and all things to travel in a straight line once again, but now through curved space. And we still have a ways to go to reach a deeper and unified understanding spacetime, gravity and matter, which I will revisit later. Suffice to say for now that Newton and Einstein contributed to the general thrust of modern physical science to find formal models with predictive power rather than to look for an actual mechanism of the universe. But the success of this approach speaks for itself; physical science can make nearly perfect predictions about many of the physical phenomena we have identified.

Function in Living Things

But life is far less tractable. Unlike non-living physical systems, whose complexity roughly remains constant, living things increase in complexity with each moment of life because the consequences of living are captured and incorporated through feedback. The amount of feedback is staggering, a constant onslaught of information. For example, every step an ant takes provides feedback about the effectiveness of its means of locomotion. The feedback is very indirect; more effective ant gene lines will outcompete less effective ones. Every beneficial trait helps a bit, but it would be hard to say how much. The gene line doesn’t evolve in isolation, but in conjunction with all the gene lines in its niche and ultimately with the whole biosphere. It is helpful to study traits in isolation and to look at survival at the level of the individual, but the feedback loops of evolution create information management systems that protect the long-term needs of the gene lines, whose interests are more complex than those of any one gene or body. Those systems have been refined over billions of years and we can safely say that by now they leave nothing to chance, except, somewhat ironically, that which is mathematically safest in the hands of chance. We know that chance plays a role in which sperm makes it to the egg, but, more significantly, we need to realize that it plays the absolute minimal role possible. Every bit of feedback the gene line has ever collected is funneled through the sexual reproduction process to create the offspring with the greatest chance of providing greater adaptive advantages than the parent had. The view that the gene line would simply perpetuate itself and leave beneficial mutations to random chance, as the Modern Synthesis, also called Neo-Darwinism, proposed, is unrealistic. Suppose our gene lines were smart enough to alter or build new genes using educated guesses and to test them out using individuals as lab rats. Would such an approach be worth it? New evidence suggests it not only would be worth it but that exactly such a genetic arms race created this kind of gene-building technology before multicellular life even arose and that it was necessary to propel life to the levels of complexity and functionality it enjoys today.3

Although natural selection is the only force at work in evolution, that doesn’t mean it must depend on random point mutations in genes to move life forward. We would never get life as we know it, and not just because it would take trillions of years, but because there is no way to form the complex genes we have through an unbroken chain of viable descendants using only point mutations. If you hope to see a monkey type out the works of Shakespeare, don’t sit him at a typewriter, sit him at a phrase-writer. If he starts with all of Shakespeare’s greatest hit phrases, it no longer seems like such a long shot that he might come up with something good. This is how genes are really built: our existing genes and all the supposed “junk” DNA comprising 80% of our genomes are really raw materials used by a genetic phrase-writer that tweaks and builds genes. Mutations are not random events but are triggered by natural selection stresses. And that’s not all: just as the best phrases (e.g. transposable elements or TE’s), are kept handy, so are the most useful ways of employing them part of the long-term know-how of the genes in charge of gene construction. This is not science fiction; we have all sorts of gene editing enzymes that can do real-time gene editing to prepare some genes for use. And much of our supposed “junk” DNA has TE’s and even fully-formed or nearly fully-formed genes that have no apparent function. Though we have not yet found genes that direct the building of other genes, the biochemistry to do it is readily available. An opportunity arises to build and rearrange genes for the next generation at the moment of sexual reproduction, and mother nature would be foolish to simply roll the dice at this moment and let the chips fall where they may. It makes more sense to suppose that natural selection is also selecting mechanisms that can build the kinds of genes that are most likely to help. In times of low environmental stress, such built-in mutation mechanisms can slow way down or stop. Times of high stress should trigger more gene editing focused especially on the functional areas where deficiencies are detected. Such directed mutation to start effecting useful changes in a single generation for commonly needed adaptations, though it is more likely that most changes will take many generations to complete. Sometimes millions of years might pass before a fully-functional de novo gene just appeared and joined the other functioning genes in the genome. If this patient approach were the most efficient way to effect change, it would definitely outcompete less effective ways. It also explains sex, the differences between the sexes, and its near ubiquity in nature. Females and male, yin and yang, represent complementary forces in the evolutionary arms race, with yin representing stability and yang representing change. The egg is a large, stable investment that minimizes risk. The sperm is a small, more mutated investment that must prove its changes against millions of competitors. If sperm were as genetically stable as eggs, this extra step would not be necessary. But mutations aren’t random: they must appear in sperm either exclusively or with much greater frequency than in eggs, and the swim-off competition is their initial viability test. Miscarriage is a judgment call that is made to cut losses on an embryo with low viability. Then, finally, the individual has his or her chance to survive and propagate. Consistent with their large investment in a stable egg, females are more likely to invest more in having and raising offspring. Not similarly equipped (or burdened), males are more expendable and are more inclined to mate as often as possible. However, since reproduction requires one male and one female, the ESS (evolutionarily stable strategy) is for males and females to be produced in the same numbers. If male births became less common, it would become advantageous to produce more males as it would lead to more descendants.

Carefully recombined, altered and constructed genes are tested first in the sperm as primary lab rats, and then embryos are secondary and individuals are tertiary. The germline continues despite these casualties among the foot soldiers. Maximal adaptive benefit derives from a combination of experience and experimentation. The important point from the perspective of function is that the information captured by DNA is deeply multifunctional in a fractal, self-referential way, encompassing both the observed traits of the organism and its long-term adaptive potential. Adaptive benefits are served in both the short and long term. Many mechanisms have evolved, only some of which we yet recognize, to capture feedback so as to produce an adaptive advantage. All the traits and the distribution of traits we see in populations were selected because they conferred the greatest survival potential to the germline and not because of random beneficial mutations or genetic drift, which are random effects. Attributing genetic change to random effects is like saying card sharks win by luck. In both cases, luck exists very locally but hardly at all in aggregate because highly competitive strategies are in pal

Function in the Mind

All the functional capacity of the mind depends on the functional capacity of the brain, which, like the body, stores its blueprint in the genes. Still, it is a mistake to say that one can build life given those blueprints; life fundamentally depends on membranes, which are maintained by the genes but not constructed by them. The original cell membranes most likely arose as natural bubbles and were gradually co-opted and altered by living metabolisms. Life has no whole blueprint; you need a working copy and the genes. Together, the genes, the proteins coded by the genes, and the cells they maintain and propagate comprise both a physical machine and an information management system. Because the physical machine is designed based on adaptive feedback, it can only be understood by interpreting that feedback. We can’t see or figure out all the feedback, but we can look at adaptations and imagine reasons for them that relate to survival pressures. Reasoning teleologically this way, from purpose back to design, is the only way to make sense of what information management systems do. Understanding the physical mechanisms, too, is also necessary for a complete understanding, and also helps reveal details too mysterious to guess from observations of function alone.

All function in the physical world derives from living organisms, but information processing in life is not restricted to information captured in DNA. Most notably, animals use brains to manage and store information neurochemically. The functionality provided by the information managed in brains is quite different from genetic functionality because it is customized based on real-time experience. Where genetic assessment can only render an “opinion” once per lifetime, brains can make assessments on a continuous basis. These assessments create an information processing feedback loop that consists of inputs, processing/assessing, and outputs. Most metabolic control functions, such as heart rate, digestion, and breathing, can be done routinely with internal inputs and outputs, and so they require little if any integration with other control functions. But almost everything else bodies do requires top-level control because the body can only be in one place at a time. Since animals must engage in many activities, this means that their activities must be carefully prioritized and coordinated, and the solution to this problem that evolution arrived at is for brains to have a centralized top-level subsystem called the mind that performs this coordination. The distinction between the mind and the brain is arguably just one of perspective: the mind is consciously aware and only exists from the perspective of its own subjective awareness and agency, while the brain is an organ in the head that objectively exists. Most of the information processing in the brain is nonconscious and so not part of the mind (though I will use the term nonconscious mind to refer to it). It is hard to quantify the amount of processing that happens outside of our awareness, both because we have no way of measuring it and because awareness and processing could be defined in different ways, but I would put it at 95 to 99%. We are now aware of how our brains process vision into recognized objects, but we know it must take a lot of real-time processing. We are not aware of how our brains decide on an emotional reaction to a situation, but we know this, too, takes a lot of processing. And we don’t know how we understand or create language, but we know this is complicated as well.

Technically, we are each only sure that we alone have a mind, but we tend to accept that all people have minds because they say they do, their behavior meets our expectations, and we are all the same species and so probably work about the same way. But we also know people are very different, giving each person a very different perspective on life. Animals, of course, don’t claim to have minds, and we know that no other animal has even remotely our capacity for reasoning. But all animal behavior meets our expectation of how they would behave if they did have a mind; that is, they act as if they were aware. Also, all animals with central nervous systems are similar in that they collect sensory information, process it, and then act. So I am going to propose that all such animals have minds as subsystems of their brains that funnel decisions through special brain states called awareness, with the understanding that awareness is vastly different for different animals. We know that every animal has its own set of senses which, if we accept that they are conscious, produces a unique set of sensory feelings in the animal that make it “what it is like” to be that animal. Similarly, their bodies and their body sense and facility for controlling their bodies (experienced as agency) all contribute to that unique feeling as well. I don’t want to mitigate the enormity of these differences in inputs and outputs, but they are ultimately only peripheral to the central processing and assessing that the mind does. People without sight or hearing are handicapped relative to normals, but their general ability to think is unaffected. So my focus in understanding the mind is really on processing and assessing. I propose that all information in the mind falls into the following four categories. I number the first one zero because it has to do with inputs and outputs, which are peripheral, while the other three have to do with assessing, which is more central:

0. Percepts
1. Instincts
2. Subconcepts
3. Concepts

Though any one piece of information in the mind is strictly one of these four kinds, newly created information of one type often draws on information of the other types, resulting in a great deal of overlap that lets us leverage each kind of information to its strengths.

I am going to describe each of these four kinds of information, but first I am going to address the questions how and why. How does information in the genes become information in the brain, and why just these four kinds? As physical devices, the genes know nothing about minds and their needs, but as functional entities, they know exactly what needs to be done. What brains need to do can only be understood from a functional perspective: they need to control the body, and the more predictively they can deliver that control, the more successful they will be. So the genes have been selected to enable ever greater degrees of control. The genes, and in turn the proteins they encode, are selected not for their physical form but for what they do. Physically, this means the brain needs to be a computer with a body that provides inputs and outputs. Low-level functions in a computer must be hardcoded, meaning that they operate in a fixed way that does not change depending on circumstances. Nonconscious functions in the brain are hardcoded, and percepts and instincts (which are conscious) are too. Percepts provide hardcoded inputs and outputs, while instincts provide hardcoded assessments. Higher-level functions in a computer need memory, which is data that can change at runtime. In practice, this data catalogs patterns detected in the inputs. Hardcoded algorithms can leverage memory to produce subconcepts, which give us predictive capabilities based on fixed logic and memory, while softcoded algorithms can leverage memory to produce concepts, which are predictive capabilities based on variable or generalized logic and memory. In the mind, using fixed logic and memory is called reasoning, and using variable or generalized logic and memory is called logical reasoning. Reasoning manipulates percepts, instincts, and subconcepts while logical reasoning manipulates concepts, but this wording underplays their interactions, because most concepts are based on the other kinds of information, and concepts, once formed, change our experience and hence impact our subconcepts. Finally, note that some percepts and instincts use memory in a number of ways to develop or train their “fixed” responses. Some percepts and instincts use memory entirely nonconsciously, while others initially require conscious guidance to become habituated. With practice, we can, in principle, recover some measure of conscious control over any nonconscious process that uses our memory because we can consciously change our memory and retrain habits. So, to summarize, genes were selected to build brains because brains could provide a sufficiently generalized framework for centralized control and memory, i.e. a computer. Brains can deliver better-centralized control with a subsystem like the mind that can leverage percepts, instincts, subconcepts, and concepts to deliver focused, real-time operation. While all animal brains use minds to achieve coordination, no other animal minds have evolved the applications of concepts as far as humans have.

Percepts. Percepts are the sensory feelings that flow into our minds continuously from our senses. Their purpose is to connect the mind to the outside world via inputs and outputs. The five classic human senses are sight, hearing, taste, smell, and touch. Sight combines senses for color, brightness, and depth to create composite percepts for objects and movement. Smell combines over 1000 independent smell senses. Taste is based on five underlying taste senses (sweet, sour, salty, bitter, and umami). Hearing combines senses for pitch, volume, and other dimensions. And touch combines senses for pressure, temperature, and pain. Beyond these five we have a number of somatic senses that monitor internal body state, including balance (equilibrioception), proprioception (limb awareness), vibration sense, velocity sense, time sense (chronoception), hunger and thirst, erogenous sensation, chemoreception (e.g. salt, carbon dioxide or oxygen levels in blood), and a few more4. We also have a sense of certain internal mental states that feel like feedback from the mind itself as opposed to the outside world or the body, including our spatial sense, awareness itself, and attention. They are sensory in that they provide continual input and we can assess them, just as if they were external to the mind. We feel all of these percepts without having to reflect on them; they are immediate and hard-wired via nonconscious mechanisms that bring them into our conscious awareness without any conscious effort. Percepts find patterns in input data and send signals that represent them to our conscious minds without any need for past experience with the perceived items. We can perceive green innately even without any past experience.

Instincts. Instincts are genetically-based control functions. Their purpose is to provide as much innate control to all members of a species as evolution can muster. For example, beavers that have never seen a dam can still build one, so we know that this behavior is instinctive.5 We don’t yet know which genes create instincts, and one instinct likely depends on many genes, but we know instincts are genetic because any member of the species can perform an instinctive function when the behavior is triggered. Spider webs seem pretty creative, but we know they are created instinctively. A group of deaf Nicaraguan children taught no language quickly created one6, demonstrating that language acquisition is instinctive in humans. It appears that the genes can encode arbitrarily complex behaviors, and in the lower animals nearly every behavior seems to be instinctive. I propose, but can’t prove, that instincts influence behavior in all animals by consciously creating an internal “preference” or “nudge” with a relative strength. Strong nudges outweigh weaker nudges, allowing the mind to prioritize competing instincts to select one action for the body at a time. Complex instinctive behaviors can thus be interrupted by more pressing needs and then resumed. What would one of these nudges feel like? If it’s like being elbowed or told to do something, we would probably just ignore it. Instead, we feel instinctive nudges in two ways, as drives or emotions. We feel drives based only on our metabolic state, for example when we are hungry or tired. We feel emotions based on our psychological state, which is a nonconscious assessment of our subconcepts and concepts. Because emotions are computed beneath our awareness and can see what we really think, we can’t control them directly. However, since anything can look better or worse when viewed differently, emotions can be controlled indirectly by shifting perspective.

I will use the word feelings to describe both percepts and instincts. Percepts are sensory feelings and instincts are internally-generated nonsensory feelings.

Subconcepts. Animals store patterns from specific experiences in their memory, and over time they will notice associations between the patterns. These associations are what I call subconcepts. The purpose of subconcepts is to give animals insight from patterns in data. For example, all animals must move to a food source to eat, and they will start to notice different common features linking places where food can be found and places where it can’t. One can’t describe the meaning of subconcepts; they are just patterns can help predict other patterns. Co-occurring patterns are reinforced as subconcepts the more often they happen. One can’t put one’s finger on any one subconcept; all the associations from all of our experience form a large pool of subconcepts with useful relationship information which we can draw on. Subconcepts carry impressions without implying a logical connection or cause and effect. Subconcepts give us our feeling of familiarity. Subconceptual impressions about what will probably happen are called intuitions or hunches. We can trust our intuition to guide us through many of our actions without resorting to the next level of thinking, conceptual analysis. Subconcepts are analogous to machine learning. Machine learning algorithms recognize voices and drive cars using associative neural nets. Instead of logically reasoning out what was said or what to do they are essentially looking up the answer with pattern matching. This works great provided you have enough experience but falters in novel situations.

Concepts. Concepts turn associative memory into building blocks by adding labeling. Most generally, a concept is a bucket of associations to which one can refer as a unit, which makes it possible to build relationships between buckets in a logical chain of reasoning. We often use word or phrases to references a concept, but language is a layer on top of concepts that only covers a small fraction of all the concepts in our minds. We can think about things without using language, for example spatially or abstractly using any mental model we devise. When we think conceptually, the concepts are mental placeholders which we manipulate with logical operations. We typically use concepts without explicitly defining them, but that doesn’t mean they don’t have definitions. We form a concept whenever we suspect that a pattern of associations we see would most usefully be managed as a group. Multiple exposures to that pattern over time iteratively imply appropriate boundaries for what falls within the concept and what does not. Some of the contained associations are subconcepts and some concepts, but either way, they themselves have only implied boundaries. No matter how precisely we define a concept it always retains an inherent vagueness because concepts are buckets that collect similar items, and similarity is always a general grouping based on the vagaries of other subconcepts and concepts and so is never perfectly precise. The definition that matters most is the noumenal definition, which is the actual way the associations are hooked up in our brains. Dictionaries provide phenomenal definitions, which attempt to characterize those associations concisely using words. But no representational definition can match the complexity of the network underlying the real concept; it can only take one or more perspectives on the concept and describe it in terms of other concepts which must themselves be defined in an infinite regress. And even if we forgive that shortcoming, every concept can have different degrees of generality or detail or emphasis depending on the context in which we use it. We conceive a localized connotation of each concept relative to the mental model in which we employ it. This is a critical point because it means that concepts and words can’t really be defined independently of the mental models in which we use them. All concepts evolve to keep up with culture. While APPLE has not changed much since apple trees were first cultivated, except in the number of varieties and uses, TECHNOLOGY and FREEDOM mean quite different things now than in the past.

Instances. Now is a good time to introduce a special kind of concept, the instance. We generally exclude instances from the definition of the word concept, which is reserved for general notions, but instances work the same way in the mind except for one crucial detail: they denote a single noumenal entity, not a class of entities. Usually, an instance is a physical entity, such as a specific apple, person, place, or event. But functional instances exist as well, such as a hypothetical specific apple, person, place, or event, or a creative work such as a book or song. In English, we call concepts common nouns and write them in lower case, and we call instances proper nouns and capitalize them. All words that are not nouns are concepts because they have general applicability. Mentally, an instance is a refinement of a concept because it has all the normal associations of a concept plus a link to a unique entity. If we drop that link to a unique entity, it becomes a concept for which the unique entity is an example of the class. Our knowledge of many instances is detailed enough that only one entity would fit the class, but if we have a quarter, usually the only thing distinguishing that quarter from any other in our minds is our memory of where that quarter has been. This is why an instance is mentally just a special case of a concept.

In the most extreme sense, every pattern we detect is unique and is thus its own instance. But in practice, we use a continuous feedback cycle to confirm that phenomenal patterns align to noumenal entities. We can’t prove the existence of physical noumena, we can only keep empirically testing for them, but the evidence is so overwhelming if we get several confirming patterns that we rarely doubt they exist. In any case, it doesn’t matter if our understanding of the world is right in an absolute sense so long as things work the way we expect. Understanding is really a functional expectation. Further, we can say that, beyond a single moment, physical things exist over a span of time called their lifetime, during which they either don’t change or change in ways that fall within the scope of their conceptual meaning. This means we can distinguish two kinds of instances, those that are time-specific (events), and those that are time-invariant (items during their lifetime). We consequently each have an episodic memory of event instances and a time-invariant memory of item instances.

Arguably, animals would be most competitive if they had instincts for all the most adaptive behaviors they needed. And most animals, most of the time, do depend on instincts to lead them to the strategy they need. But the strategic arms race to maximize effectiveness opened the door for techniques to evolve that could figure out strategies customized to specific situations. Only strategies with general-purpose value in many situations can evolve, and although that can cover a wide range of behaviors given enough time, special situations will always arise for which a customized strategy would work better. Subconcepts provide a first line of attack to gain information from experience by giving animals common sense. Internal memory lookups against their large pool of experience will just give them an intuitive sense of what to do without any further reflection. Subconcepts are much older and more pervasive in animal brains than concepts, which provide a second line of attack. The value of concepts derives from the second-order analysis of relationships between concepts. While the relationships between subconcepts are only impressions supported by the data, relationships between concepts are nominated to be “true”. The way this works is that a mental model is hypothesized that proposes a set of relationships between a group of concepts because the data suggests the relationships might always hold. Although we at no point know whether those relationships do always hold, within the model they do by definition. The model is then further tested and through iterative feedback it is refined to the point where we develop a level of confidence for which the model applies to the phenomena it describes. Internally, the model is true, but externally it is only true to the degree it is applicable. However, we iteratively develop a very high confidence level for being able to judge circumstances for which the model is applicable. The value of establishing a mental model this way is that we can develop the internal relationships to an arbitrarily formal degree, and such relationships carry the power of logical implication. Logic can provide perfect foreknowledge, which means conceptual thinking can predict the future where subconceptual thinking can only give us good hunches and instinct can only act by rote.

In practice, conceptual thinking can directly solve problems quickly. Subconceptual thinking serves up solutions suggested by past experience. It improves with experience, so one can just practice more as a way of indirectly solving problems, provided the problems one wants to solve are the sort that tend to follow from trial and error. Instinctive thinking provides solutions in a way analogous to subconceptual thinking, but the accumulated experience takes much longer to become encoded genetically. Subconceptual and instinctive thinking don’t reveal why they work, they only carry the feeling that they will work. But conceptual thinking says exactly why, though the reasons hinge on the model being well-constructed and well-applied. Not only do logical models specify exact relationships, but logic can also be leveraged arbitrarily with chained causal reasoning. Subconcepts and instincts can also form chains given enough time, but only if there is an incremental benefit from each step in the chain. While problem-solving with concepts is not an exact science, it has unlimited potential because there are no limits on the number of models we can propose. While no model is perfect, many models can be found and then refined to become increasingly helpful, which is a proactive and fast approach relative to the alternatives.

Internally, a model can be said to be perfectly objective because its implications are completely independent of who uses it, but externally models must be applied judiciously, which invariably involves some subjective judgments. Scientific modeling requires extra precautions to maximize objectivity and minimize subjectivity which I will discuss more later, but we are confident enough about most of the modeling we do to understand and interact with the world that we believe it would count as objective against most standards. We also all maintain a highly subjective view of the world which is quite idiosyncratic to our own experience and perspective, but still counts as conceptual modeling because we draw implications from it.

When we look at something and recognize it as an apple, the work is done for us nonconsciously. The nonconscious work requires many pattern comparisons beneath our awareness that cascade from percepts to subconcepts to concepts. We perceive red, round, and shiny and associate subconceptually to organic, plant, food, good, etc., not as named associations but as impressions. Helped by these associations, conceptual associations are triggered, including concepts for some of the percepts and subconcepts, and more discrete concepts like fruit and apple. Percepts give us informant, subconcepts give us familiarity, and concepts give us models and hence understanding. In this case, once we understand what we are looking at, instinct may trigger hunger or happiness.

This has been a quick overview of function in the abstract and how it presents in the mind. What I have said so far is generally supported by common knowledge, but my goal is to develop a rigorous scientific basis. To get there, I am going to have to review the philosophy of science and then overhaul it to be more appropriate to the study of the mind.

Natural and Artificial Information

I divided information management systems above into two classes: life in general, which stores information in DNA, and brains, which store it neurochemically. Let me now make a further distinction of natural and artificial information, where the former derives from DNA and the latter goes above and beyond DNA in a crucial sense. All information in DNA is natural, and most of the information stored in brains is natural as well, but some is artificial. Information that is created nonconsciously or through percepts or instincts are direct consequences of genetic mechanisms and so are natural. Information that is created consciously through subconceptual or conceptual thinking is artificial. Subconcepts are arguably a gray area because they are formed by nonconscious mechanisms, but I refer here to the conscious component of subconceptual thinking, which includes developing common sense and an intuitive feel for how things work. Sometimes intuition produces eureka moments, but mostly it just helps us integrate our knowledge in familiar and practical ways. Concepts are more obviously artificial. If we see a pattern and form a concept to represent it, that is a conscious decision. For example, when we are very young we notice that one often gets in and out of a room through a flap that swings open and closed and we form the concept DOOR for that flap. We categorize all the flaps we have seen that provide access through openings as examples of doors. Later we learn the word for it. Our concept DOOR is artificial because it was created by real-time data analysis and is not a direct byproduct of genetic information. Our initial eureka moment that doors were a thing that one encounters repeatedly that share common traits derives from subconceptual knowledge: all remember all the doors we have ever seen based on the salient, functional properties we noticed about them at the time. Our inclination to group them under the concept DOOR came entirely naturally to us, as the ability to form concepts is innate. But although we are designed to think this way, the creations of thought are not anticipated by the genes and so are artificial. Whether or not we have free will to form and use concepts as we like is a more subtle question which I will get to later.

Physically, every moment of our lives is completely unique, so from a physical standpoint we should have no idea what will happen next, and for that matter, we should not even have ideas. In theory, if someone (Laplace’s Demon) “knows the precise location and momentum of every atom in the universe, their past and future values for any given time are entailed; they can be calculated from the laws of classical mechanics.” We now know that quantum uncertainty that the world may run like a clock but still be unpredictable. Putting this aside, though, only the demon should be able to predict the future. But it turns out that we can predict the future of many things with near perfect accuracy, and have minds with ideas to do it. The broad reason is information management, but the narrow reason I am focusing on here is conceptual information management. We group the world into concepts, and the uniformity of nature allow those concepts to “reappear” many times in many ways, even though each instance was actually unique and is only related to the others functionally, not physically (because relations are always functional). So we look for patterns everywhere, and when we find one that seems to repeat in different contexts we automatically form a concept to group it. Most of our concepts have a very transient nature, but they group together into broader and broader categories and intersect with the concepts other people form through language. Each concept is continuously confirmed and revised by each instance we experience. And concepts work together in mental models to create logical frameworks that explain how the world works, which is the source of our more impressive predictive powers. Because we can reflect on our thoughts endlessly, we have unlimited potential to review and revise, and so can develop our concepts in new, unpredictable directions. Artificial information is chiefly characterized by logical frameworks that are continually revised through feedback loops.

While all advanced animals have some capacity to think subconceptually and conceptually, humans have a few strengths that let them leverage these capacities to produce accelerating change, an exponential growth caused by information. Exponential growth in nature usually results in an exhaustion of resources and a 99% die-off. Human civilization is quickly converging on such a catastrophe, but my point here is that accelerating change has been shaping human evolution for millions of years and civilization for thousands of years. Evolution improved our capacity for subconceptual and conceptual thinking, and then civilization helped us leverage artificial constructs functionally through knowledge and physically through artifacts. Artificial, meaning from an art or craft, is the conscious use of the imagination in a skillful way, which is to say functionally rather than randomly. While other advanced animals can use subconcepts and concepts, they can’t leverage them even remotely as well as we can to produce artifacts, though some may well develop significant knowledge.

The study of the mind includes its capacity to manage both natural and artificial information, but our stronger interest is not in the kinds of natural information we share with other animals but in the artificial information that make humans unique. We don’t yet understand a great deal about either, but natural information in inherently more tractable to study because its feedback loops are less abstract. Our percepts and instincts have fixed, identifiable functions and so probably mostly have relatively fixed, discoverable physical mechanisms. For example, we have a fair idea how visual processing works in humans, even though we don’t yet know how the images appear in our conscious mind or what our conscious minds physically are. Another example is hunger. The hormones leptin and ghrelin are produced by the body and their levels in the blood are read by receptors in the hypothalamus to produce the feeling of hunger. Emotions are produced entirely within the brain using feedback from subconcepts and concepts. The amygdala is a pair of almond-shaped clusters in the brain that are central to emotional response. While much is known, much more still is not, emotions involve a vastly more complex interplay of information feeding back on itself than hunger. Even so, the genetic basis of emotion can eventually be teased out, even if the way we deal with our emotions remains subject to many artificial influences.

What makes humans unique is our capacity to think in ways that generate artificial information, which constitutes much of what we think of as understanding. It is something of a lie, because artificial information, much like the related word artifice, consists solely of models that bias the interpretation of what is happening towards our purposes. Even when we purport that our purpose is to expose the truth, our real purpose is to gain functional advantage. But it’s not deceit; maximizing function advantage is effectively the real nature of truth. We can’t see physical noumenal truth directly, so we must settle for the most effective phenomenal approximations. So the question is, how do we generate artificial information? Our underlying capacity to think with subconcepts and concepts is entirely natural, provided by good genes. That natural ability generates artificial information, and that information includes new ways to think, some of which we dream up ourselves and some of which we learn from others. These new ways to think are give us a boosted capacity to think that is artificial, and our artificial capacity can be leveraged to make it more powerful in some ways than our native capacities. So the study of how we think needs to encompass both our natural talents and our learned methods.

Additionally, the study of how we think is itself necessarily an artificial informational construct, so I will have to devote some attention to the question of how to study the mind. What science studies how the mind works? The natural part is covered by the life sciences, which study natural information. Artificial information is studied by the social and applied sciences. The other sciences, including the physical and formal sciences, don’t study information (except that some formal sciences study abstract (non-living) information systems). Psychology, which is the study of the mind, is a life science to the extent it is concerned with innate mechanisms which are fixed and predictable, and it is a social science to the extent it is concerned with the effects of cultural or artificial feedback on the behavior of the mind. So psychology has sufficient scope to study the whole mind, making this work primarily a work of psychology. I have to note again here that an important new branch of psychology, cognitive science, was launched in the 1970’s to take the computational aspects of mind more seriously than the existing subfields of psychology were doing. This push came mostly from the artificial intelligence community, but included linguistics and neuroscience. Where psychology is largely seen as a soft science, cognitive science wanted more hard science credibility. In my terminology, hard and soft correspond to natural and artificial. Yes, it is easier to study natural information because the mechanisms managed by DNA, although complicated, are relatively static and tend to be shaped by long-term adaptive pressures which we have some hope of identifying. In other words, we can mostly figure it out given enough time. Artificial information, on the other hand, is arbitrarily more complicated, very dynamic, and is shaped by short-term pressures. Sciences that study artificial information are therefore intrinsically less exact. Many branches of psychology necessarily encompass both natural and artificial information, which arguably compromises their explanatory reach. But quite a few branches, including behavioral neuroscience, cognitive psychology, developmental psychology, and evolutionary psychology, explicitly exclude artificial information (perhaps we should refactor cognitive science back into the psychology department). Note that most of our talent for thinking with artificial information is itself natural. As we mature, we will naturally learn to think better, improving our ability to prioritize, broaden our perspective, and think logically or rationally. No academic field studies how we think or has anything to say about it; nor do we attempt to teach people how to think. We have cultivated rhetoric, the persuasive use of language, and we have some tips and tricks to spur creativity, like brainstorming, thinking outside the box, and improvisation, and some strategies for prioritization and organization, like the 80/20 rule, top-down, bottom-up, breadth-first, depth-first, and flow charting, but we don’t know how we think and so can’t say what might help us do it better. I would contend that we know more than we think we know about how we think; we just need to organize what we know from the top down to see where we stand.

A Functional Look at the History of the Philosophy of Science

I have made the case that it is reasonable to use the mind to study the mind and I have outlined how minds, and functional systems in general, are variable and open instead of fixed and closed. This has implications for how we should study them which I am going to consider now.

First, let’s take a closer look at how we study fixed systems to see what we can learn. I have noted that science stands by the scientific method as the best way to approach experimental science. In addition to the basic loop — observe, hypothesize, predict, and test — the method now attempts to control bias through peer review, preregistering research, and more, but needs to go further to ensure that scientific research is motivated only by the quest for knowledge and not corrupted by money and power. Still, the basic scientific method that iterates the steps as often as needed to improve the match between model (hypothesis) and reality (as observed) works pretty well. In general, feedback loops are the source of all information and function, but the scientific method aims for more than just information — it is after truth. Scientific truth is the quest for a single, formal model that accurately describes the salient aspects of reality. General-purpose information we gather from experience and access through memory uses a mixture of data and informal models; it is more like a big data approach that catalogs impressions and casual likelihoods. And when we do reason logically, we usually do it quite informally with a variety of approximate models. But science recognizes the extra value that rigorous models can provide. Although we can’t prove that a scientific model is correct because our knowledge of the physical world is limited to sampling, all particles of a given type do seem to behave identically, which makes near-perfect predictions in the physical sciences possible. While the exact laws of nature are still (and may always be) a bit too complex for us to nail down completely, the models we have devised so far work well enough that we can take them as true for all the intents and purposes to which they apply. We regard scientific theories as laws once their robustness has been demonstrated by an overwhelming amount of experimental evidence. If one of these laws seems to fail to work, we still won’t doubt the law but can safely conclude that physical reality didn’t live up to the model, meaning imperfections in the materials or our grasp of all the forces in play were to blame.

This pretty straightforward philosophy of science is sufficient for physical science. We accept a well-supported theory as completely true until an exception can be rigorously demonstrated, at which point we start to look for a theory that covers the exception. Scientific knowledge is not intended to be absolute but is meant to be contextual within the scope of situations the laws describe. This is a very workable approach and supports a lot of very effective technology. This approach is also serviceable for studying the functional sciences, but it can only take us so far. Using it, we can lay out any set of assumptions we like and then test theories based on them. If the theories hold up reasonably well, that means we can make somewhat reliable predictions, even if the assumptions have no foundation. This is how the social sciences are practiced, and while nobody would consider any conclusions of the social sciences to be definitive, we do assume that a reputable study should sway our conception of the truth. The shortcomings of science as practiced are still large enough that we know that we should doubt any one study, but we still hope that anything demonstrated by a preponderance of studies has some truth to it. But couldn’t we do better? The social sciences should not be built out of unsupported assumptions about human nature but from the firm foundation of a comprehensive theory of the mind. My objective here is to expand the philosophy of science to encompass the challenges of studying functional systems, and minds in particular.

I’m going to build this philosophy from first principles, but before I start, I’m going to quickly review the history of the philosophy of science. Not all philosophy is philosophy of science, but perhaps it should be, because philosophy that is not scientific is just art: pretty, but of dubious value.1 I’m going to discuss just a few key scientists and movements, first listing their contributions and then interpreting what they did from a functional stance.

Aristotle is commonly regarded as the father of Western philosophy, along with Plato and Socrates, whose tradition he inherited. Unlike them, Aristotle also extensively studied natural philosophy, which we have renamed science. Aristotle was an intuitive functionalist. He focused his efforts on distinctions that carried explanatory power, aka function, and from careful observations almost single-handedly discovered the uniformity of nature, which contrasted with the prevailing impression of an inherent variability of nature. Through many detailed biological studies, he established the importance of observation and the principle that the world followed knowable natural laws rather than unknowable supernatural ones at the whims of celestial spirits.

Francis Bacon outlined the scientific method in the <a href=”https://en.wikipedia.org/wiki/Novum_OrganumNovum Organum (1620) by emphasizing the value of performing experiments to support theories with evidence. Bacon intentionally expanded on Aristotle’s Organon with a prescriptive approach to science that insisted that only a strict scientific method would build a body of knowledge based on facts instead of conjectures. Controlled induction and experiments would accurately reveal the rules behind the uniformity of nature if one were careful to avoid generalizing beyond what the facts demonstrate. In practice, most scientists today adopt this attitude and don’t think too much about the caveats that arose in the coming centuries that I will get to next.

René Descartes established a clear role for judgment and reason in his Discourse on the Method of Rightly Conducting One’s Reason and of Seeking Truth in the Sciences (1637). His method had four parts: (a) trust your judgment, while avoiding biases, (b) subdivide problems into as many parts as possible, (c) start with the simplest and most certain knowledge and then build more complex knowledge, and (d) conduct general reviews to assure that nothing was omitted. Further, Descartes concluded, while thinking about his own thoughts, “that I, who was thinking them, had to be something; and observing this truth, I am thinking therefore I exist”2, which is known popularly as Cogito ergo sum or I think, therefore I am. He felt that whatever other doubts he might have about the world, this idea was so “secure and certain” that he “took this as the first principle of the philosophy I was seeking.” He further concluded that “I was a substance whose whole essence or nature resides only in thinking, and which, in order to exist, has no need of place and is not dependent on any material thing. Accordingly this ‘I’, that is to say the Soul by which I am what I am, is entirely distinct from the body and is even easier to know than the body; and would not stop being everything it is, even if the body were not to exist.”3 Descartes attempted a physical explanation based on the observation that most brain parts were duplicated in each hemisphere. He believed that since the pineal gland “is the only solid part in the whole brain which is single, it must necessarily be the seat of common sense, i.e., of thought, and consequently of the soul; for one cannot be separated from the other.”4 In this, he was quite mistaken, and it ultimately undermined his arguments, but it was a noble effort! Looking at Descartes functionally, he recognized the role our own minds play in scientific discovery and simply implored us to use good judgment. His assertion that some methods are more effective for science than others was a purely functional stance (because it does all come down to what is effective). He further recognized the preeminence of mind and reason, to the point of proposing substance dualism to resolve the mind-body problem, which I have reformulated into form and function dualism. Descartes was entirely correct in his cogito ergo sum statement, if we interpret it from a form and function dualism perspective. In this view, the function of our minds requires no place or time to exist but can be thought of as existing in the abstract by virtue of the information it represents. Although Descartes fascination with brain anatomy and assumption of the irreducibility of the soul (no doubt derived from a desire to align Catholicism with science) led to some unsupported and false conclusions, he was on the right track. The mind arises entirely from physical processes but is more than just physical itself, because information has a functional existence that transcends physical existence because it is referential and so can be detached from the physical. It is not that there is a “nonphysical” substance that is connected to the physical brain, it is that function is a different kind of thing than form. Physical mechanisms leverage feedback to create the mind, but function and behavior of these mechanisms can’t be explained by physical laws alone because information generalizes function into abstract entities in their own right. Descartes anatomical conclusion that the soul could not be distributed across the brain and so had to be concentrated in the one part that was not doubled was wrong. His assertion that common sense, thought, and the soul cannot be separated is similarly wrong; our sense of self is an aggregation of many parts, including the sense that it is unified and not aggregate.

David Hume anticipated evolutionary theory in his A Treatise of Human Nature (1739), which saw people as a natural phenomenon driven by passions more than reason. Hume divided knowledge into ideas (a priori) and facts (a posteriori). One studies ideas through math and the formal sciences and facts via the experimental sciences. As we ultimately only know of the physical world through our senses, all our knowledge of it must ultimately come from the senses. He further recognized via the problem of induction that we could never prove anything from experience or observation; we could only extrapolate from it. This meant we have no rational basis for belief in the physical world, though we have much instinctive and cultural basis. Hume expanded on Descartes’ “cogito ergo sum” by proving that knowledge from induction could not be proven and that we must therefore remain perpetually skeptical of science. Hume is arguably the founder of empiricism, the idea that knowledge comes only or primarily from sensory experience. While empiricism is a cornerstone of scientific inquiry, this focus on the source of knowledge may have inadvertently moved science away from functionalism, which focuses on the use of knowledge.

Though principally a sociologist, and the inventor of the word sociology, August Comte also lifted empiricism to another level called positivism, which asserted that all knowledge we know for sure or positively must be a posteriori from experience and not a priori from reason or logic. He proposed in 1822 in his book Positive Philosophy that society goes through three stages in its quest for truth: the theological, the metaphysical, and the positive (though different stages could coexist in the same society or in the same mind). The theological or fictitious stage is prescientific and cites supernatural causes. In the metaphysical or abstract stage people used reason to derive abstract but natural forces such as gravity or nature. Finally, in the positive or scientific stage, we abandon the search for absolutes or causes and embrace the power of science to reveal nature’s invariant laws through an ever-progressing refinement of facts based on empirical observations5. While Comte did not insist that this progression was necessarily sequential or singular, but could happen at different times in different societies, institutions, or minds, he broadly proposed that the world entered the positivistic stage in 1800 and used this generalization to support his reactionary authoritarian agenda that sought to elevate scientists to elite technocrats who governed according to the findings of the new science of sociology that he founded. In Comte’s mind, skepticism of science was unnecessary; instead, we should embrace it as proven knowledge that could be refined further but not overturned. Although Hume may have been technically right, empiricism moved progressively toward positivism because it just worked so well, and by the end of the 19th century, many thought the perfect mathematical formulation of nature was nearly at hand.

In 1878, Charles Sanders Peirce wrote a paper called, “How To Make Our Ideas Clear,” which distinguished three grades of clarity we can have of a concept. The first grade was visceral, the understanding that comes from experience without analysis, such as our familiarity with our senses and habitual interactions with the world. The second grade was analytic, as evidenced by an ability to define the concept in general terms abstracted from a specific instance. The third grade was pragmatic, being a conception of “practical bearings” the concept might have. While Peirce had some considerable difficulty grappling with whether a general scientific law could be taken to imply practical bearings, in the end he did endorse such scientific implications even in instances where one could not test them. Peirce’s first grade of clarity describes what I call instinctive and subconceptual knowledge. The second grade characterizes conceptual knowledge. While being able to provide a definition is good evidence of conceptual knowledge, it is not actually necessary to provide a definition to use a concept. Peirce put great stock in language as the bearer of scientific knowledge, but I don’t; language is a layer above the knowledge which helps us characterize and communicate it, but which also inevitably opens the door for much to be lost in translation. I would describe the third grade of clarity as actually being the function. Instincts, subconcepts, and concepts all have functions, and the functions of the former contribute to the functions of the latter as well. Where empiricism tied meaning to the source of information, i.e. to empirical evidence, pragmatism shifted meaning to the destination, i.e. its practical effects. The power of science is that it focuses on the practical effects at the conceptual level as carefully and rigorously as we can manage. By construction, all information is pragmatic, but scientific information uses methods and heuristics to find the most widely useful information. While pragmatism has been slowly gathering support, it had little impact on science at the time.

Positivism made another big leap forward in the 1920’s and 30’s when a group of scientists and philosophers called the Vienna Circle proposed logical positivism, which held that only scientific knowledge was true knowledge and, brashly, that knowledge from other sources was not just false and empty, but meaningless. These other sources included not just tradition and personal sources like experience, common sense, introspection, and intuition, but also the whole metaphysics of academic philosophy. Logical positivism sought to perfect knowledge through reason and from there all of civilization. It all hinged on the hope that physical science (and by extension natural and social science) was “proving things” and “getting somewhere” to attain “progress”. To this end, they sought to unify science under a single philosophy that captured meaning and codified all knowledge into a standardized formal language of science. They maintained the empirical view that knowledge about the world ultimately derived from sensory experience but further acknowledged the role of logical reasoning in organizing it. Perhaps more accurately, logical positivism was part of a movement called logical empiricism across several decades and continents of leading scholars intent on improving scientific methodology and the role of science in society rather than espousing any specific tenets, but logical positivism as I described it approximates the philosophies of circle members Rudolf Carnap and Moritz Schlick. Logical positivism attempted to formalize what science seemed to do best, to package up knowledge perfectly. But even at the time, this idealized modernist dream was starting to crack at the seams. Instead of progressively adding detail, physics had revealed that reality was more nebulous than expected with wave-particle duality, curved space and time, and more. Gödel’s incompleteness theorems proved that no formal system could ever be complete or consistent but must be inherently limited in its reach. Willard Van Orman Quine famously wrote in Two Dogmas of Empiricism in 1951 that “a boundary between analytic and synthetic statements simply has not been drawn. That there is such a distinction to be drawn at all is an unempirical dogma of empiricists, a metaphysical article of faith.” Analytic statements are a priori logical conclusions, while synthetic statements are a posteriori statements based on experience. The flaws Quine cited relate to the fact that statements are linguistic, and a linguistic medium in intrinsically synthetic because it is not itself physical. Logical positivism invested too much in the power of language, which is descriptive of function but not the same as function, and so it was left behind, along with the rest of modernism, to be replaced by the inherent skepticism of postmodernism. From my perspective, functionally, I would say that the logical positivists correctly intuited that science creates real knowledge about the world, but they just grasped for an overly simplified means of describing that knowledge.

If positivist paths to certainty were now closed, where could science look for a firm foundation? Thomas Kuhn provided an answer to this question in The Structure of Scientific Revolutions in 1962, which is remembered popularly for introducing the idea of paradigm shifts (though Kuhn did not coin the phrase himself). Without exactly intending to do so, Kuhn created a new kind of coherentist solution. An epistemology or theory of knowledge must provide a solution to the regress problem, which is this: if a belief is justified by providing a further justified belief, then how do you reach the base justified beliefs? There are two traditional theories of justification: foundationalism and coherentism. Aristotle and Descartes were foundationalists because they sought basic beliefs that could act as the foundation for all others, eliminating the perceived problem of infinite regress. Coherentists hold that ideas support each other if they are mutually consistent, much like the words in a language can all be defined in terms of each other. The positivists were struggling to make foundationalism work, and in the end it just didn’t because Hume was right: knowledge from induction could not be proven, so the logical base was just not there. Into this relative vacuum, Kuhn claimed that normal science consisted of observation and “puzzle solving” within a paradigm, which was a coherent set of beliefs that mutually support each other rather than depending on ultimate foundational beliefs. He further, somewhat controversially, proposed that revolutionary science occurred when an alternate set of beliefs incompatible with the normal paradigm overtook it in a paradigm shift. While Kuhn’s conclusions are right as far as they go, which helps explain why this was the most influential book on the philosophy of science ever written, he inadvertently alienated himself from most physical scientists because it made it look as if science was purely a social construction, which was not his intent at all. But once he had let the cat out of the bag, he could not put it back in again. With the door open for social constructionists to undermine science as an essentially artistic endeavor, scientific realists took on the challenge of restoring certainty to science.

Scientific realism (~1980-present) has supplanted logical positivism as the leading philosophy of science by looking to fallibilism for epistemological support. Fallibilism is not a theory of justification, but it is an excuse for claiming justification is unnecessary. Instead of looking to axioms, or mutual support, or support from an infinite chain of reasons, fallibilism just acknowledges that no beliefs can be conclusively justified, but asserts that “knowledge does not require certainty and that almost no basic (that is, non-inferred) beliefs are certain or conclusively justified”. They recognize that claims in the natural sciences, in particular, are “provisional and open to revision in the light of new evidence”. The difference between skepticism and fallibilism is that while skeptics deny we have any knowledge, fallibilists claim that we do, even though it might be revised following further observation. Knowledge can be said to arise because while “a theory cannot be proven universally true, it can be proven false (test method) or it can be deemed unnecessary (Occam’s razor). Thus, conjectural theories can be held as long as they have not been refuted.”6. This suggests that until it has been proven false or redundant, it can be taken as effectively true. Realists further propose that this mantle of scientific truth not be extended to every scientific claim not yet disproven, but should be reserved for those satisfying a quality standard, which is generally taken to be include things like having maturity and not being ad hoc. Maturity suggests having been established for some time and been well tested, and not being ad hoc suggests not being devised just to satisfy known observations without having undergone suitable additional testing.

With this philosophical underpinning, scientific realists feel justified in thinking that the observed uniformity of nature and success of established scientific laws can be taken to mean that the physical world described by science exists and is well characterized by those laws. Put another way, “The scientific realist holds that science aims to produce true descriptions of things in the world (or approximately true descriptions, or ones whose central terms successfully refer, and so on).”7 In a nutshell, Richard Dawkins summarized the realist sentiment in 2013 by noting that “Science works, bitches!”8. It sounds pretty plausible, but is it enough? The determination of what is mature enough and not too ad hoc is ultimately subjective, and a function of the paradigms of the day, which suggest that the social constructive view still permeates scientific realism. Furthermore, it takes for granted that the idealized models of science can be objectively applied to reality but specifies no certain way to do that. The methods and approaches that have become mature and established, though also subjective, are taken as valid ways to match theory to reality. So the question remains, is scientific realism actually justified, and if so, how?

Superficially, the central idea of scientific realism is that the physical world described by science exists. But I would claim that this is irrelevant and incidental; the deeper idea of scientific realism is that it works, where “works” means that it provides functionality. We do engage in science because we want to know the truth about nature, both because the knowledge brings functional power and just because it is cool — the potential power that elegant explanations bring is very satisfying to our function-seeking brains. Scientific laws are general; beyond specific situations, they specify general functionality or capacity for a range of possible situations. But none of this changes the fact that we can never prove that the physical world really exists. Its actual existence is not the point. The point is what science has to say about it, which is a functional existence, that we experience through the approximate but strong sense of consistency between our theories and observations. As I will explore later, our minds are wired to think about things as being certain even though deep down we can appreciate that nothing is certain. That deeper reality (that nothing is certain) just doesn’t impress our mental experience as much as the feeling of certainty does. So scientific realism is just an accommodation to human nature and our desire to feel certainty. The real philosophy of science has to be functionalism, which isn’t concerned with certainty, only with higher probabilities for desired outcomes. I am ok with scientific realism so long as we understand it is a slightly misleading shorthand for functionalism.9

“Epistemologically, realism is committed to the idea that theoretical claims (interpreted literally as describing a mind-independent reality) constitute knowledge of the world.”10 We can see what realism is after: it seems intuitive that since the scientific laws work we should just be able to think of them as knowledge. But was Newton’s law of gravity knowledge? We know it was not right; because of relativistic effects it is never 100% accurate, and because his model proposed action at a distance, even Newton felt it was unjustifiably mystical. Einstein later corrected gravity for relativity and also formulated it as a field effect and not an “interaction” between objects, but we know that general relativity is not the whole story about gravity either. So, if the models aren’t right, on what basis are we entitled to think we have knowledge? Is it our willingness to “commit” to it? Willingness to believe is not good enough. I interpret realism as an incomplete philosophy that takes the important step of affirming aspects of science we know intuitively make sense, without being too demanding about providing the ontological and epistemological basis for those aspects.

In the 1990s, postmodernists did push the claim that all of science was a social construction in the so-called science wars. Scientific realism alone was inadequate to fight off postmodern critiques, so formally science is losing the battle against relativism. I contend that the stronger metaphysical support of functionalism is enough to push the postmodernists back into the hills, but only if science embraces it. The Sokal affair, a bogus and meaningless scientific paper that actually did get published, highlights a fundamental flaw in science as practiced: it becomes divorced from foundational roots. The foundation must never be taken for granted but must always be spelled out to some level of detail in every scientific paper. The current convention is for a scientific paper to presume some level of innate acceptance of unspoken paradigms, and the greater the presumption, the more authoritative the paper sounds. But this is the wrong path; papers should start from nothing and introduce the assumptions on which they build, with a critical eye. This philosophical backdrop doesn’t need to take over the paper, but without it, the paper is only of use to specialists, which undermines generalism, which is ultimately as important to functionalism as specialization.

Now I can reveal the real solution to the regress problem. The answer is not in the complete support of foundationalism or the mutual support of coherentism, or any other theories put forth so far. It is in “bootstrapism”. Information is captured by living information systems through four levels: genetic, instinct, subconcept and concept, and only the last level leverages logic, and only a small part of that logic is based on logical systems we have thought up, e.g. the three traditional laws of thought. Furthermore, there is a further “fifth” level, the linguistic level, that is not really level of information but a level of representation of information from the other four levels. Also, note that the four to five interacting information management systems are not the only levels; we create virtual levels with every model that builds on other models and lower-level information. So the regress problem boils down to bootstrapping, which is done by building more powerful functional systems with the help of simpler ones. The solution to the seeming paradox of infinite regress doesn’t require infinite support (though feedback can cycle endlessly), it just requires a few levels of information that build on each other. The levels also interact with each other to become mutually supporting, which can create the illusion that the topmost, conceptual level, or even more absurdly, the linguistic level, might be keeping the whole boat afloat by itself. It just isn’t like that; the levels depend on each other, and language just renders a narrow slice of that information. The idea that well-formed sentences of a language have meaning is flawed; the sentences of languages, formal or natural, have no meaning in and of themselves, though they may stimulate us to think of things with meaning. The Vienna Circle inadvertently put too much faith in formal logic (which is one-leveled) and conflated it with thought (which is multi-leveled).

Science works because scientific methods increase objectivity while reducing subjectivity and relativism. It doesn’t matter that it doesn’t (and, in fact, can’t) eliminate it. All that matters is that it reduces it. This distinguishes science from social construction by directing it toward goals. Social constructions go nowhere, but science creates an ever more accurate model of the world. So one could fairly say that science is a social construction, but it is one that continually moves closer to the truth, if truth is defined in terms of knowledge that can be put to use. In other words, from a functional perspective, truth just means increasing the amount, quality, and levels of useful information.

It is not enough for scientific communities to assume their best efforts will produce objectivity, we must also discover how preferences, biases, and fallacies can mislead the whole community. Tversky and Kahneman did groundbreaking work exposing the extent of cognitive biases in scientific research, most notably in their 1971 paper, “Belief in the law of small numbers.”1112. Beyond just being aware of biases, scientists should not have to work in situations with a vested interest in specific outcomes. This can potentially happen in both public and private settings but is more commonly a problem when science is used to justify a commercial enterprise. Scientists must not be put in the position of having a vested interest in supporting a specific paradigm. To ensure this, they must be encouraged and required to mention both the paradigm they support and its alternatives, at least to a sufficient degree to fend off the passive coercion that failing to do so creates psychologically.

Summary

As practiced, physical science (arguably) starts with these paradigmatic assumptions:

(a) the physical world exists independent of our conception of it,

(b) its components operate only via natural causes and with perfect consistency,

(c) evidence from the physical world can be used to learn about that consistency, and

(d) logical models can describe that consistency, making near-perfect prediction possible.

I have explained why assumption (a) is ultimately irrelevant since knowledge derives from phenomena and not noumena themselves. Point (b) is relevant, but not necessary either because functionalism doesn’t require perfect consistency, only enough consistency to be able to make useful predictions. Assumption (c) forms the practical basis for functionalism; the creation of information relies exclusively on feedback. We start with our senses and move on to instruments for greater accuracy and independence from subjectivity. And point (d) simply goes to the power of information management systems to build more powerful information from simpler information, though the physical sciences only scratch the surface by sticking to logical models and near-perfect prediction. Statistical analyses can reveal useful patterns without logic or perfection and are essential tools of the mind and any comprehensive information management system. So functionalism is largely consistent with science as practiced and vice versa. But as we look to explain purely functional phenomena, like the mind itself, we need to move beyond these simplified assumptions to the broader and stronger functional base, because they won’t get us very far.

The stronger functional base is simply that function as an entity exists; i.e. that information and its management exist, both theoretically and via physical manifestations of information management systems. The concept of information is that patterns exist and can be detected (observed) and represented to predict future patterns. Information can be about physical things, or not, and can be represented using physical means, or not. Either way, it is abstracted from the physical via indirect reference and consequently is not physical itself, despite the assistance physical mechanisms provide.

The Rise of Consciousness

Contents

The Rise of Function
The Power of Modeling and Entailment
Qualia and Thoughts
Color
Emotions
Thoughts
The Self
The Stream of Consciousness
The Hard Problem of Consciousness
Our Concept of Self

The Rise of Function

I’ve established what function is and suggested ways we can study it objectively, but before I get into doing that I would like to review how function arose on Earth. It started with life, which created a need for behavior and its regulation, which then created value in learning, and which was followed at last by the rise of consciousness. We take information and function for granted now, but they are highly derived constructs that have continuously evolved over the past 4.28 billion years or so. We can never wholly separate them from their origins as the acts of feedback that created them help define what they are. However, conceptual generalizations about what functions they perform can be fairly accurate for many purposes, so we don’t have to be intimately familiar with everything that ever happened to understand them. This is good, because the exact sequence of events that sparked life is still unknown, along with most of the details since. The fossil record and genomes of living things are enough to support a comprehensive overview and also gives us access to many of the details.

We know that living, metabolizing organisms invariably consist of cells that envelop a customized chemical stew. We also know that all organisms have a way to replicate themselves, although viruses do it by hijacking the cells of other organisms and so cannot live independently. But all life has mutual dependencies on all other life either very narrowly through symbiosis or more broadly by sharing resources in the same ecological niche or the same planet. Competition between or across species is rewarded with a larger population and share of the resources. The chemical stew in each cell is maintained by a set of blueprint molecules called DNA (though originally thought to have been RNA) which contain recipes for all the chemicals and regulatory mechanisms the cell needs to maintain itself. Specifically, genes are DNA segments that are either transcribed into proteins or regulate when protein-coding genes turn on or off. Every call capable of replication has at least one complete set of DNA1. DNA replicates itself as a double helix that unwinds like a zipper while simultaneously “healing” each strand to form two complete double helices. While genes are not directly functional, proteins have direct functions and even more indirect ones. Proteins can act as enzymes to catalyze chemical reactions (e.g. replicating DNA or breaking down starch), as structural components (e.g. muscle fibers), or can perform many other metabolic functions. Not all the information of life is in the DNA; some is in the cell walls and the chemical stew. The proteins can maintain the cells but can’t create them from scratch. Cell walls probably arose spontaneously as lipid bubbles, but through eons of refinement, their structure has been completely transformed to serve the specific needs of each organism and tissue. The symbiosis of cells and DNA was the core physical pathway that brought function into the world.

A stream has no purpose; water just flows downhill. But a blood vessel is built specifically to deliver resources to tissues and to remove waste. This may not be the only purpose it serves, but it is definitely one of them. All genes and tissues have specific functions which we can identify, and usually one that seems primary. Additional functions can and often do arise because having multiple applications is the most convenient way for evolution to solve problems given proteins and tissue that are already there. Making high-level generalizations about the functions of genes and tissues is the best way to understand them, provided we recognize the limitations of generalizations. Furthermore, studying the form without considering the function is not very productive: physicalism must take a back seat to functionalism in areas driven by function.

Lifeforms have many specialized mechanisms to perform specific functions, many of which happen simultaneously. However, an animal that moves about can’t do everything at once because it can only be in one place at a time. A mobile animal must, therefore, prioritize where it will go and what it will do. This functional requirement led to the evolution of animal brains, which collect information about their environment through senses and analyze it to develop strategies to control the body. Although top-level control happens exclusively in the brain, the nervous and endocrine systems work in tandem as a holobrain (whole brain) to control the body. Nerves transmit specialized or generalized messages electronically while hormones transmit specialized messages chemically. While instinctive behavior has evolved for as many fixed functions as has been feasible, circumstances change all the time, and nearly all lifeforms consequently have some capacity to learn, whether they have brains or not. Learning was recently demonstrated quite conclusively in plants by Monica Gagliano2. While non-neural learning mechanisms are not yet understood, it seems safe to say that both plants and animals will habituate behaviors using low-level and high-level mechanisms because the value of habituation is so great. While we also can’t claim full knowledge about how neural learning works, we know that it stores information dynamically for later use.

My particular focus here, though, is not on every way brains learn (using neurons or possibly hormonal or other chemical means), but on how they learn and apply knowledge using minds. “Mind” is something of an ambiguous word: does it mean the conscious mind, the subconscious mind, or both? English doesn’t have distinct words for each, but the word “mind” mostly refers to the conscious mind with the understanding that the subconscious mind is an important silent partner. When further clarity is needed, I will say “whole mind” to refer to both and “conscious mind” or “subconscious mind” to refer to each separately. Consciousness is always relevant for any sense of the word “mind” and never of particular relevance when using the word “brain” (except when used very informally, which I won’t do). Freud distinguished the unconscious mind as a deeper or repressed part of the subconscious mind, but I won’t make that distinction here as we don’t know enough about the subconscious to subdivide it into parts. While subconscious capabilities are critical, we mostly associate the mind with conscious capabilities, namely four primary kinds: awareness, attention, feelings, and thoughts. Under feelings, I include sensations and emotions. Thoughts include ideas and beliefs, which we form using many common sense thinking skills we take for granted, like intuition, language, and spatial thinking. Feelings all have a special experiential quality independent of our thoughts about them, while thoughts reference other things independent of our feelings about them. Beliefs are an exception; they are thoughts because they reference other things, but we have a special feeling of commitment toward them. We have many thoughts about our feelings, but emotions and beliefs are feelings about our thoughts. I’ll discuss them in detail below after I have laid some more groundwork. Awareness refers to our overall grasp of current thoughts and feelings, while attention refers to our ability to focus on select thoughts and feelings. All four capabilities — awareness, attention, feelings, and thoughts — help the conscious mind control the body, which it operates through motor skills that work subconsciously. Habituation lets us delegate the subconscious to execute fairly complex behaviors with little or no conscious direction. This may make it seem like the subconscious mind acts independently because it initiates some reactions before we are consciously aware we reacted. It is more efficient and effective to leave routine matters to the subconscious as much as possible, but we can quickly override or retrain it as needed.

The Power of Modeling and Entailment

The role of consciousness is to promote effective top-level decision making in animals. While good decisions can be made without consciousness, as some computer programs demonstrate, consciousness is probably the most effective way for animals to make decisions, and in any case, it is that path that evolution chose. Consciousness is best because it solves the problem of TMI: too much information. Gigabytes of raw sensory information flow into the brain every second. A top-level decision, on the other hand, commits the whole body to just one task. How can all that information be processed to yield one decision at a time? Two fundamentally different information management techniques might be used to do this, which I generically call data-driven and model-driven. Data-driven approaches essentially catalog lots of possibilities and pick the one that seems best. Model-driven approaches break situations down into more manageable pieces that follow given rules. Data-driven techniques are holistic and integrate diverse data, while model-driven techniques are atomistic and differentiate data. The subconscious principally uses data-driven methods, while the conscious mind principally uses model-driven methods, though they can leverage results from each other. The reason is that data-driven methods need parallel processing while model-driven methods work require single-stream processing (at least, they require it at the top level). The conscious mind is a single-stream process while the rest of the mind, the subconscious, is free to process in parallel and most likely is entirely parallel. This difference is not a coincidence, and I will argue more later that the sole reason consciousness exists is so that our decision making can leverage model-driven methods.3 The results of subconscious thinking like recognition, recollection, and intuition just spring into our conscious minds after the subconscious has holistically scanned its stored data to find matches for given search criteria. While we know these searches are massively parallel, the serial conscious mind has no feel for that and only sees the result. The drawback of data-driven techniques is that while they can solve any problem whose solution can be looked up, the world is open-ended and most real-world problems haven’t been posed yet, much less solved and recorded. Will a data-driven approach suffice for self-driving cars? Yes, probably, since the problem space is “small” enough that millions of hours of experience logged by self-driving cars is enough for them to equal or exceed what humans can do on just hundreds to thousands of hours. Many other human occupations can also be largely automated by brute-force data-driven approaches, all without introducing consciousness to robots.

The more interesting things humans do involve consciously setting up models in our minds that simplify more complex situations. Information summarizes what things are about — phenomena describing noumena — and so is always a simplification or model of what it represents. But models are piecewise instead of holistic; they explicitly attempt to break down complex situations into simpler parts. The purpose of this dissection is that one can then consider logical relationships between the simplified parts and derive entailment (cause and effect) relationships. The power of this approach is that conclusions reached about models will also work for the more complex situations they represent. They never work perfectly, but it is uncanny how well they work most of the time. Data-driven approaches just don’t do this; they may discriminate parts but don’t contemplate entailment. Instead, they look solutions up from their repertoire, which must be very large to be worthwhile. While physical models are comprised of parts and pieces, conceptual models are built out of concepts, which I will also sometimes call objects. Concepts or objects are functionally delineated things that are often spatially distinct as well. An object (as in object-oriented programming) is not the thing itself, but what we know about it. What we can know about an object is what it refers to (is about) and its salient properties, where salience is a measure of how useful a property is likely to be. Because a model is simpler than reality, the function of the concepts and properties that comprise it can be precisely defined, which can lead to certainty (or at least considerable confidence) in matters of cause and effect within the model. Put into the language of possible worlds logic, we say that if something is true in a possible world, then it is necessarily true. Knowing that something will necessarily happen is perfect foreknowledge, and some of our models apply so reliably to the real world that we feel great confidence that many things will happen just the way we expect, even though we know that in the real world extraneous factors occasionally prevent our simple models from being perfect fits. We also use many models that are only slightly better than blind guessing (e.g. weather forecasting), but any measure of confidence better than guessing provides a huge advantage.

Though knowledge is imperfect, we must learn so we can act confidently in the world. Our two primary motivations to learn are consequences and curiosity. Consequences inspire learning through positive and negative feedback. Direct positive consequences provide an immediate reward for using a skill correctly. Direct negative consequences, aka the school of hard knocks, let us know what it feels like to do something wrong. Indirect positive or negative consequences such as praise, punishment, candy, grades, etc., guide us when direct feedback is lacking. The carrot-and-stick effect of direct and indirect consequences pulls us along, but we mostly need to push. We can’t afford to wait and see what lessons the world has for us, we need to explore and figure it out for ourselves, and for this we have curiosity. Curiosity is an innate, subconscious motivating force that gives us a rewarding feeling for acquiring knowledge about useless things. Ok, that’s a joke; we are most curious about things we think will be helpful, but we do often find ourselves fascinated by minor details. But curiosity drives us to pursue mastery of skills assiduously. Since the dawn of civilization people have needed a wide and ever-changing array of specialized skills to survive. We don’t really know what knowledge will be valuable until we use it, so our fascination with learning for its own sake is critical to our survival. We do try to guess what kind of knowledge will benefit people generally and we try to teach it to them at home and in school, but we are just guessing. Parenting never had a curriculum and only emerged as a verb in the 1960’s. Formal education traditionally stuck to history, language, and math, presumably because they are incontrovertibly useful. But picking safe subjects and formally instructing them is not the same as devising a good education. The Montessori method addresses a number of the shortcomings of traditional early education, largely by putting more emphasis on curiosity than consequences. In any case, evolution will favor those with a stronger drive to explore and learn over the less curious up until the point where it distracts them from goals more directly necessary for survival. So curiosity is balanced against other drives but is increasingly helpful in species that are more capable of applying esoteric knowledge. So curiosity was a key component of the positive feedback loop that drove up human intelligence. Because it is so fundamental to survival, curiosity is both a drive and an emotion; I’ll clarify the distinction a bit further down.

To summarize, consciousness operates as a discrete top-level decision-making process in the brain by applying model-driven methods while simultaneously considering data-driven subconscious inputs. We compartmentalize the world into concepts and models in which they operate according to rules of cause and effect. Emotional rewards, including curiosity, continually motivate us to strive to improve our models to be more successful. Data-driven approaches can produce good decisions in many situations, especially where ample experience is available, but they are ultimately simplistic, “mindless” uses of pattern recognition which can’t address many novel problems well. So the brain needs the features that consciousness provides — awareness, attention, feeling, and thinking — to achieve model-based results. But now for the big question: why is consciousness “experienced”? Why do we exist as entities that believe we exist? Couldn’t the brain go about making top-level decisions effectively without bothering to create self-possessed entities (“selves”) that believe they are something special, something with independent value above and beyond the value of the body or the demands of evolution? Maybe; it is not my intention to disprove that possibility. But I do intend to prove that first-person experience serves a vital role and has a legitimate claim to existence, namely functionality, which I have elaborated on at length already but which takes on new meanings in the context of the human mind.

Qualia and Thoughts

The context the brain finds itself in naturally drives it toward experiencing the world in the first person. The brain must conduct two activities during its control of the body. First, it must directly control the inputs and outputs, i.e. receive sensory information and move about in the world. And second, it must develop plans and strategies telling it what to do. We can call these sets of information self and not-self, or self and other, or subject and object (before invoking any concept of first-personness). It is useful to keep these two sets of information quite distinct from each other and to have specialized mechanisms for processing each. My hypothesis of consciousness is that it is a subprocess within the brain that manages top-level decisions and that the (subconscious) brain creates an experience for it that only makes information relevant to top-level decisions available. In particular, it uses specialized mechanisms to create a very different experience for self-information than for not-self-information. Self-information is experienced consciously as awareness, attention, feelings, and thoughts, but by thoughts, here, I mean just experiencing the thoughts without regard to their content. These things constitute our primary sense of self. Not-self-information is the content of the thoughts, i.e. what we are thinking about. Put another way, the primary self is the information an agent grasps about itself automatically (without having to think about it), and not-self are the things it sees and acts upon as a result of thinking about them. It is the responsibility of the consciousness subprocess to make all self-information appear in our minds experientially without having to be about anything. The customized feel of this information can be contrasted with the representational “feel” of not-self-information. Not-self-information doesn’t “feel” like anything at all, it just tells us things about other things, representing them, describing them and referencing them. Those things themselves don’t need to really exist; they exist for us by virtue of our ability to think about them.

We know what our first-person experience feels like, but we can’t describe it objectively. Or rather, we can describe anything objectively, but not in a way that will adequately convey what that experience feels like to somebody who can’t feel it. The qualities of the custom feelings we experience are collectively called qualia. We distinguish red from green as very different qualia which we know from intimate experience, but we could never characterize in any useful way how they feel different to a person who has red-green color blindness. Each quale (pronounced kwol-ee, singular of qualia) has a special feeling created for our conscious minds by untold subconscious processing. It is very real to our conscious minds, and has the objective reality that some very specialized subconscious processing is making certain information feel a certain way for our conscious benefit. Awareness and attention themselves are fundamental sorts of qualia that conduct all other qualia. And thoughts feel present in our minds as we have them, but thoughts don’t “feel” like anything because they are general-purpose ways of establishing relationships about things (beliefs are an exception discussed below that have a feeling of “truth”). So we most commonly use the word qualia to describe feelings, which each have a very distinct customized feel that awareness, attention, and thoughts lack. In other words, all awareness, attention, and thoughts feel the same, but every kind of feeling feels different. Our qualia for feelings divide into sensory perceptions, drives, and emotions. Sensory perceptions come either from sense organs like eyes, ears, nose, tongue, and skin, or from body senses like awareness of body parts (proprioception) or hunger. Drives and emotions arise from internal (subconscious) information management mechanisms which I will describe more further down. Our qualia, then, are the essence of what makes our subjective experience exist as its own perspective, namely the first-person perspective. They can be described in terms of what information they impart, but not how they feel (except tautologically in terms of other feelings).

We create a third-person world from our not-self-information. This is the contents of all our thoughts about things. Where qualia result from innate subconscious data processing algorithms, thoughts develop to describe relationships about things encountered during experience. Thoughts can characterize these relationships by representing them, describing them, or referencing them. This description makes it sound like some “thing” must exist to which thoughts refer, but actually thoughts, like all information, are entities of function: information separates from noise only to the extent that it provides predictive power, aka function. It can be often useful when describing information to call out conceptual boundaries separating functional entities of representation, description or reference, but much information (especially subconscious information) is a much more abstract product of data analysis. But in any case, thoughts are data about data, patterns found in the streams of information that flow into our brains. We can consequently have thoughts about awareness, attention, feelings, and other thoughts thought those thoughts being the awareness, attention, feelings, and thoughts themselves. These thoughts about our conscious processes form our secondary sense of self. We know humans have a strong secondary sense of self because we are very good at thinking, and so we suspect other animals have a much weaker secondary sense of self because they are not as good at thinking, though they do all have such a sense because all animals with brains can analyze and learn from experiential data, which includes data about consciousness.

This logical separation of self and not-self-information does not in itself imply the need for qualia, i.e. first-person experience. The reason feeling is vital to consciousness has to do with how self-information is integrated. Subconsciously, we process sensory information into qualia so we can monitor all our senses simultaneously and yet be able to tell them apart. It is important that senses work this way as the continuous, uninterrupted and distinguished flow of information from each sense helps us stay alive. But it is how we tell them apart that gives each quale its distinctive feel. Ultimately, information is a pattern that can be transmitted as a signal, and viewed this way each quale is much like another because patterns don’t have a feel in and of themselves. But each quale reaches our conscious mind through its own data channel (logically, the physical connection is still unknown) that brings not only the signal but a custom feel. What we perceive as the custom feel of each quale is really just “subconscious sugar” to help us conveniently distinguish qualia from each other. The distinction between red and green is just a convenient fiction created by our subconscious to facilitate differentiation, but they feel different because the subconscious has the power to create feelings and the conscious mind must accept the reality fed to it. We can think whatever conscious thoughts we like, but qualia are somehow made for us outside conscious control. While the principal role of qualia is to distinguish incoming information for further analysis, they can also trigger preferences, emotions, and memories. Taste and smell closely associate with craving and disgust. Color and sound have an inherent calming or alerting effect. These associations help to further differentiate qualia in a secondary way. To some extent, we can feel qualia not currently stimulated by the senses by remembering them, but the memory of a quale is not as vivid or convincing as it felt at first-hand, though it can seem that way when dreaming or under hypnosis.

Color

How we tell red and green apart is ineffable; we just can. We see different colors in the rainbow as a wide variety of distinctive qualities and not just as shades of (say) gray. All shades of gray share the same color quale and only vary in brightness. We are dependent on ambient lighting even to tell them apart. Not so with red and green, whose quale feel completely different. This facility stems from how red, green, and blue cone cells in the eye separate colors into independent qualia. Beyond this, we see every combination of red, green, and blue as a distinct color, up to about ten million hues. While we interpret many of these combined hues as colors in the spectrum, three color values in combination define a plane, not a line, so we see many colors not in the rainbow. Significantly, we interpret the combination of red and blue without green as pink, and all three together as white. Brown is another, but we can actually only distinguish hundreds to (at the very most) thousands of distinct colors along the visible band of the electromagnetic spectrum, which means that nearly all colors we distinguish are non-spectral. Although being able to distinguish colors is the primary reason we can do it, this doesn’t explain why they are “pretty”, i.e. colorful. First, note that if we take consciousness as a process that is only fed simple intensity signals for red, green and blue, then it could distinguish them but they wouldn’t feel like anything. I propose, but can’t prove, that the qualia we feel for colors that I called subconscious sugar above result from considerable additional subconscious processing which extends a simple intensity signal into something which feels much more readily unique to the conscious mind than the qualia would feel if, say, they appeared as a number of gauges. While qualia are ultimately just information and not qualities, the way we consciously feel the world is entirely a product of the information our subconscious feeds us, so we shouldn’t think of our conscious perception of the world as a reflection of it, we should think of it as a complex creation of the subconscious that gives a deep informational structure to each kind of sensory input. Qualia are like built-in gauges which we don’t have to read; we can just feel the value they are using awareness alone. Since the role of consciousness is to evaluate available information to make decisions quickly and continuously, anything the subconscious mind can do to make different kinds of information distinctively appear in our awareness helps. We can distinguish many colors, but nowhere near all the light information objects give off. Our sense of a three-dimensional object feels to us like we know the noumenon of the object itself and are not just representing it in our minds. To accomplish this, we subconsciously break a scene down into a set of physically discrete objects, automatically building an approximate inventory of objects in sight. Our list of objects and features of each, like their color, form an informational picture. That picture is not the objects themselves but is just a compendium of facts we consider useful. Qualia innately convert a vast stream of information into a seamless recreation of the outside world in our head. Our first-person lives are just an efficient way to allow animals to focus their decision-making processing on highly-condensed summaries of incoming information, solving the problem of TMI (too much information).

But why does red specifically feel red as we know it, and is everyone’s experience of red the same? The specific qualities of the qualia is at the heart of what David Chalmers has famously called the hard problem of consciousness. This problem asks why we experience qualia at all, and specifically why does a given quale feel the specific way it does. We think of qualia as being as real as anything we know since all of our reality is mediated through them. However, we must admit that they are imaginary informational constructs our brain puts together for us subconsciously and presents to our conscious mind with the mandate that we believe they are real. So, objectively, then, we realize our brains are creating these experiences for us. It is in our brain’s best interests that the information the qualia provide us be as consistent with the outside world as possible so that we will have full confidence to act. When we lose a vital sense, e.g. when we are plunged into darkness, our confidence and reactions are severely compromised. But even knowing that a quale’s feel is imaginary doesn’t explain why it has the characteristic feel that it has. To explain this, I would suggest that we recall that the mind exists to serve a function, not just to exist as physical noumena do. The nature of the qualia, then, is intimately and entirely a product of their function: they feel like what they inspire us to do. While the primary role of the feel of qualia is let us simultaneously feel many channels of information simultaneously while keeping them distinct, their exact feeling is designed to persuade us to consider them appropriately. Green is not just distinct from red, it is calming where red is provocative. Colors are pretty, yes, but my contention is that their attractiveness actually derives from their emotional undertones. Grays don’t carry this extra emotional coloring, so we feel neutral about them. There is typically no reason to be interested in gray. From an evolutionary standpoint, the more helpful it is to consciously notice and distinguish a quale when it is perceived, the stronger the need for it to have a strong and distinctive custom feeling. Hunger, thirst, and sex drives can be very compelling. Damaging heat or cold or injuries are painful. Dangerous substances often smell or taste bad. We shy away from the unpleasant and seek the comfortable to the exact degree the custom feeling of the qualia involved inspire us. Qualia are the way the subconscious inspires the conscious mind to behave well. To develop this further, let’s consider how we perceive color.

We sense colored light using three types of cone cells in the retina. A second stage of vision processing done by retinal ganglion cells creates two signals, one indicating either yellow or blue (y/b) and the other indicating either red or green (r/g). Because these two signals can never produce both yellow and blue or both red and green, it is psychologically impossible for something to be reddish green or yellowish blue (note that mixing red and green paint may make brown, and yellow and blue may make green, but that is not the same thing as making something reddish or yellowish). These four psychological primary colors are then blended in a third stage of vision processing to make all the colors we see. The blended colors form a color wheel from blue to green, green to yellow, yellow to red, and red to blue. These follow the familiar spectral colors until one reaches red to blue, at which point they pass through the non-spectral purples, including magenta. If one blends toward white in the center of the color wheel one creates pastel colors, or one can blend toward any shade of gray or black in the center for additional colors. This gives the full range of ten million colors we can see, of which only a few hundred on the outer rim are spectral colors. Instead of thinking of color as a frequency of light, it is more accurate to think of it as a three-dimensional space using y/b, r/g and brightness dimensions that is built with measurements from the four kinds of photopsins (photoreceptor proteins) in the eye. More accurately still, color goes through a fourth stage in which the colors surrounding an object are taken into consideration. The brain will automatically adjust the color actually seen to the color it would most likely take if properly illuminated under white light by reversing the effects of shadowing and colored lighting. For example, contextual interpretation can make gray look like blue in yellow context or like yellow in blue context. The brain can’t defeat this illusion until the surrounding context is removed. 4

One way to explore the meaning of qualia is by inverting them5. John Locke first proposed an inverted spectrum scenario in which one person’s blue was another person’s yellow and concluded that because they could still distinguish the colors as effectively and we could not see into their minds, completely “different” but equivalent ideas would result. Locke’s choice of yellow and blue was prescient, as we now know the retina sends a y/b signal to the brain which could theoretically be flipped with surgery, producing the exact effect he suggests6. Or we could design a pair of special glasses that flipped colors along the y/b axis, which would produce a very similar effect. (Let’s ignore some asymmetries in how well we discriminate colors in different parts of the color wheel.)7 Locke’s scenario presumes a condition present from birth and concludes that while an inverted person’s ideas would be different, their behavior would be the same. As a functionalist, I disagree. I would argue that whether the condition existed from birth or was the result of wearing glasses, the inverted person would see yellow the same as the normal person. This hinges entirely on what we mean by “different” and “same”; after all, no two people have remotely similar neural processes at a low level. By “same”, I mean what you would expect: if we could find a way to let one person peek into another person’s mind (and I believe such mind sharing is possible in principle and happens to some degree with some conjoined twins), then they would see colors the way that person did and would find that the experience was like their own. What I mean by this stance is that our experience of yellow and blue are not created by the eye but by the visual cortex, which interprets the signals to serve functional goals. But wait; certainly if one put one the glasses, yellow would become blue and vice versa right away. Yes, that is true, but how would our minds accommodate the change over time? Consider the upside-down vision experiment conducted by George Straiten in 1896 and again by Theodor Erismann and Ivor Kohler in 1955. Wearing glasses that inverted the perceived image from top to bottom and left to right, just as a camera flips an image, was disorienting at first, but after a week to ten days the world effectively appeared normal and one could even ride a motorcycle with no problem. The information available had been mapped to produce the same function as before. I believe an inverted y/b signal would produce the same result, with the colors returning to the way the mind used to see them after some days or weeks. Put another way, many of the associations that make yellow appear yellow are functional, so for the brain to restore functionality effectively it would subconsciously notice that the best solution is to change the way we interpret the incoming signals to realign them with their functions. For colors to function correctly, yellow needs to stand out more provocatively to our attention process than blue, and blue needs to be darker and calmer. We would remember how things used to be colored and how they felt, and our brains would not be happy with the new arrangement and would start to pick up on it and start making yellow things seem calm and blue things stand out. As our feelings toward the colors changed, our subconscious would become more inclined to map them back to the way they were. And if it were a condition we were born with, we would just wire physical yellow to psychological yellow in the first place. I don’t know if adult minds would necessarily be plastic enough for this effect to be perfect, but there is no reason to think they are any less capable of reversing a color flip than an orientation flip, though it would probably take longer as the feedback is much more subtle. Our brains probably have the plasticity needed to make these kinds of adjustments because we do continually adjust our interpretation of sensory information, for example to different levels of brightness. Our brains are built to interpret sensory data effectively. I am not saying that yellow has no real feel to it and that we just make it up as we go; quite the opposite. I am saying that yellow and all our qualia have a substantial computational existence at a high (but subconscious) level in the cortex which is flexibly connected to the incoming sensory signals, and that this flexibility is not only lucky but necessary. Knee-jerk-type reflexes are hardwired, but it is much more practical and adaptable for many fixed subconscious skills (like sensory interpretation) to develop in the brain adaptively to fulfill a role rather than using rigid neural connections. This kind of development has the additional advantage that it can be rewired or refined later for special circumstances, for example the help the remaining senses compensate when one sense is lost (e.g. through blindness).

Emotions

We have two kinds of qualia, sensory and dispositional. Sensory qualia bring information from outside the brain into it using sensory nerves, while dispositional qualia bring us information from inside our brain that tell us how we feel about ourselves. We experience both consciously, but kinds of experiences are created for us subconsciously. Dispositional qualia come in two forms, drives and emotions. Each drive and emotion has a complex subconscious mechanism that generates it when triggered by appropriate conditions. Drives arise without conscious involvement, while emotions depend on how we consciously interpret what is happening to us. The hunger drive is triggered by a need for energy, thirst by a need for water, sleep for rest, and sex for bonding and reproduction. Emotions are subconscious reactions to conscious assessments: a stick on the ground prompts no emotional reaction, but once we recognize it as a snake we might feel fear. When an event fails to live up to our expectations, we may feel sad, angry, or confused, but the feeling is based on our conscious assessment of the situation. We can’t choose to suppress an emotion because the subconscious mind “reads” our best conscious estimation of the truth and creates the appropriate reaction. But we can learn control our emotional reactions better by viewing our conscious interpretations from more perspectives, which is another way of saying we can be more mature. Both drives and emotions have been shaped by evolution to steer our behavior in common situations, and are ultimately the only forces that motivate us to do anything. The subconscious mind can’t generate emotions independent of our conscious assessments because only the conscious mind understands the nuances involved, especially with interpersonal interactions. And the rational, conscious mind needs subconsciously-generated emotional reactions because rational thinking needs to be directed towards problems worth solving, which is the feedback that emotions provide.

We have more emotions than we have qualia for emotions, which means many emotions overlap in how they feel. The analysis of facial expressions suggests there are just four basic emotions: happiness, sadness, fear, and anger.8 I disagree with that, but these are certainly four of the most significant emotional qualia. While there are no doubt good evolutionary reasons why emotions share qualia, but the most basic reason, it seems to me, is that qualia help motivate us to react in certain ways, and we need fewer ways to react than we need subconscious ways to interpret conscious beliefs (i.e. emotions). So satisfaction, amusement, joy, awe, admiration, adoration, and appreciation are distinct emotions, but share an uplifting happy feeling that makes us want to do more of the same. We distinguish them consciously, so if the quale for them feels about the same it doesn’t impair our ability to keep them distinct. Aggressive emotions (like happiness and anger) should spur us to participate more, while submissive emotions (like sadness and fear) should spur us to back off. We don’t need to telegraph all our emotional qualia thought facial expressions; sexual desire and romance are examples that have their own distinct qualia (and sex, like curiosity, is backed by both drives and emotions). We feel safe emotions (like happiness and sadness) when we are not threatened, and unsafe emotions (like anger and fear) when we are. In other words, emotions feel “like” what action they inspire us to take. Wikipedia lists seventy or so emotions, while the Greater Good Science Center identifies twenty-seven9. But just as we can see millions of colors with three qualia, we can probably distinguish a nearly unlimited range of emotional feelings by combining four to perhaps a dozen emotional qualia which correspond to a nearly unlimited set of circumstances. Sadness, grief, despair, sorrow, and regret principally trigger a sadness quale in different degrees, and probably also bring in some pain, anger, surprise, confusion, nostalgia, etc. Embarrassment, shyness, and shame may principally trigger awkwardness, tinged with sadness, anxiety, and fear. Similarly to sensory qualia, emotional responses recalled from memory tend not to be quite as vivid or convincing as they originally were. When we remember an emotion, we feed the circumstances to our subconscious, which evaluates how strongly we believe the situation calls for an emotional response. Remembered emotional reactions are muted by the passage of time, during which memories fade and lose their relevance and connection to now, and because memory only stores a small fraction of the original sensory qualia experienced.

When I was young, I tried to derive a purely rational reason for living, but the best I could come up with is that continuing to live is more interesting than dying. Unfortunately, this reason hinges on interest or curiosity, which I did not realize was an emotion. Unfortunately, as much as it irks committed rationalists, there is no rational reason to live or to do anything. Our reason for living, and indeed all our motivation, comes entirely from drives and emotions. The purpose of the brain is to control an animal, and the purpose of the conscious mind within it is to make top-level decisions well. The brain is free to pursue any course that furthers its overall function, and it does, but the conscious mind, being organized around the first-person perspective, must believe that the top-level decisions it makes are the free product of its thought processes, i.e. it must believe in its own free will. Humans have an unlimited capacity to pursue general-purpose thought processes using a number of approaches (which I have not yet described), and there is nothing intrinsic to general-purpose thoughts that would direct them along one path in preference to any other. In other words, we can contemplate our navels indefinitely. But we don’t; we are still physical creatures who must struggle to survive, so our minds must have internal mechanisms that will ensure we apply our minds to survive and flourish. If our first-person experience consisted only of awareness, attention, sensory qualia and thoughts, we would not prioritize survival and would soon die. Drives and emotions fill the gap through dispositional qualia. These qualia alter our mood, affecting what we feel inclined to do. They create and maintain our passion for survival and nudge us toward behaviors that ensure it. They don’t mandate immediate action the way reflexes do, because that would be too inflexible, but they apply enough “mental pressure” to “convince” us to do their bidding eventually. Drives impact our thoughts independent of any conscious thought, but emotions “read” our thoughts and react to them. So while our rational thinking does spur us toward goals, those goals ultimately come from drives and emotions. The purpose of life, from the perspective of consciousness, is to satisfy all our drives and emotions as best we can. We must check all the boxes to feel satisfied with the result. We sometimes oversimplify this mission by saying happiness is the goal of life, but the positive emotional feeling of joy is just one of many dispositional qualia contributing to our overall sense of fulfillment of purpose. People with very difficult lives can feel pretty satisfied with how well they have done despite a complete absence of joy.

Thoughts

Let’s take a closer look now at thoughts. Thoughts are the product of reasoning, which refers loosely to all the ways we consciously manage descriptive or referential information, i.e. information that is about something else. Thoughts are constructed using models that combine concepts and subconcepts, which are in turn based on sensory information. Although reasoning is conscious, it draws on subconscious sources like feelings, recollection, and intuition for information and inspiration. The subconscious mind provides these things either unbidden or in response to conscious queries. Consequently, many of our ideas and the models that support them arise entirely from intuitive, subconscious sources and appear in our minds as hunches about the way things are. We then employ these in a variety of more familiar conscious ways to form our thoughts about things. This is a highly iterative process that over a lifetime leads to what seems to be a clear idea of how the world works, even though it is really a very loose amalgamation of subconceptual and conceptual frameworks (mental models). Consciousness directs and makes top-level decisions, but is heavily influenced by qualia, memory, and intuition.

Unlike awareness, attention, and feelings, which are innate, reactive information management systems, thinking is the proactive management of custom information that an animal gathers from experience through its single stream of consciousness and multiple paths of subconsciousness. Our ability to think is innate, but how we think about things both specifically and generally is unlimited and unpredictable because how it develops depends on what we experience. We remember things we think about as thoughts, which are configurations of subconceptual and conceptual details. Subconsciously, we derive patterns from our experiences and store them away as subconcepts. Subconcepts group similar things with each other without labeling them as specific kinds. Consciously, we label frequently-seen groupings as kinds or concepts, and we associate details with them that apply to all, most, or at least some instances we encounter. Once we have filed concepts away in our memory, we can access them subconsciously, so our subconscious minds work with both subconcepts and concepts. Consciously, subconcepts all bubble up from memory and usually feel very familiar, though sometimes they only feel like vague hunches. Much of subconceptual memory imitates life in that we can imagine feeling something first hand even though we are just imagining doing so. Our sense of intuition also springs from familiarity, as it is really just an effort to recall explanations for situations similar to the one we currently find ourselves in. We can reason using both subconcepts and concepts, essentially pitting intuition against rationality. In fact, we always look for intuitive support of rational thinking, except for the most formalized rational thinking in strict formal systems. We also perceive concepts as the bubble up through memory. Concepts can be thought of as a subset of subconcepts which have been labeled and clarified to a higher degree. As we think about concepts and subconcepts pulled from memory we continually form new concepts and subconcepts as needed, which we then automatically commit to memory. How well we commit something to memory and how well we recall it later is largely a function of intently we rehearse and use it.

We have a natural facility to form mental models and concepts, and with practice we can develop self-contained logical models where the relationships are explicit and consistently applied. Logical reasoning leverages these conceptual relationships to develop entailments. Rigorous logical systems like mathematics can prove long chains of necessary conclusions, but we have to remember that all models are subconceptual at the lowest levels because concepts are built out of subconcepts. The axiomatic premises of formal logical models arguably need no subconceptual foundation, but if we ever hope to apply such models to real-world situations then we need subconceptual support to map those premises to physical correlates. From a practical standpoint, we let our intuition (meaning the whole of our subconscious and subconceptual support system) guide us to the concepts and purposes that matter most, and then we use logical reasoning to formalize mental models and develop strong implications.

Logical reasoning itself is a process, but concepts are functional entities that can represent either physical entities or other functional entities. Concepts can be arbitrarily abstract since function itself is unbounded, but functional concepts typically refer to procedures or strategies for dealing with certain states of affairs within certain mental models. The same concept can be applied in different ways across an arbitrarily large range of mental models whose premises vary. Consequently, concepts invariably only specify high-level relational aspects, and often with built-in degrees of freedom. Apples don’t have to be red, but are almost certainly red, yellow, green or a combination of them, though in very exceptional cases might be colored differently still. Concepts are said to have prototypical properties that are more likely to apply in generic mental models than more specific ones. As a functional entity, a concept or thought has its own noumenon, and because it is necessarily about something else (its referent), that referent also has its own noumenon. We think about the thought itself via phenomena or reflections on its noumenon, and additionally we only know of the referent’s noumenon through phenomena about it. Our awareness of our knowledge is hence a phenomenon, even though noumena underlies it (including functional and not just physical noumena). Just as with physical noumena, we can prove the existence of functional noumena (thoughts, in this case) by performing repeated observations that demonstrate their persistence. That is, we can think the same sorts of thoughts about the same things in many ways. Persistence is arguably the primary attribute of existence, so the more we observe and see consistency, the more it can be said to exist. Things exist functionally because we say they do, but physical existence is unprovable and is only inferred from phenomena. While physical persistence means persistence in spacetime, functional persistence means persistence of entailment: the same causes yield the same effects. Put another way, logic is inherently persistent, so any logical configuration necessarily exists functionally (independent of time and space).

All thoughts fall into one of two camps, theoretical or applied. Thoughts are comprised of information, and while information is always a functional construct, its function is latent in theoretical information and active in applied information. We process theory and application quite differently because theory is mostly conceptual while application is mostly subconceptual. Subconceptual thought is one level deep; we look things up by “recognizing” them as appropriate. So subconceptual “theories” consist of direct, “one-step” consequences: using recall we can search all the conceptual models we know to see if we can match a starting condition to an outcome without thinking through any steps one might need to actually employ the model. For example, if we need to go to work, we will recall that our car is appropriate without any thought as to why or how we drive it. But if we need to compare our car to foot, bike, motorcycle, bus, or car service, for example, we would use logical models that spelled out the pros and cons of each. Logical models, aka conceptual theories, decompose problems into explanatory parts. Application, on the other hand, is mostly a matching operation, which is why we usually do it subconceptually rather than devising a conceptual way to do it. We have a native capacity to align our models to circumstances and to keep them aligned by monitoring sense data as we go. Similarly, the easiest way to teach something is to demonstrate it, which taps into this native matching capacity. Monkey-see-monkey-do has been demonstrated at the neuron level via mirror neurons, which fire when we perform an action we see someone else do. Alternately, one could verbally teach applied information via a conceptual theory. For example, why a cyclist must countersteer to the left to make a right turn can be explained via physical theory. But few cyclists ever learn the theory. Like most applied knowledge, it is easily done but not easily explained. This exemplifies why there is no substitute for experience; conceptual theories are just the tip of the iceberg of all the hands-on knowledge and experience is required to do most jobs well. Consequently, most of our basic knowledge comes from doing, not studying, which means development of subconceptual rather than conceptual knowledge. As we do things, our subconscious innate heuristics will detect subconceptual patterns which we can then recall later.

In principle, theory is indifferent to application. We can develop arbitrarily abstract theories which may have no conceivable application. In practice, our time is limited and need to be productive in life, so most of our theories are designed with possible applications in mind. We can certainly develop theories purely for fun, but then we are amused, which also serves a function. We are unlikely to pursue theories we don’t find interesting, because satisfying curiosity is, as noted, a basic drive and emotion. But my point is just that theory can proceed in any direction and stand on its own without regard to application. Application, on the other hand, cannot proceed in any direction but must make accommodations to the situation at hand. Application done purely subconceptually makes a series of best-fit matches from stored knowledge to current conditions. Application done with the support of theories, which are conceptual, will also make a series of best-fit matches. First, we subconsciously evaluate how well the current circumstance fit models we know to pick the best model. We further evaluate ways that model might fit and doesn’t fit to establish rough probabilities that the model will correctly predict the outcome. Although theories are open-ended, application requires commitment to a single decision at a time. Given everything that can go wrong picking a model, fitting it to the situation, and extrapolating the outcomes, how can we quickly and continuously make decisions and act on them? The answer is belief. Beliefs must be general enough to support quick, confident decisions for almost any situation where a belief would be helpful, but specific enough to apply to real situations correctly. I said above that thoughts are comprised of ideas and beliefs: uncommitted thoughts are ideas, and committed ones are beliefs. Belief is also called commitment, opinion, faith, confidence, and conviction. The critical distinction between ideas and beliefs is that belief, like emotions, is a subconscious reaction to conscious knowledge. We feel commitment as a quale similarly to happiness and sadness, but it makes us feel determined, even to the point of stubbornness. We won’t act without belief, and conversely, belief makes us feel like acting, and acting confidently at that. Because belief has to pass through two evaluations, one conscious and rational and the other subconscious (which we then experience consciously via the feeling of commitment), the rational and subconscious components can get out of sync when more information becomes available. It sounds redundant, but we have to believe in our beliefs; that is, we have to rationally endorse what we feel. Our subconscious mind will resist changing beliefs because the whole value of beliefs comes from being tightly held. Trust, and trust in beliefs, must be earned over time. We are consequently prone to rationalizing, which is the creation of false reasoning that deflects new knowledge while supporting emotional loyalties. Ironically, rationalizing (in this sense) is an irrational abuse of rational powers. While this loyalty to beliefs is a handy survival mechanism, it also makes us very susceptible to propaganda and advertising, which seek to monopolize our attention with biased information to control our minds.10

The effectiveness of theories and their degree of applicability are matters of probability, but belief creates a feeling of certainty. This raises the question of what certainty and truth really are. Logically, certainty and truth are defined as necessities of entailment; that which is true can be proven to be necessarily true from the premises and the rules of logic. One could argue that logical truths are tautologies and are true by definition and so are not telling us anything we didn’t know would follow from the premises. Usually, though, when we think about truth we not concerned so much with the logical truths of theory, whose may spell things out perfectly, but with the practical truths of applied knowledge. We can’t prove anything about the physical world beyond all doubt or know anything about the true nature of noumena, because knowledge is entirely a phenomenal construct that can only imperfectly describe the physical world. However, with that said, we also know that an overwhelming amount of evidence now supports the uniformity of nature. The Standard Model of particle physics professes that any two subatomic particles of the same kind are identical for all predictive purposes except for occupying a different location in spacetime.11. This uniformity translates very well for aggregate materials at human scale, leading to very reliable sciences for materials, chemistry, electricity, and so on. Models from physical science make quite reliable predictions so long as nothing happens to muck the model up. If something unexpected does happen, we can usually identify new physical causes and adjust or extend the models to restore our ability to predict reliably. Circumstances with too many variables or that involve chaotic conditions are less amenable to prediction, but even in these cases models can be developed that do much better than chance. If we believe something physical will happen, it means we are sufficiently convinced that the model we have in mind is applicable and will work. So the purpose of belief is to enable us to act quickly, which we feel subjectively as confidence. Because the goal is to be able to apply the model for all intents and purposes that are reasonably anticipated, our standard for truth is pragmatic rather than logical and so can admit exceptions, which can then be dealt with. The scope of what is reasonably anticipated is usually more of a hunch than something we reason out. This is a good strategy most of the time because our hunches, whose scope includes the full range of our subconscious intuition, are quite trustworthy for most matters. Most of what we have to do, after all, is just moving about and interacting with things, and our vast experience doing this gives us very reliable intuitions about what we should believe about our capabilities. Logical reasoning, and consciousness in general, steps in when the autopilot of intuition doesn’t have the answer.

Many of the considerations we manage as minds concern purposes, which have no physical corollary. In particular, whether we should pursue given purposes are ethical considerations, so we need to understand what drives ethics. We have preferences for some things over other due to dispositional qualia, which as noted above include drives and emotions. Like belief, ethics are a subconscious reaction to conscious knowledge, and so critically depend on both subconscious and conscious factors. But disposition itself is not rational, so ethics are ultimately innate. Considerable evidence now exists supporting the idea that ethical inclinations are innate12, and it stands to reason that behavioral preferences as fundamental as ethics would be influenced by genes because reason alone can’t create preferences. To understand ethical truth, then, we need only understand these inclinations. Note that inclinations don’t lead to universal ethics; ethics need to be flexible enough to adapt to different circumstances. We don’t yet have a sufficient understanding of the neurochemistry of the mind to unravel the physical basis of any qualia, let alone one as complex as ethics, but evolutionary psychology suggests some things. Our understanding of evolution suggests that we should feel an ethical responsibility to protect, with decreasing priority, ourselves, our family, our tribe, our habitat, our species, and our planet. I think we do feel those responsibilities in just that decreasing order, which I can tell by comparing them to each other to see how I would prioritize them. While these ethical inclinations are innate, we build our beliefs about ethics from ideas we learn consciously, which we then accept subconsciously and consequently feel consciously. So, as with any belief, our feelings can get out of sync with our thoughts. Many people believe things that contradict their ethical responsibilities without realizing it because they have either not learned enough to know better or have been taught or accepted false information. So adequate and accurate information is essential to making good ethical decisions.

The Self

I’ve spoken of self and not-self-information, but not about self-awareness. Is self-awareness unavoidable or can a conscious agent get by in the world without noticing themselves? First, consider that all animals with brains exhibit behavior characteristic of awareness, but this doesn’t imply they all have the conscious experience of awareness. Ants, for example, act as if they were aware, but with such small brains, it may seem more plausible that their behavior is simply automatic. And yet ants are self-aware: “ants marked with a blue dot on their forehead attempted to clean themselves after looking in the mirror while ants with brown dots did not.”1314 You can’t fake this: the ants knew exactly where their own bodies were and could reverse information about their bodies from a mirror. From a behavioral standpoint, they are self-aware. But this still doesn’t imply they experience this awareness. Without anthropomorphizing, I would say that an aware (and self-aware) agent experiences things if its brain uses an intermediate layer between sensation and decision (essentially a theater of consciousness) that makes decisions based on a simplified representation of the external world rather than on all data at its disposal. This “experiencing” layer would necessarily acquire a first-person perspective in order to interpret that simplified world and keep it aligned with the external world. We can’t actually tell from behavior whether the ant brain has this additional layer, but I would argue that it does not. The reason is that arbitrarily complex behavior can be encoded as instinct, and in very small animals this strategy is the most effective route. They do detect foreign dots on their heads and interact with them, so their brains have advanced visual and motor skills, but they do this only as a consequence of instincts that help them preserve body integrity. Only a handful of more advanced animals (e.g. apes, elephants, dolphins, Corvus) can pass the mirror test to identify themselves, which they do not as a direct consequence of instinct but because they have an agent layer that experiences self-awareness. Probably all vertebrates and some invertebrates have some degree of consciousness including awareness, attention, and some qualia, and probably all mammals and birds have some measure of emotions and thoughts. Those that can pass the mirror test have sufficient agency to model their physical selves and probably their mental selves as well. Still, though some animals have abilities that can match ours, and many have senses and abilities that surpass ours, something special about human consciousness sets us apart. That something, as I previously noted, is our greater capacity for abstraction, the ability to decouple information from physical referents, which lets us think logically independent of physical reality. And when we think about self-awareness, we are more concerned about this abstract introspection into our “real” selves, which is our set of feelings, desires, beliefs, and thoughts, than with our body awareness.

The idea that the consciousness subprocess is a theater that acts as an intermediate layer between sensation and decision raises the question of who watches that theater. The homunculus argument suggests that a tiny person (homunculus) in the brain watches it, which humorously implies an infinite regress of tinier and tinier homunculi. Though we don’t feel someone else is inside our self, and so on ad infinitum, we do feel like our self is inside us watching that theater. Explaining it away as an illusion is pointless because we already know that the way our minds interpret the world is just a representation of reality and not reality itself. It only counts as an illusion if the intent is to deceive or mislead us, and, of course, the opposite is the case: our senses are designed to give us reliable information. What is happening is that the qualia fed into the consciousness process represent the outside world using an internal representation that simplifies the world down to what matters to us, i.e. down into functional terms. The internal representation bears no resemblance to the external reality. How could it, after all? One is physical and the other is functional (information). But for consciousness to work effectively to bring all those disparate and incomplete sources of information together, it must create the lusion (to coin a word opposite to illusion) that all this information accurately represents the external world. To be completely accurate, it provides this information in two forms, spatial and projected. We feel our bodies themselves spatially through thousands of nerves carrying information to our brains from actual points in space. We interpret our bodies in a spatially omnipotent way, although these nerves actually convey rather limited information. This lusion seems real, though, because we have many body-sense qualia keeping us updated continuously and we feel it gives us seamless, complete awareness of our bodies in 3-D.

We have no such spatial beyond our body, but we have projected senses. Sight and hearing give our eyes and ears information about distant objects. To interpret it, we have a head-centric agent that builds a projection-based internal model of the external world. Smell and touch provide additional information from airflow and vibrations. As with body senses that work together to create a seamless spatial model of the body, our projected senses work together to great a seamless projected model of the world. Eyes collect visual information the same way cameras do, so it should come as no surprise that vision interprets 2-D projections as a window into a 3-D world. Furthermore, binocular vision could in theory and does in practice achieve stereoscopic sight. The signal from a monocular or binocular video camera itself does nothing to facilitate the interpretation of images. We interpret at that data using a highly bespoke process that cherry-picks the information most likely to be relevant (e.g. lines of sharp contrast (boundaries)) and applies real-time recognition to create the lusion that one has correctly identified what one is looking at and can consequently interact with it confidently. The goal is entirely functional (i.e. to give us the competence to act), and our feeling that the outside world has “actually” been brought into our minds happens only because the consciousness process is “instructed” to believe what it sees. The resulting lusion is a fair and complete description of reality given “normal” senses, though we are abundantly biased about what counts as normal. Scientific instruments can extend our perception of the world down to the microscopic level (for example), but not by giving us new qualia. Rather, instruments just map information into the range of our existing qualia, which can create a new kind of fair and complete lusion when done well. Our sensory capacity remains constrained by the qualia our subconscious feeds our conscious. Also, we consciously commit to a single interpretation of a sensory input at a time15, demonstrating that sensory “belief” happens below the level of consciousness. Consciously, we go along with sensory belief so long as it is not contradicted, but if our recognition triggers any other match, as when a harmless stick starts to move like a snake, we will flip instantly to the new match. In practice, surprises are rare and we feel like we continuously and effortlessly understand what we are seeing. This is a pretty surprising result considering how complex a process real-time recognition is, but it is not so surprising once we appreciate the contribution of memory.

We are convinced by the lusion our senses present to us because it is integrated so closely with our memory. In fact, understanding is really a product of memory and not senses; our senses only confirm what we already (think we) know. We don’t have to examine everything about us closely because we have seen it all before (or stuff similar enough to it) and we are comfortable that further sensory analysis would only confirm what we already know. We do inevitably reexamine the objects we interact with in order to use them, but never more closely than is necessary to achieve the functions we have in mind because knowledge is a functional construct. If we do take the time to study an ordinary object just for fun or to pass time, this dedication of attention has still surpassed all others in that moment to become the one action that has the most potential to serve our overall functional objectives. In other words, we can’t escape our functional imperatives. If our senses don’t align with any memory (e.g. consider the inverted glasses example, or being unexpectedly swallowed a whale, etc.), we will be disoriented until we can connect senses to memory somehow. Our confidence in the seamless continuity of what we see is a function of the mind’s real world, which is the mental model (in our memory) of the current state of the physical world. Our sensory inputs don’t create that model, they only confirm it. The attention subprocess of consciousness (which is itself subconscious) stays alert for differences between what it expects the mind’s real world to be and what the senses provide. These differences are resolved subconsciously in real-time to prevent the simultaneous interpretations of one image into multiple objects despite the fact that any image is potentially ambiguous. The subconscious mind actively crafts the lusion we perceive, even though it can be tricked into seeing an illusion. The important thing is that the match between lusion and reality is generally very reliable, meaning we can act on it with confidence. Our whole suite of qualia continuously confirm that the mind’s real world is the actual world by making it “feel like” it is. As I work by the window, cars travel up and down my street and I hear them before I see them. I know about what they will look like before I see them, and I generally only see them peripherally, but I am confident in my seamless mental model despite being surprisingly short on detailed information.

Back to the original question, is there a homunculus viewing these material and projected views of the world? Yes, there definitely is, and it is the consciousness subprocess. This subprocess is technically quite distinct from a small person because it is just one subprocess in a person’s brain and not a whole person. The confusion comes because we identify our conscious minds with the bodies that use them to preserve the lusion. But we don’t need to extrapolate another body for the mind; we know minds are disembodied functional entities with no physical substance. So there is no regress; consciousness was always a disembodied agent. It is only awkward for us to conceive of ourselves as having both physical and functional components to ourselves if we are militantly physicalist. Using common sense, we have had no trouble with this dichotomy, probably for thousands and even millions of years. It is not regress to say that consciousness is a subprocess of the brain with its own internal model of the world, it is just a statement of fact. Consciousness is designed to see itself as an agent in the world rather than as a collector and processor of information, and the subconscious is designed to spoon-feed consciousness information in the forms that support that lusion. The result is that consciousness has a lusion that mirrors the outside world, and interacting with the lusion makes the body perform in the real world, much like pulling on puppet strings.

The Stream of Consciousness

We’ve taken a closer look at some of the key components, but haven’t yet hit some of the bigger questions. I have pointed out how consciousness separates self and not-self-information. I described why qualia need to be distinguishable from each other and also how stronger custom feelings inspire stronger reactions. I reviewed how emotions and thoughts work. And I described how the self is an informational entity that is fed a simulation (a lusion) that lets us engage in virtual interactions that power physical interactions in the external world. But I still haven’t tied together just why consciousness uses awareness, attention, feelings, and thoughts to achieve its goal of controlling the body. It comes down to a simple fact: there is only one body to control. This has the consequence that the whatever algorithm is used to control the body, it must settle on just one action at a time (if one takes the direction of all bodily parts as a single, coordinated action). For this reason, I call this core part of consciousness that does the deciding the SSSS, for single-stream step selector. The SSSS must be organized to facilitate taking the most advantageous step appropriate in every circumstance it encounters. This is not the kind of problem modern-day procedural computer programs can solve because it must simultaneously identify and address many goals whose attainment covers many time scales. The evolutionary goal of survival, which requires sustenance and reproduction (at least), is the only long-term goal, but it must be subdivided into many sub-strategies to outperform many competitors.

From a logical standpoint, before we consider the role of consciousness, let’s look at Maslow’s Hierarchy of Needs, which Abraham Maslow proposed in 1943. He outlined five levels of needs which must be satisfied in order for a person to thrive: physiological, safety, belongingness, esteem, and self-actualization. All of these needs follow necessarily and logically from the single evolutionary need to survive, but it is not immediately apparent why and how. I would put his first two needs at the same level. Physiological needs, such as food, shelter, and sex, are positive goals and their corresponding negative goals, which are defensive or protective measures, are Maslow’s safety needs. All animals must achieve these positive and negative goals to survive. The next two needs are belongingness and esteem, which are only relevant for social species. Individuals must work together in a social species to maximize fitness of the group, so whatever algorithm controls the body must incorporate drives for socialization. Belongingness refers to the willingness to engage with others, while esteem refers to the effectiveness of those engagements. Addressing physiological and safety needs benefits survival directly, but the benefits of one socialization strategy over another are indirect and can take generations to demonstrate their value. This value has been captured by instincts that make us inclined to favor socialization behaviors that have been successful in the past. We may feel that our social behavior is mostly rational, but it is mostly driven by emotions, which are complex instinctive socialization mechanisms. We must attend to these first four needs to prevent problems, so they are called deficiency needs. The last need, self-actualization, is called a growth need because it inspires action beyond a deficiency need. For maximum competitiveness, all animals must both avoid deficiencies and desire gains for their own sake, which I described above as consequences and curiosity. But self-actualization goes beyond curiosity to provide what Kurt Goldstein originally called in 1934 “the driving force that maximizes and determines the path of an individual”16. We don’t really have any concrete evidence to support a self-actualization drive, but it does stand to reason that instincts would evolve to push us both to cover deficits and seize opportunities to flourish and grow, and the latter should provide more competitive edge than the former. Such a drive would inspire people to achieve overall purposes in life, i.e. to seek meaning in life through certain kinds of activities, and all people feel a pull to satisfy such purposes. It is safe to say that the reasons we imagine drive us toward those purposes are rationalizations, meaning that we devise the reasons to explain the behavior rather than the other way around. But it doesn’t matter whether we know this; we are still inspired to lead purpose-driven lives that go above and beyond apparent survival benefit because the self-actualization drive compels us to excel and not just live.

Consciousness may not be the only solution that can satisfy these needs effectively, but it is the one nature has chosen I’m going to list the main reasons consciousness is well suited to meet these needs, roughly from most to least important.

  1. Divide and conquer. Some decisions are more important than others, so any control algorithm needs to be able to devote adequate resources and focus to important decisions and less to mundane ones without becoming confused. Consciousness solves this in many ways, but most significantly it bifurcates matters worthy of top-level consideration from those that are not by cordoning the latter group off in the subconscious outside conscious awareness. Secondly, it uses qualia of different levels of motivating power to focus more attention on pressing needs. And third, it provides a medium for conceptual analysis, which divides the world up into generalized groups about which one has useful predictive knowledge.

  2. Awareness and attention. Important information can arrive at any moment, so any control algorithm should stay alert to any and all information the senses provide. At the same time, computing resources are finite, so senses should specialize in just the kinds of information that have proven the most useful. Consciousness achieves this goal admirably with awareness and attention. Awareness keeps all qualia running at all times, while attention leverages subconscious algorithms to notice unusual inputs and also lets us direct conscious thoughts along the most promising pathways. Sleep is a notable exception and so it must provide enough benefits to warrant the cost.

  3. Incorporating feedback. Let’s look first at the selection part of single-stream step selection. The algorithm must pick one action instead of others, and it has to live with the consequences immediately. This is equivalent to saying it is responsible for its decisions. Consciousness creates a feeling of responsibility, not because we controlled what we did but because future behavior builds on past decisions. This side-steps the question of free will for now; I’ll get back to that. The important point is that consciousness feels like it caused decisions because this feeling of responsibility is such a great way to incorporate feedback.

  4. Single-stream. Now let’s think about single-stream. It is not a coincidence that our decisions must happen one at a time and we also have a single stream of consciousness. We know the subconscious does many things in parallel, but we can’t consciously think in parallel, and we must, in fact, resolve concepts down to one thing at a time to think further with them. The reason is that it is a strong adaptive advantage to be of one mind when it comes time to make a decision. If we have two or more competing lines of thought which simultaneously have our attention, then when we come to the moment of decision we will need to narrow them down to one since we have only one body. But the problem is, every moment is a possible moment of decision. If evolved creatures could make decisions on their own timetable, then it would be faster to think through many scenarios simultaneously and then pick the best. But not only do we not get to choose those moments, we actually make decisions every moment. We are always doing something with our bodies, and while much of that is not consuming much of our conscious attention because we have it on subconscious “autopilot,” we are committing to actions continuously, and it wouldn’t do to be unsure. Consequently, it is better for our conscious perspective to be one which perceives a single train of thought making the best decisions it can with the available information. This means that alternative strategies must be simulated serially and then compared. While this is slower than thinking in parallel, our subconscious helps us reap some benefits of parallel thinking by giving us a good feel for alternatives and by facilitating switching between different lines of thought.

  5. Qualia help us prioritize. Qualia help us keep our many goals straight in our minds simultaneously. If the primary purpose of consciousness is to make decisions, but it needs to prioritize many constantly competing goals, it needs an easy way to prioritize them without bogging down the reasoning process. Qualia are perfect at that because they force our attention to the needs our subconscious considers most pressing. It is not a good idea to leave the decisions about what to think about next entirely up to the rational mind, because it has no motivation to focus on important matters.

The Hard Problem of Consciousness

Does the above resolve the “hard” problem? The problem is only hard if we aren’t willing to think of consciousness as an experience created by the subconscious. That’s odd, because we know it has to be. There is clearly no physical substance to the conscious mind other than the neurochemistry supporting it. It must therefore be a consequence of processes in the brain. We know that our senses (including complex processing like 3-D vision), recognition, recollection, language support, and so on require a lot of computation of which we have no conscious awareness, so we have to conclude the subconscious does the work and feeds it to us in the form of our first-person perspective. What separates this first-person perspective from what a zombie or toaster would experience (i.e. nothing), and what ultimately gives our qualia and other experiences meaning and “feel,” is their function. Experience is information, which means it enables us to predict likelihoods and take actions. The whole feel and excitement of experience relate to the potential value of the information. If it were white noise, we wouldn’t care and all of experience would dissolve into nothingness. So the subconscious doesn’t just provide us with pretty accurate information through many qualia, it also compels us to care about that information (via drives, emotions, and beliefs) roughly in proportion to how much it matters to our continued survival. As I said, qualia feel like what they inspire us to do. The feel of qualia is just the feel of the survival drive itself broken down to a more granular level.

So my contention is that information is the secret sauce that makes experience possible, specifically because it makes function possible. Chalmers is open to information being the missing link:

Information seems to be a simple and straightforward construct that is well suited for this sort of connection, and which may hold the promise of yielding a set of laws that are simple and comprehensive. If such a set of laws could be achieved, then we might truly have a fundamental theory of consciousness.

It may just be…that there is a way of seeing information itself as fundamental.17

Information is fundamental. While it is ultimately physical systems that collect and manage information in a physical world, one can’t explain the capabilities of these systems in physical terms because they leverage feedback loops to create informational models. The connection from the information to their application just becomes too indirect and abstract to track physically. But they can be explained in functional terms. Our first-person experience of consciousness is a kind of program running in the brain that interprets awareness, attention, feelings, and thoughts as the components of a unified self. We are predisposed by evolution to think of ourself as a consistent whole, despite it really being a mishmash of disparate information. Millions of years of tweaks bring it all together to make a convincing lusion of a functional being acting as an agent in the world.

The first-person perspective of consciousness is a good and possibly ideal solution for controlling animal bodies. The reason is that it links function to form using effectiveness as the driving force. Specifically, it continuously aligns representations in the brain to external reality by providing conscious rewards for behavior that lines up with survival needs. Survival is a hard job and sounds onerous, but we enjoy doing it when we do it well because of these rewards. The other side of the coin, of course, is that we dislike it when we do it badly, which gives us an incentive to up our game. It isn’t the kind of control system one could build with if-x-happens-then-do-y logic. Rather, it is self-balancing and autopoietic. Autopoiesis refers to an organism’s ability to reproduce and maintain itself, but in the case of the brain’s top-level control system it more generally refers to its ability to manage many simultaneous sources of information and many goals (polytely) gracefully. Subjectively, we feel different priorities fighting for our attention in different ways, leading us to prioritize them as needed. I don’t know if first-person subjectivity is the only way to solve this problem gracefully and well, but I suspect so. In any case, it evolved and works and we only need to explain how. Subjectively, consciousness has its own functional, high-level way of seeing the world that interprets things in terms of their perceived functions, dwells on what it knows, and issues orders to the body to act.

We can only consciously think a single stream, but it seems likely that all the algorithms of the subconscious are parallel. Thousands to millions of parallel paths are vastly more powerful than a single path, but the single path of consciousness draws on many subconscious algorithms to become much more than a single logical path could. Consciousness must logically resolve into a single stream so we can come to one decision at a time to guide one action at a time. Theoretically, a brain could maintain multiple streams of consciousness before the moment of decision, but mother nature has apparently found that this creates a counterproductive internal conflict because we find we can feel or think just one non-conflicting thing at a time. Note that severing the hemispheres via corpus callosotomy possibly forces such a split in some ways, at least temporarily. But split-brain patients report feeling the same as before the split, and can still use either hemisphere to sense objects presented only to one (e.g. to the left eye, which is controlled by the right hemisphere). It is now theorized that the thalamus, which is not separated by this operation, mediates communication between the hemispheres18. It is also possible that neural plasticity can either regrow cortical pathways or repurpose subcortical (e.g. thalamic) pathways to maximize interhemispheric integration19. But I would just emphasize that the stream of consciousness is linked to the SSSS (single-stream step selector), so having more than one stream of consciousness would make coordinated action impossible, which would at the very least require one stream to dominate the other.

Multiple personalities, now called dissociative identity disorder (DID), arguably creates serial rather than parallel streams of consciousness. This strategy usually arises as an adaption for dealing with severe childhood abuse. As a protective mechanism, DID patients’ identities fragments into imaginary roles as an escape from their true circumstances. Although their situation has driven them to invest an unrealistic level of belief in these alter personas, which in turn suppresses belief in their real persona, I don’t consider this condition to represent true multiple serial streams of consciousness, but rather just a single, confused stream. All of us live in worlds we construct with our mental models, and that includes hopes and dreams not at all apparent from our physical circumstances but constructed from our interpretation of the fabric of social reality. When we embrace personality traits as our own, we are trying on a role to see how it fits. The way we behave then comes to define us, both from our own perspective and from others. But it isn’t all we are; we always have potential to change, and, in any case, our past behavior may be indicative of but does not constrain our future behavior. Physical events don’t actually repeat themselves; only patterns repeat, and patterns have boundaries that are always subject to interpretation. Given this inherent fluidity of function, is it meaningful to speak of a unified self?

Our Concept of Self

I have spoken of how our knowledge divides into knowledge about the self and not-self, and of how consciousness creates a perspective of an agent in the world, which is the self. But I haven’t spoken about what we know about our own self, i.e. about self-knowledge. Self-knowledge is clearly an exercise in abstract thinking because while all higher animals have some capacity to experience simulations of themselves acting in the world, knowledge of self goes further to attribute situation-independent qualities to that self. Self-knowledge arises mostly from the same submerged iceberg of subconscious knowledge that underlies all our knowledge and then leverages the same kind of generalization skills we use to process all conceptual knowledge. But it has the important distinction of being directed at ourselves, the machine doing the processing. Having the capacity to reflect on ourselves is a liability from an evolutionary standpoint because it presents evolution with the additional challenge of ensuring that we will be happy with what we see. Since we have been designed incrementally, this problem has been solved seamlessly by expanding our drives and emotions sufficiently over time to keep our rational minds from wandering excessively. Humans are such emotional creatures, relative to other animals, because we have to be persuaded to dedicate our mental powers sufficiently toward survival and all its attendant needs. This persuasion doesn’t stop with short-term effects, but influences how we model the world by leading us to adopt beliefs and convictions for which we will strive diligently. Those beliefs may be rationally supported, but they are more usually emotionally supported, often based solely on social pressure, because we are adapted to put more stock in our drives, emotions, and the opinions of others than our own ability to think.

So we can see ourselves, but we are biased in our perspective. When well-adjusted, we will accept the conflicting messages from our emotional, social, and rational natures to see a unified and balanced entity. While it is not hard to see why it is adaptive for us to feel like unified agents, doesn’t our ability to think rationally highlight all the diverse pieces of our minds, which could alternately support a fractured view of ourselves? Yes, and it does. We see many perspectives and are often torn over which ones to back. We can doubt whether the choices we make even accurately represent what we are or are just compromises we have to make because we don’t have enough time or information to discover our true preferences. But our persistent sense of self is generally not shaken by such conflicts. Our feeling of continuity with our past creates for us at any given moment a feeling of great stability: we may not be able to picture all the qualities we associate with ourselves as we probably have not given any of them much active thought recently, but we know they are there. Themes about ourselves float in our heads in such a way that we can sense that they are there without thinking about them. This “floating”, which applies to any kind of memory and not just thoughts about ourselves, feels like (and is) thousands of parallel subconscious recollections helping to back up our conscious thoughts without our having to focus directly on them. Subconscious thoughts not brought into focus contribute to our conscious thought by giving us more confidence in the applicability of any intuition or concept to the matters at hand. We know they are there because we can and often do explore such peripheral thoughts consciously, which reinforces our sense that our whole minds go much deeper than the thoughts we are executing at any given moment in time. Note that this is both because the subconscious does so much for us and because thoughts are informational structures in their own right — functional entities — and not just steps in a process, and so have a timeless quality.

So do we know ourself? Knowledge is always phenomenal, not noumenal, and so presents a perspective or representation of something without explaining the whole thing. We know many things about ourselves. Intuitively, we know ourselves through drives, emotions, our responses to social situations, and even our habitual thinking patterns. Rationally, we know ourselves from impartial considerations of the above and our abilities. It isn’t a complete picture — nothing ever is — but it is enough for most of us to go on. We see ourselves both as entities with certain capabilities and potential and as accomplishers of a variety of deeds. We know many things strongly or for sure, many more things still we only suspect or know little about, and of everything else, which is surely a lot, we know nothing. But it is enough. We always know enough to do something, because time marches on and we must always act. Is it wrong that we don’t spend more time in self-contemplation to ferret out our inner nature? Aside from the obvious point that what we do with our minds can’t be either right or wrong but simply is what it is, we can’t break with our nature anyway. Our genomes have a mission which has been reinforced over countless generations, and it makes us strongly inclined to pursue certain objectives. “I yam what I yam and tha’s all what I yam.”20 So while I would argue that we can direct our thoughts in any direction, we can’t fundamentally alter the nature of our self, which is determined by nature and nurture, though we can alter it somewhat over time by nurturing it in new directions.

Knowing now that we look at ourselves from a combination of intuitive and rational perspectives, what do we see? Mostly, we see an agent directing its body, both in practice and as the lead character in stories we tell ourselves. The self is a fiction just as all knowledge is a fiction, but that doesn’t make knowledge or the self unreal; both are real as functional entities: we are real because we can think of ourselves as real. As I said above, self and not-self information is a fundamental distinction in the brain, so far from being illusory, it is profoundly “lusory”. Our concept of self develops from turning our subjective attention inward, making our subjective capacity the object of study: I as subject study me as object. Some philosophers claim that the self can’t understand the self, because self-referential analysis in inherently circular, but this is not true. The study of anything creates another layer, a description of the phenomenon that is not the object (noumenon) under study itself. But if what we know about the self or mind is created by the mind, is it really knowledge? Can we escape our inherent subjectivity to achieve objectivity? Yes, because objective knowledge is never absolute, it is functional — it is knowledge if it makes good predictions. We don’t actually have to know anything that is incontrovertibly true about the mind; we only need to make generalizations that stand up consistently and well. So it doesn’t matter if the theories we cook up are far-fetched or bizarre from some perspectives as that can be said of all scientific theories. Theories about our subjective lives attain objectivity if they test well. This doesn’t mean we should throw every theory we can concoct at the wall to see if it sticks; we should try to devise theories that are consistent with all available knowledge using both subjective and objective sources. We can’t afford to ignore our subjective knowledge about the mind because almost everything we know about it emanates from our subjective awareness of it.

Deriving an Appropriate Scientific Perspective for Studying the Mind

I’ve made the case for developing a unified and expanded scientific framework that can cleanly address both mental and physical phenomena. I’ve reviewed how scientific physicalism squeezed out the functional view. And I’ve reviewed how function arose in nature and led to consciousness. This culminates in a new challenge: we need to develop an appropriate scientific perspective to study the mind, which will also impact how we view the study of science at large. I will follow these five steps:

1. Our Common Knowledge Understanding of the Mind
2. Form & Function Dualism: things and ideas exist
3. The nature of knowledge: pragmatism, rationalism and empiricism
4. What Makes Knowledge Objective?
5. Orienting science (esp. cognitive science) with form & function dualism and pragmatism

1. Our Common Knowledge Understanding of the Mind

Before we get all sciency, we should reflect on what we know about the mind from common knowledge. Common knowledge has much of the reliability of science in practice, so we should not discount its value. Much of it is uncontroversial and does not depend on explanatory theories or schools of thought, including our knowledge of language and many basic aspects of our existence. So what about the mind can we say is common knowledge? This brief summary just characterizes the subject and is not intended to be exhaustive. While some of the things I will assume from common knowledge are perhaps debatable, my larger argument will not depend on them.

First and foremost, having a mind means being conscious. Consciousness is our first-person (subjective) awareness of our surroundings through our senses and our ability to think and control our bodies. We implicitly trust our sensory connection to the world, but we also know that our senses can fool us, so we’re always re-sensing and reassessing. Our sensations, formally called qualia, are subjective mental states like redness, warmth, and roughness, or emotions like anger, fear, and happiness. Qualia have a persistent feel that occurs in direct response to stimuli. When not actually sensing we can imagine we are sensing, which stimulates the memory of what qualia felt like. It is less vivid than actual sensation, though dreams and hallucinations can seem pretty real. All qualia are strictly functional, but about different kinds of things. While our sensory qualia generate properties about physical things (forms), our drives and emotional qualia generate properties about mental states (functions). Fear, desire, love, hunger, etc., feel as real to us as sight and sound, though we recognize them as abstract constructions of the mind. As with sensory qualia, we can recall emotions, but again, the feeling is less vivid.

Even more than our senses, we identify our conscious selves with our ability to think. We can tell that our thoughts are happening inside our heads, and not, say, in our hearts. It is common knowledge that our brains are in our heads and brains think1, so this impression is a well-supported fact, but why do we feel it? Let’s say we call this awareness of our brains “encephaloception”. It is a subset of proprioception (our sense of where the parts of our body are), but also draws on other somatosenses like pain, touch, and pressure. Encephaloception pinpoints our thoughts in our heads because we need to know the impact pain, motion, impact, balance, etc. have on our ability to think. Of course, our sense of vision and hearing are close to the brain, which further enhances the feeling that we think with our brains, but we can tell withing seeing or hearing.

But what is thinking? Loosely speaking it is the union of everything we feel happening in our heads. We mostly consider thinking to be something we do rather than something that happens to us, but the difference is rather subtle and will be the subject of a later section on free will. For now let’s just think of doing and happening as the same sort of thing. We experience a continuous train of images, events, words, and other mental constructs flowing together in a connected way that create what feels like internal and external worlds, though we know they are entirely imaginary. With little conscious effort it feels like we are directing our own movie. Our minds just match what we see to similar things and situations we have seen before and get a feel for what will probably happen next based on how much familiarity we have. While most of our thinking involves the sensory and event-based thoughts that comprise the mind’s real world, we also have other ways to think, notably through language, memory, and logical reasoning. Our innate gift for language lets us form trains of thought that are entirely abstracted from senses and events. Our ability to remember things relevant to our thoughts gives us an intuitive capacity to free associate in useful ways. Though intuition can be quite powerful, it essentially amounts to making good use of our associative memory. We just prod our memories to recall patterns or strategies related to what we need and either right away or after a while useful recollections materialize in bursts of intuition. Finally, we can think logically, chaining ideas together based on logical relationships rather than just senses, language, and intuition. One more critical thinking skill is learning, which is the review and assessment of feedback to discover more useful patterns. Focused learning in one domain over months and years results in mastery, which is a combination of knowledge and motor skills that give us expertise with relatively little conscious thought.

I’ve listed some different kinds of thinking but that still doesn’t tell us what they are. We can feel, remember and learn from our sensory perception of the world, but but we can’t break it down subjectively. Our senses and memories either just happen or feel like they do our bidding, but we can’t explain how they work subjectively. But we can at least partially break down two of our subjective abilities: reasoning and language. We feel we have access to reasons and rules of logic to manipulate those reasons that we use to help us reach decisions. We carry a large number of mental models in our heads which describe simplified situations in which idealized objects interact according to logical rules, and we are always trying to apply these models to real-world situations that seem like good fits for them. When we find a good fit, we then believe that the implications we see in the mental model will also hold in the real world so long as the fit remains good. All of our cause-and-effect understanding of the world derives from this kind of logical modeling. We roughly know from common knowledge how we reason logically because we do this reasoning entirely consciously. We could not do it at all without great subconscious support from recognition, sensory and/or linguistic capacities, learning, and our natural facility to project models in our heads. But given that support, the final product, logical reasoning, is entirely conscious and anyone can explain their lines of reasoning.

Most of language just appears in our heads as we need it, but we can define every word in terms of other words. It is common knowledge that words are well-defined and are not circular and fuzzy. But how can this be? Since I’m sticking to common knowledge and not getting too technical, I’ll just say that we feel we know the concepts the words represent, and we know that dictionary definitions rather accurately describe those concepts to the appropriate degree of detail to explain them. We further know, though we may not realize it, that every definition is either physical or functional but not both. Physical things are ultimately only knowable through our senses, so they break down to into ways we can sense the objects. Functional things are ultimately only knowable through what they can do, so they break down into capacities of the objects. This linguistic division alone essentially proves that our world has a form and function dualism. But for both physical and functional things, words are functional entities — they are phenomena through which we can refer to the noumena — so the definitions and the words are tools we can use to achieve our functional aims. So we differentiate physical things from each other only because it is helpful to us for functional reasons to do so, not because there is any intrinsic reason to draw those lines. Ultimately words are supported purely by inexplicable subconscious support, which is either a sensory basis for physical things or a functional basis for functional things. That functional basis is ultimately ineffable: we distinguish methods that work from those that don’t based on experience. We can articulate specific sets of logical rules in formal systems that are perfectly articulated and certain, but they are not functional. Function requires an application, and application requires fitting, which is invariably approximate, and approximate is not certain. Though it can’t be explained at the lowest level logically, function can be explained as the consequence of finding patterns in data, fitting them to situations, and assessing the amount of function that results. In our brains, this happens subconsciously and so is beyond our ability to explain via common knowledge, but we know it when we see it.

2. Form & Function Dualism: things and ideas exist

We can’t study anything without a subject to study. What we need first is an ontology, a doctrine about what kinds of things exist. We are all familiar with the notion of physical existence, and so to the extent we are referring to things in time and space that can be seen and measured we share the well-known physicalist ontology. Physicalism is an ontological monism, which means it says just one kind of thing exists, namely physical things. But is physicalism is a sufficient ontology to explain the mind? Die-hards insist it is and must be, and that anything else is new-age nonsense. I am sympathetic to the extent that I agree that mysticism is not explanatory and has no place in science. And we can certainly agree from common knowledge that physical things exist. But we also know that physical things alone don’t yet explain our subjective experience, which is so much more complex than the observed physical properties of the brain would seem to suggest. So we really need to consider whether we can extend science’s reach into the mind without resorting to the supernatural.

We are intimately familiar with the notion of mental existence, as in Descartes’ “I think therefore I am.” Feeling and thinking (as states of mind) seem to us to exist in a distinct way from physical things as they lack extent in space or time. Idealism is the monistic ontology that asserts that only mental things exist, and what we think of as physical things are really just mental representations. In other words, we dream up reality any way we like. But science and our own experience offer overwhelming evidence of a persistent physical reality that doesn’t fluctuate in accord with our imagination, which makes pure idealism untenable. But if we join the two together, we can imagine a dualism of mind and matter with both mental and physical entities that don’t reduce to each other. Religions seized on this idea, stipulating a soul (or something like it) that is distinct from the body. Descartes also promoted dualism, but he got into trouble identifying the mechanism: he guessed that the brain had a special mental substance that did the thinking, a substance that could in principle be separated from the body. Descartes imagined the two substances somehow interacted in the pineal gland. But no such substance was ever found and the pineal gland’s primary role is to make melatonin, which helps regulate sleep.

We know from science that the brain works using physical laws with nothing supernatural added, so we need an explanation of the mind bound by that constraint. While Descartes’ substance dualism doesn’t deliver, two other forms of dualism have been proposed. Property dualism tries to separate mind from matter by asserting that mental states are nonphysical properties of physical substances (namely brains). This misses the mark, too, because it suggests a direct or inherent relationship between mental states and the physical substance that holds the state (the brain), and, as we will see, this relationship is not direct. It is like saying software is a non-physical property of hardware. But while software runs on hardware, the hardware reveals nothing about what the software is meant to do. Predicate dualism proposes that predicates, being any subjects of conversation, are not reducible to physical explanations and so constitute a separate kind of existence. I will demonstrate that this is true and so hold that predicate dualism is the correct ontology science needs, but I am rebranding it as form and function dualism (just why is explained below). Sean Carroll writes,2

“Does baseball exist? It’s nowhere to be found in the Standard Model of particle physics. But any definition of “exist” that can’t find room for baseball seems overly narrow to me.”

Me too. Baseball encompasses everything from an abstract set of rules to a national pastime to specific sporting events featuring two baseball teams. Some of these have a physical corollary and some don’t, but the physical part isn’t the point. A game is an abstraction about possible outcomes when two sides compete under a set of rules. “Three” is an abstraction of quantity, “red” of color, “happy” of emotion. Quantity is an abstraction of groups, color of light frequency, brightness and context, and emotion of experienced mental states. Even common physical items can be rather abstract. Water is the liquid that comprises lakes, oceans, and rain, even though all have dissolved solids, with water from oceans having up to 3.5% salts. Diet soda has far less aspartame (0.05%), yet we would never call it water. So whether we use the word water depends on the functional impact of the dissolved solids — if no impact, then it still counts as plain water. Seawater counts as water for many purposes, just notably not for hydrating plants or animals.

So why don’t I like the term predicate dualism? The problem is that it suggests that because propositional attitudes can’t be eliminated from explaining the mind that they are also irreducible, but that is not true. They are readily reduced to simpler functional entities. Let’s take a quick look at how that happens. Brains work within physical laws, but are different from rock slides or snowstorms because they manage information. Information is entirely natural and can be managed by a physical system that can systematically leverage feedback. Living things are systems capable of doing this.3 Genes can’t manage real-time information, but brains, a second-order living information management system, can. We don’t really need to posit souls or minds to get started, we only need to focus on information, which is another way of saying capacity or function. So I prefer form and function dualism to predicate dualism because it more clearly describes the two kinds of things that exist. Function is much bigger than predicates, which are items which can be affirmed or denied. Information is broader than simple truth or falsity and includes any patterns which can be leveraged to achieve function. For example, while predicates are the subjects (and objects) of logical reasoning, function includes not just these active elements that can be manipulated by logical reasoning but also passive forms, like the capacities imbued in us by evolution, instinct, and conditioning. These are mechanisms and behaviors that have been effective in past situations. Evolution established fins, legs, and wings mostly for locomotion. Animals don’t need to know the details so long as they work, but the selection pressures are on function, not form. However, we can actively reason out the passive function of wings to derive principles that help us build planes. Some behaviors originally established with reason, like tying shoelaces, can be executed passively (on autopilot) without active use of predicates or reasoning. So we should more generally think of this nonphysical existence as a capacity for doing things rather than as yes or no predicates.

This diagram shows how form and function dualism compares to substance dualism and several monisms. These two perspectives, form and function, are not just different ways of viewing a subject, but define different kinds of existences. Physical things have form, e.g. in spacetime, or potentially in any dimensional state in which they can have an extent. Physical systems that leverage information are no longer just physical but physical and functional systems. Function has no extent but is instead measured in terms of its predictive power. Evolution uses feedback to refine algorithms (e.g. catalysis and pattern-matching) to increase their functionality. The higher-order information management systems found in brains use real-time feedback to accelerate the development of functionality. Although information management systems make function possible in an otherwise physical world, form and function can’t be reduced to each other. I show them as planes with a line of intersection not because they meet in the pineal gland but because there are relationships between them. Physical information management systems allow functional entities to operate using physical mechanisms. These entities are not in the physical universe because they are not physical, but they control the behavior of physical systems and so change the physical universe. Viewed the other way, we create the physical world in our minds by modeling it via phenomena. We never know the actual form of physical things (i.e. their noumena) but only our interpretation of them (i.e. their phenomena), so to our minds the physical world is primarily a functional construct and only secondarily physical. So the physical world is capable of simulating some measure of function, and the functional world is capable of simulating some measure of form. As I have noted, the uniformity of nature gives an otherwise physical universe the capacity to develop functional entities through feedback, so our universe is not strictly just physical because life has unleashed function into it. For this reason, function can be said to emerge from form, meaning that certain interactions of forms make function “spring” into existence with new capabilities not otherwise present in forms. It isn’t magic; it is just results from the fact that patterns can be used to predict what will happen next in a uniform universe, and competitive feedback systems leverage those patterns to survive. Living things are still physical, but the function they manage is not. Function can be said to exist in an abstract, timeless, nonphysical sense independent of whether it is ever implemented. This is true because an idea is not made possible because we think it; it is “out there” waiting to be thought whether we think it or not. Genes can only capture information gathered from feedback across generations of life cycles. This can lead to instinctive support for some complex mental behaviors, like dam-building in beavers, but it can’t manage information in real-time. Brains do gather information in real-time, and learning commits it to memory to let them surpass their instinctive behavior. Humans can apply information in arbitrarily abstract ways, which could, in principle, let them think any thought or attain any function. Our own brains are, of course, heavily constrained by their architecture, and any artificial brain we build would still have physical constraints, so we can’t, in practice, think anything. Across the infinite range of possible functions we can only access a smaller, but still infinite, set.

So the problem with physicalism as it is generally presented is that form is not the only thing a physical universe can create; it can create form and function, and function can’t be explained with the same kind of laws that apply to form but instead needs its own set of rules. If physicalism had just included rules for both direct and abstract existence in the first place, we would not need to have this discussion. But instead, it was (inadvertently) conceived to exclude an important part of the natural world, the part whose power stems from the fact that it is abstracted away from the natural world. It is ironic considering scientific explanation itself (and all explanation) is itself immaterial function and not form. How can science see both the forest and the trees if it won’t acknowledge the act of looking?

Pipe

A thought about something is not the thing itself. “Ceci n’est pas une pipe,” as Magritte said4. The phenomenon is not the noumenon, as Heidegger would have put it: the thing-as-sensed is not the thing-in-itself. If it is not the thing itself, what is it? Its whole existence is wrapped up in its potential to predict the future; that is it. However, to us, as mental beings, it is very hard to distinguish phenomena from noumena, because we can’t know the noumena directly. Knowledge is only about representations, and isn’t and can’t be the physical things themselves. The only physical world the mind knows is actually a mental model of the physical world. So while Magritte’s picture of a pipe is not a pipe, the image in our minds of an actual pipe is not a pipe either: both are representations. And what they represent is a pipe you can smoke. What this critically tells us is that we don’t care about the pipe, we only care about what the pipe can do for us, i.e. what we can predict about it. Our knowledge was never about the noumenon of the pipe; it was only about the phenomena that the pipe could enter into. In other words, knowledge is about function and only cares about form to the extent it affects function. We know the physical things have a provable physical existence — that the noumena are real — it is just that our knowledge of them is always mediated through phenomena. Our minds experience phenomena as a combination of passive and active information, where the passive work is done for us subconsciously finding patterns in everything and the active work is our conscious train of thought applying abstracted concepts to whatever situations seem to be good matches for them.

Given the foundation of form and function dualism, what can we now say distinguishes the mind from the brain? I will argue that the mind is a process in the brain viewed from its role of performing the active function of controlling the body. That’s a mouthful, so let me break it down. First, the mind is not the brain but a process in the brain. Technically, a process is any series of events that follows some kind of rules or patterns, but in this case I am referring specifically just to the information managing capabilities of the brain as mediated by neurons. We don’t know quite how they do it, but we can draw an analogy to a computer process that uses inputs and memory to produce outputs. But, as argued before, we are not so concerned with how this brain process works technically as with what function it performs because we now see the value of distinguishing functional from physical existence. Next, I said the mind is about active function. To be clear, we only have one word for mind, but might be referring to several things. Let’s call the “whole mind” the set of all processes in the brain taken from a functional perspective. Most of that is subconscious and we don’t necessarily know much about it consciously. When I talk about the mind, I generally mean just the conscious mind, which consists only of the processes that create our subjective experience. That experience has items under direct focused attention and also items under peripheral attention. It includes information we construct actively and also provides us access to much information that was constructed passively (e.g. via senses, instinct, intuition, and recollection). The conscious mind exists as a distinct process from the whole mind because it is an effective way for animals to make the kinds of decisions they need to make on a continuous basis.

3. The nature of knowledge: pragmatism, rationalism and empiricism

Given that we agree to break entities down into form and function, things and ideas, physical and mental, we next need to consider what we can know about them, and what it even means to know something. A theory about the nature of knowledge is called an epistemology. I described the mental world as being the product of information, which is patterns that can be used to predict the future. What if we propose that knowledge and information are the same thing? Charles Sanders Peirce called this epistemology pragmatism, the idea that knowledge consists of access to patterns that help predict the future for practical uses. As he put it, pragmatism is the idea that our conception of the practical effects of the objects of our conception constitutes our whole conception of them. So “practical” here doesn’t mean useful; it means usable for prediction, e.g. for statistical or logical entailment. Practical effects are the function as opposed to the form. It is just another way of saying that information and knowledge differ from noise to the extent they can be used for prediction. Being able to predict well doesn’t confer certainty like mathematical proofs; it improves one’s chances but proves nothing.

Pragmatism takes a hard rap because it carries a negative connotation of compromise. The pragmatist has given up on theory and has “settled” for the “merely” practical. But the whole point of theory is to explain what will really happen and not simply to be elegant. It is not the burden of life to live up to theory, but of theory to live up to life. When an accepted scientific theory doesn’t exactly match experimental evidence, it is because the experimental conditions are more complex than the theory’s ideal model. After all, the real world is full of imperfections that the simple equations of ideal models don’t take into account. However, we can potentially model secondary and tertiary effects with additional ideal models and then combine the models and theories to get a more accurate overall picture. However, in real-world situations it is often impractical to build this more perfect overall ideal model, both because the information is not available and because most situations we face include human factors, for which physical theories don’t apply and social theories are imprecise. In these situations pragmatism shines. The pragmatist, whose goal is to achieve the best prediction given real-world constraints, will combine all available information and approaches to do it. This doesn’t mean giving up on theory; on the contrary, a pragmatist will use well-supported theory to the limit of practicality. They will then supplement that with experience, which is their pragmatic record of what worked best in the past, and merge the two to reach a plan of action. Recall that information is the product of both a causative (reasoned) approach and a pattern analysis (e.g. intuitive) approach. Both kinds of information can be used to build the axioms and rules of a theoretical model. We aspire to causative rules for science because they lead to necessary conclusions, but in their absence we will leverage statistical correlations. We associate subconscious thinking with the pattern analysis approach, but it also leverages concepts established explicitly with a causative approach. Both our informal and formal thinking is a combination at many levels of both causation and pattern analysis. Because our conscious and subconscious minds work together in a way that appears seamless to us, we are inclined to believe that reasoned arguments are correct and not dependent on subjective (biased) intuition and experience. But we are strongly wired to think in biased ways, not because we are fundamentally irrational creatures but because biased thinking is often a more effective strategy than unbiased reason. We are both irrational and rational because both help in different ways, but we have to spot and overcome irrational biases or we will make decisions that conflict with our own goals. All of our top-level decisions have to strike a balance between intuition/experience-based (conservative) thinking and reasoned (progressive) thinking. Conservative methods let us act quickly and confidently so we can focus our attention on other problems. Progressive methods slow us down by casting doubt but they reveal better solutions. It is the principal role of consciousness to provide the progressive element, to make the call between a tried-and-true or a novel approach to any situation. These calls are always themselves pragmatic, but if in the process we spot new causal links then we may develop new ad hoc or even formal theories, and we will remember these theories along with the amount of supporting evidence they seem to have. Over time our library of theories and their support will grow, and we will draw on them for rational support as needed.

Although pragmatism is necessary at the top level of our decision-making process where experience and reason come together to effect changes in the physical world, it is not a part of the theories themselves, which exist independently as constructs of the mental (i.e. functional) world. We do have to be pragmatic about what theories we develop and about how we apply them, but since theories represent idealized functional solutions independent of practical concerns, the knowledge they represent is based on a narrower epistemology than pragmatism. But what is this narrower epistemology? After all, it is still the case that theories help predict the future for practical benefits. And Peirce’s definition, that our conception of the practical effects of the objects of our conception constitutes our whole conception of them, is also still true. What is different about theory is that it doesn’t speak to our whole conception of effects, inclusive of our experience, but focuses on causes and effects in idealized systems using a set of rules. Though technically a subset of pragmatism, rule based-systems literally have their own rules and can be completely divorced from all practical concerns, so for all practical purposes they have a wholly independent epistemology based on rules instead of effects. This theory of knowledge is called rationalism, and holds that reason (i.e. logic) is the chief source of knowledge. Put another way, where pragmatism uses both causative and pattern analysis approaches to create information, reason only uses the logical, causative approach, though it leverages axioms derived from both causative and pattern-based knowledge. A third epistemology is empiricism, which holds that knowledge comes only or primarily from sensory experience. Empiricism is also a subset of pragmatism; it differs in that it pushes where pragmatism pulls. In other words, empiricism says that knowledge is created as stimuli come in, while pragmatism says it arises as actions and effects go out. The actions and effects do ultimately depend on the inputs, and so pragmatism subsumes empiricism, which is not prescriptive about how the inputs (evidence) might be used. In science, the word empiricism is taken to mean rationalism + empiricism, i.e. scientific theory and the evidence that supports it, so one can say that rationalism is the epistemology of theoretical science and empiricism is the epistemology of applied science.

Mathematics and highly mathematical physical theories are often studied on an entirely theoretical basis, with considerations as to their applicability left for others to contemplate. The study of algorithms is mostly theoretical as well because their objectives are established artificially, so they can’t be faulted for inapplicability to real-world situations. Developing algorithms can’t, in and of itself, explain the mind, because even if the mind does employ an algorithm (or constellation of algorithms), the applicability of those algorithms to the real-world problems the mind solves must be established. But iteratively we can propose algorithms and tune them so that they do align with problems the mind seems to solve. Guessing at algorithms will never reveal the exact algorithm the mind or brain uses, but that’s ok. Scientists never discover the exact laws of nature; they only find rules that work in all or most observed situations. What we end up calling an understanding or explanation of nature is really just a framework of generalizations that helps us predict certain kinds of things. Arguably, laws of nature reveal nothing about the “true” nature of the universe. So it doesn’t matter whether the algorithms we develop to explain the mind have anything to do with what the mind is “actually” doing; to the extent they help us predict what the mind will do they will provide us with a greater understanding of it, which is to say an explanation of it.

Because proposing algorithms, or outlines of potential algorithms, and then testing them against empirical evidence is entirely consistent with the way science is practiced (i.e. empiricism), this is how I will proceed. But we can’t just propose algorithms at random; we will need a basis for establishing appropriate artificial objectives, and that basis has to be related to what it is we think minds are up to. This is exactly the feedback loop of the scientific method: propose a hypothesis, test it, and refine it ad infinitum. The available evidence informs our choice of solution, and the effectiveness of the solution informs how we refine or revise it. From the high level at which I approach this subject in this book, I won’t need to be very precise in saying just how the algorithms work because that would be premature. All we can do at this stage is provide a general outline for what kinds of skills and considerations are going into different aspects of the thought process. Once we have come to a general agreement on that, we can start to sweat the details.

While my approach to the subject will be scientifically empirical, we need to remember that the mind itself is primarily pragmatic and only secondarily capable of reason (or intuition) to support that pragmatism. So my perspective for studying the mind is not itself the way the mind principally works. This isn’t a problem so long as we keep it in mind: we are using a reasonable approach to study something that is itself uses a highly integrated combination of reason and intuition (basically causation and pattern). It would be disingenuous to suggest that I have freed myself of all possible biases in this quest and that my conclusions are perfectly objective; even established science can never be completely free of biases. But over time science can achieve ever more effective predictive models, which is the ultimate standard for objectivity: can results be duplicated? But the hallmark of objectivity is not its measure but its methods: logic and reason. The conclusions one reaches through logic using a system of rules built on postulates can be provably true, contingent on the truth of the postulates, which make it a very powerful tool. Although postulates are true by definition from the perspective of the logical model that employs them, they have no absolute truth in the physical world because our direct knowledge of the physical world is always based on evidence from individual instances and not on generalities across similar instances. So truth in the physical world (as we see it from the mental world) is always a matter of degree, the degree to which we can correlate a given generality to a group of phenomena. That degree depends both on the clarity of the generalization and on the quality of the evidence, and so is always approximate at best, but can often be close enough to a perfect correlation to be taken as truth (for practical purposes). Exceptions to such truths are often seen more as “shortcomings of reality” than as shortcomings of the truth since truth (like all concepts) exists more in a functional sense than in the sense of having a perfect correlation to reality.

But how can we empirically approach the study of the mind? If we can accept the idea that the mind is principally a functional entity, it is largely pointless to look for physical evidence of its existence, beyond establishing the physical mechanism (the brain) that supports it. This is because physical systems can make information management possible but can’t explain all the uses to which the information can be put, just as understanding the hardware of the internet doesn’t say anything about the information flowing through it. We must instead look at the functional “evidence.” We can never get direct evidence, being facts or physical signs, of function (because function has no form), so we either need to look at physical side effects or develop a way to see “evidence” of function directly independent of the physical. Behavior provides the clearest physical evidence of mental activity, but our more interesting behavior results from complex chains of thought and can’t be linked directly to stimulus and response. Next, we have personal evidence of our own mind from our own experience of it. This evidence is much more direct than behavioral evidence but has some notable shortcomings as well. Introspection has a checkered past as a tool for studying the mind. Early hopes that introspection might be able to qualitatively and quantitatively describe all conscious phenomena were overly optimistic, largely because they misunderstand the nature of the tool. Our conscious minds have access to information based both on causation and pattern analysis, but our conscious awareness of this information is filtered through an interpretive layer that generalizes the information into conceptual buckets. So these generalized interpretations are not direct evidence, but, like behavior, are downstream effects of information processing. Even so, our interpretations can provide useful clues even if they can’t be trusted outright. Freud was too quick to attach significance to noise in his interpretation of dreams as we have no reason to assume that the content of dreams serves any function. Many activities of the mind do serve a function, however, so we can study them from the perspective of those functions. As the conscious mind makes a high-level decision, it will access functionally relevant information packaged in a form that the conscious subprocess can handle, which is at least partially in the form of concepts or generalizations. These concepts are the basis of reason (i.e. rationality), so to the extent our thinking is rational then our interpretation of how we think is arguably exactly how we think (because we are conscious of it). But that extent is never exact or complete because our concepts draw on a vast pool of subconscious information which heavily colors how we use them, and also we use subconscious data analysis algorithms (most notably memory/recognition). For both of these reasons any conscious interpretation will only be approximate and may cause us to overlook or misinterpret our actual motivations completely (for which we may have other motivations to suppress).

While both behavior and introspection can provide evidence that can suggest or support models of the mind, they are pretty indirect and can’t provide very firm support for those models. But another way to study function is to speculate about what function is being performed. Functionalism holds that the defining characteristics of mental states are the functions they bring about, quite independent of what we think about those functions (introspectively) or whether we act on them (behaviorally). This is the “direct” study of function independent of the physical to which I alluded. Speculation to function, aka the study of causes and effects, is an exercise of logic. It depends on setting up an idealized model with generalized components that describes a problem. These components don’t exist physically but are exemplars that embody only the properties of their underlying physical referents that are relevant to the situation. Given the existence of these exemplars (including their associated properties) as postulates, we can then reason about what behavior we can expect from them. Within such a model, function can be understood very well or even perfectly, but it is never our expectation that these models will align perfectly with real-world situations. What we hope for is that they will match well enough that predictions made using the model will come true in the real world. Our models of the functions of mental states won’t exactly describe the true functions of those mental states (if we could ever discover them), but they will still be good explanations of the mind if they are good at predicting the functions our minds perform.

Folk explanations differ from scientific explanations in the breadth and reliability of their predictive power. While there are unlimited folk perspectives we can concoct to explain how the mind works, all of which will have some value in some situations, scientific perspectives (theories) seek a higher standard. Ideally, science can make perfect predictions, and in many physical situations it nearly does. Less ideally, science should at least be able to make predictions with odds better than chance. The social sciences usually have to settle for such a reduced level of certainty because people, and the circumstances in which they become involved, are too complex for any idealized model to describe. So how, then, can we distinguish bona fide scientific efforts in matters involving minds from pseudoscience? I will investigate this question next.

4. What Makes Knowledge Objective?

It is easier to define subjective knowledge that objective knowledge. Subjective knowledge is anything we think we know, and it counts as knowledge as long as we think it does. We set our own standard. It starts with our memory; a memory of something is knowledge of it. Our minds don’t record the past for its own sake but for its potential to help us in the future. From past experience we have a sense of what kinds of things we will need to remember, and these are the details we are most likely to commit to memory. This bias aside, our memory of events and experiences is fairly automatic and has considerable fidelity. The next level of memory is of our reflections: thoughts we have had about our experiences, memories and other thoughts. I call these two levels of memory and knowledge detailed and summary. There is no exact line separating the two, but details are kept as raw and factual as possible while summaries are higher-order interpretations that derive uses for the details. It takes some initial analysis, mostly subconscious, to study our sensory data so we can even represent details in a way that we can remember. Summaries are a subsidiary analysis of details and other summary information performed using both conscious (reasoned) and subconscious (intuitive) methods. These details and summaries are what we know subjectively.

We are designed to gather and use knowledge subjectively, so where does objectivity come in? Objectivity creates knowledge that is more reliable and broadly applicable than subjective knowledge. Taken together, reliability and broad applicability account for science’s explanatory power. After all, to be powerful, knowledge must both fit the problem and do so dependably. Objective approaches let us create both physical and social technologies to manage both goods and services to high standards. How can we create objective knowledge that can do these things? As I noted above, it’s all about the methods. Not all methods of gathering information are equally effective. Throughout our lives, we discover better ways of doing things, and we will often use these better ways again. Science makes more of an effort to identify and leverage methods that produce better information, i.e. with reliability and broad applicability. These methods are collectively called the “scientific method”. It isn’t one method but an evolving set of best practices. They are only intended to bring some order to the pursuit and do not presume to cover everything. In particular, they say nothing of the creative process or seek to constrain the flow of ideas. The scientific method is a technology of the mind, a set of heuristics to help us achieve more objective knowledge.

The philosophy of science is the conviction that an objective world independent of our perceptions exist and that we can gain an understanding of it that is also independent of our perceptions. Though it is popularly thought that science reveals the “true” nature of reality, it has been and must always be a level removed from reality. An explanation or understanding of the world will always be just one of many possible descriptions of reality and never reality itself. But science doesn’t seek a multitude of explanations. When more than one explanation exists, science looks for common ground between and tries to express them as varying perspectives of the same underlying thing. For example, wave-particle duality allows particles to be described both as particles and waves. Both descriptions work and provide explanatory power, even though we can’t imagine macroscopic objects being both at the same time. We are left with little intuitive feel for the nature of reality, which serves to remind us that the goal of objectivity is not to see what is actual there but to gain the most explanatory power over it that we can. The canon of generally-accepted scientific knowledge at any point in time will be considered charming, primitive and not terribly powerful when looked back on a century or two later, but this doesn’t mitigate its objectivity or claim on success.

That said, the word “objectivity” hints at certainty. While subjectivity acknowledges the unique perspective of each subject, objectivity is ostensibly entirely about the object itself, its reality independent of the mind. If an object actually did exist, any direct knowledge we had of it would then remain true no matter which subject viewed it. This goal, knowledge independent of the viewer, is admirable but unattainable. Any information we gather about an object must always ultimately depend on observations of it, either with our own senses or using instruments we devise. And no matter how reliable that information becomes, it is still just information, which is not the object itself but only a characterization of traits with which we ultimately predict behavior. So despite its etymology, we must never confuse objectivity with “actual” knowledge of an object, which is not possible. Objectivity only characterizes the reliability of knowledge based on the methods used to acquire it.

With those caveats out of the way, a closer look at the methods of science will show how they work to reduce the likelihood of personal opinion and maximize the likelihood of reliable reproduction of results. Below I list the principle components of the scientific method, from most to least helpful (approximately) in establishing its mission of objectivity.

    1. The refinement of hypotheses. This cornerstone of the scientific method is the idea that one can propose a rule describing how kinds of phenomena will occur, and that one can test this rule and refine it to make it more reliable. While it is popularly thought that scientific hypotheses are true until proven otherwise (i.e. falsified, as Karl Popper put it), we need to remember that the product of objective methods, including science, is not truth but reliability5. It is not so much that laws are true or can be proven false as that they can be relied on to predict outcomes in similar situations. The Standard Model of particle physics purports (with considerable success) that any two subatomic particles of the same kind are identical for all predictive purposes except for occupying a different location in spacetime.6. Maybe they are identical (despite this being impossible to prove), and this helps account for the many consistencies we observe in nature. But location in spacetime is a big wrinkle. The three body problem remains insoluble in the general case, and solving for the movements of all astronomical bodies in the solar system is considerably more so. Predictive models of how large groups of particles will behave (e.g. for climate) will always just be models for which reliability is the measure and falsifiability is irrelevant. Also, in most real-world situations many factors limit the exact alignment of scientific theory to circumstances, e.g. impurities, ability to acquire accurate data, and subsidiary effects beyond the primary theory being applied. Even so, by controlling the conditions adequately, we can build many things that work very reliably under normal operating conditions. Some aspects of mental function will prove to be highly predictable while others will be more chaotic, but our standard for scientific value should still be explanatory power.
    2. Scientific techniques. This most notably includes measurement via instrumentation rather than use of senses. Instruments are inherently objective in that they can’t have a bias or opinion regarding the outcome, which is certainly true to the extent the instruments are mechanical and don’t employ computer programs into which biases may have been unintentionally embedded. However, they are not completely free from biases or errors in how they are used, and also there are limits in the reliability of any instrument, especially at the limits of their operating specifications. Scientific techniques also include a wide variety of practices that have been demonstrated to be effective and are written up into standard protocols in all scientific disciplines to increase the chances that results can be replicated by others, which is ultimately the objective of science.
    3. Critical thinking. I will define critical thinking here without defense, as that requires a more detailed understanding of the mind than I have yet provided. Critical thinking is an effort to employ objective methods of thought with proven reliability while excluding subjective methods known to be more susceptible to bias. Next, I distinguish three of the most significant components of critical thinking:

3a. Rationality. Rationality is, in my theory of the mind, the subset of thinking concerned with applying causality to concepts, aka reasoning. As I noted in The Mind Matters, thinking and the information that is thought about divide into two camps, being reason, which manages information that derives using a causative approach, and intuition, which manages information that derives using a pattern analysis approach. Both approaches are used to some degree for almost every thought we have, but it is often useful to focus on one of these approaches as the sole or predominant one for the purpose of analysis. The value of the rational approach over the intuitive is in its reproducibility, which is the primary objective of science and the knowledge it seeks to create. Because rational techniques can be written down to characterize both starting conditions and all the rules and conclusions they imply, they have the potential to be very reliable.

3b. Inductive reasoning. Inductive reasoning extrapolates patterns from evidence. While science seeks causative links, it will settle for statistical correlations if it has to. Newton used inductive reasoning to posit gravity, which was later given a cause by Einstein’s theory of general relativity as a deformation of space-time geometry.

3c. Abductive reasoning. Abductive reasoning seeks the simplest and most likely explanations, which is a pattern matching heuristic that picks kinds of matches that tend to work out best. Occam’s Razor is an example of this often used in science: “Among competing hypotheses, the one with the fewest assumptions should be selected”.

3d. Open-mindedness. Closed-mindedness means having a fixed strategy to deal with any situation. It enables a confident response in any circumstance, but works badly if one tries to use it beyond the conditions those strategies were designed to handle. Open-mindedness is an acceptance of the limitations of one’s knowledge along with a curiosity about exploring those limitations to discover better strategies. While everyone must be open-minded in situations where ignorance is unavoidable, one hopes that one will develop sufficient mastery over most of the situations that one encounters to be able to act confidently in a closed-minded way without fear of making a mistake. While this is often possible, the scientist must always remember that perfect knowledge is unattainable and must always be alert for possible cracks in one’s knowledge. These cracks should be explored with objective methods to discover more reliable knowledge and strategies than one might already possess. By acknowledging the limits and fallibility of its approaches and conclusions, science can criticize, correct, and improve itself. Thus, more than just a bag of tricks to move knowledge forward, it is characterized by a willingness to admit to being wrong.

3e. Countering cognitive biases. More than just prejudice or closed-mindedness, cognitive biases are subconscious pattern analysis algorithms that usually work well for us but which are less reliable than objective methods. The insidiousness of cognitive biases was first exposed by Tversky and Kahneman their 1971 paper, “Belief in the law of small numbers.”78. Cognitive biases use pattern analysis to lead us to conclusions based on correlations and associations rather than causative links. They are not simply inferior to objective methods because they can account for indirect influences that can be overlooked by objective methods. But robust causative explanations are always more reliable than associative explanations, and in practice they tend to be right where biases are wrong. (where “right” and “wrong” here are taken not as absolutes but as expressions of very high and low reliability).

    4. Peer review. Peer review is the evaluation of a scientific work by one or more people of similar competence to assess whether it was conducted using appropriate scientific standards.
    5. Credentials. Academic credentials attest to the completion of specific education programs. Titular credentials, publication history, and reputation add to a researcher’s credibility. While no guarantee, credentials help establish an author’s scientific reliability.
    6. Pre-registration. A recently added best practice is pre-registration, which clears a study for publication before it has been conducted. This ensures that the decision to publish is not contingent on the results, which would be biased 9.

The physical world is not itself a rational place because reason itself it has a functional existence, not a physical existence. So rational understanding, and consequently what we think of as truth about the physical world, depends on the degree to which we can correlate a given generality to a group of phenomena. But how can we expect a generality (i.e. hypothesis) that worked for some situations to work for all similar situations? The Standard Model of particle physics professes (with considerable success) that any two subatomic particles of the same kind are identical for all predictive purposes except for occupying a different location in spacetime.10. Maybe they are identical (despite this being impossible to prove), and this helps account for the many consistencies we observe in nature. But location in spacetime is a big wrinkle. The three body problem remains insoluble in the general case, and solving for the movements of all astronomical bodies in the solar system is considerably more so. Predictive models of how large groups of particles will behave (e.g. for climate) will always just be models for which reliability is the measure and falsifiability is irrelevant. Particles are not simply free-moving; they clump into atoms and molecules in pretty strict accordance with laws of physics and chemistry that have been elaborated pretty well. Macroscopic objects in nature or manufactured to serve specific purposes seem to obey many rules with considerably more fidelity than free-moving weather systems, a fact upon which our whole technological civilization depends. Still, in most real-world situations many factors limit the exact alignment of scientific theory to circumstances, e.g. impurities, ability to acquire accurate data, and subsidiary effects beyond the primary theory being applied. Even so, by controlling the conditions adequately, we can build many things that work very reliably under normal operating conditions. The question I am going to explore in this book is whether scientific, rational thought can be successfully applied to function and not just form, and specifically to the mental function comprising our minds. Are some aspects highly predictable while others remain chaotic?

We have to keep in mind just how much we take correlation of theory to reality for granted when we move above the realm of subatomic particles. No two apples are alike, or any two gun parts, though Eli Whitney’s success with interchangeable parts has led us to think of them as being so. They are interchangeable once we slot them into a model or hypothesis, but in reality any two macroscopic objects have many differences between them. A rational view of the world breaks down as the boundaries between objects become unclear as imperfections mount. Is a blemished or rotten apple still an apple? What about a wax apple or a picture of an apple? Is a gun part still a gun part if it doesn’t fit? A hypothesis that is completely logical and certain will still have imperfect applicability to any real-world situation because the objects that comprise it are idealized, and the world is not ideal. But still, in many situations this uncertainty is small, often vanishingly small, which allows us to build guns and many other things that work very reliably under normal operating conditions.

How can we mitigate subjectivity and increase objectivity? More observations from more people help, preferably with instruments, which are much more accurate and bias-free than senses. This addresses evidence collection, but it not so easy to increase objectivity over strategizing and decision-making. These are functional tasks, not matters of form, and so are fundamentally outside the physical realm and so not subject to observation. Luckily, formal systems follow internal rules and not subjective whims, so to the degree we use logic we retain our objectivity. But this can only get us so far because we still have to agree on the models we are going to use in advance, and our preference of one model over another ultimately has subjective aspects. To the degree we use statistical reasoning we can improve our objectivity by using computers rather than innate or learned skills. Statistical algorithms exist that are quite immune to preference, bias, and fallacy (though again, deciding what algorithm to use involves some subjectivity). But we can’t yet program a computer to do logical reasoning on a par with humans. So we need to examine how we reason in order to find ways to be more objective about it so we can be objective when we start to study it. It’s a catch-22. We have to understand the mind first before we figure out how to understand it. If we rush in without establishing a basis for objectivity, then everything we do will be a matter of opinion. While there is no perfect formal escape from this problem, we informally overcome this bootstrapping problem with every thought through the power of assumption. An assumption, logically called a proposition, is an unsupported statement which, if taken to be true, can support other statements. All models are built using assumptions. While the model will ultimately only work if the assumptions are true, we can build the model and start to use it on the hope that the assumptions will hold up. So can I use a model of how the mind works built on the assumption that I was being objective to then establish the objectivity I need to build the model? Yes. The approach is a bit circular, but that isn’t the whole story. Bootstrapping is superficially impossible, but in practice is just a way of building up a more complicated process through a series of simpler processes: “at each stage a smaller, simpler program loads and then executes the larger, more complicated program of the next stage”. In our case, we need to use our minds to figure out our minds, which means we need to start with some broad generalizations about what we are doing and then start using those, then move to a more detailed but still agreeable model and start using that, and so on. So yes, we can only start filling in the details, even regarding our approach to studying the subject, by establishing models and then running them. While there is no guarantee it will work, we can be guaranteed it won’t work if we don’t go down this path. While not provably correct, nothing in nature can be proven. All we can do is develop hypotheses and test them. By iterating on the hypotheses and expanding them with each pass, we bootstrap them to greater explanatory power. Looking back, I have already done the first (highest level) iteration of bootstrapping by endorsing form & function dualism and the idea that the mind consists of processes that manage information. For the next iteration, I will propose an explanation for how the mind reasons, which I will then use to support arguments for achieving objectivity.

So then, from a high level, how does reasoning work? I presume a mind that starts out with some innate information processing capabilities and a memory bank into which experience can record learned information and capabilities. The mind is free of memories (a blank slate) when it first forms but is hardwired with many ways to process information (e.g. senses and emotions). Because our new knowledge and skills (stored in memory) build on what came before, we are essentially continually bootstrapping ourselves into more capable versions of ourselves. I mention all this because it means that the framework with which we reason is already highly evolved even from the very first time we start making conscious decisions. Our theory of reasoning has to take into account the influence of every event in our past that changed our memory. Every event that even had a short-term impact on our memory has the potential for long-term effects because long-term memories continually form and affect our overall impressions even if we can’t recall them specifically.

One could view the mind as being a morass of interconnected information that links every experience or thought to every other. That view won’t get us very far because it gives us nothing to manipulate, but it is true, and any more detailed views we develop should not contradict it. But on what basis can we propose to deconstruct reasoning if the brain has been gradually accumulating and refining a large pool of data for many years? On functional bases, of which I have already proposed two: logical and statistical, which I introduced above with pragmatism. Are these the only two approaches that can aid prediction? Supernatural prophecy is the only other way I can think of, but we lack reliable (if any) access to it, so I will not pursue it further. Just knowing that however the mind might be working, it is using logical and/or statistical techniques to accomplish its goals gives us a lot to work with. First, it would make sense, and I contend that it is true, that the mind uses both statistical and logical means to solve any problem, using each to the maximum degree they help. In brief, statistical means excel at establishing the assumptions and logical means at drawing out conclusions from the assumptions.

While we can’t yet say how neurons make reasoning possible, we can say that it uses statistics and logic, and from our knowledge of the kinds of problems we solve and how we solve them, we can see more detail about what statistical and logical techniques we use. Statistically, we know that all our experience contributes supporting evidence to generalizations we make about the world. More frequently used generalizations come to mind more readily than lesser used and are sometimes also associated with words or phrases, such as about the concept APPLE. An APPLE could be a specimen of fruit of a certain kind, or a reproduction or representation of such a specimen, or used in a metaphor or simile, which are situations where the APPLE concept helps illustrate something else. We can use innate statistical capabilities to recognize something as an APPLE by correlating the observed (or imagined) aspects of that thing against our large database every encounter we have ever had with APPLES. It’s a lot of analysis, but we can do it instantly with considerable confidence. Our concepts are defined by the union of our encounters, not by dictionaries. Dictionaries just summarize words, and yet words are generalizations and generalizations are summaries, so dictionaries are very effective because they summarize well. But brains are like dictionaries on steroids; our summaries of the assumptions and rules behind our concepts and models are much deeper and were reinforced by every affirming or opposing interaction we ever had. Again, most of this is innate: we generalize, memorize, and recognize whether we want to or not using built-in capacities. Consciousness plays an important role I will discuss later, but “sees” only a small fraction of the computational work our brains do for us.

Let’s move on to logical abilities. Logic operates in a formal system, which is a set of assumptions or axioms and rules of inference that apply to them. We have some facility for learning formal systems, such as the rules of arithmetic, but everyday reasoning is not done using formal systems for which we have laid out a list of assumptions and rules. And yet, the formal systems must exist, so where do they come from? The answer is that we have an innate capacity to construct mental models, which are both informal and formal systems. They are informal on many levels, which I will get into, but also serve the formal need required for their use in logic. How many mental models (models, for short) do we have in our heads? Looked at most broadly, we each have one, being the whole morass of all the information we have every processed. But it is not very helpful to take such a broad view, nor is it compatible with our experience using mental models. Rather, it makes sense to think of a mental model as the fairly small set of assumptions and rules that describe a problem we typically encounter. So we might have a model of a tree or of the game of baseball. When we want to reason about trees or baseball, we pull out our mental model and use it to draw logical conclusions. From the rules of trees, we know trees have a trunk with ever small branches branching off that have leaves that usually fall off in the winter. From the rules of baseball, we know that an inning ends on the third out. Referring back a paragraph, we can see that models and concepts are the same things — they are generalizations, which is to say they are assessments that combine a set of experience into a prototype. Though the same data, models and concepts have different functional perspectives: models view the data from the inside as the framework in which logic operates, and concepts view it from the outside as the generalized meaning it represents.

While APPLE, TREE, and BASEBALL are individual concepts/models, no two instances of them are the same. Any two apples must differ at least in time and/or place. When we use a model for a tree (let’s call it the model instance), we customize the model to fit the problem at hand. So for an evergreen tree, for example, we will think of needles as a degenerate or alternate form of leaves. Importantly, we don’t consciously reason out the appropriate model for the given tree; we recognize it using our innate statistical capabilities. A model or concept instance is created through recognition of underlying generalizations we have stored from long experience, and then tweaked on an ad hoc basis (via further recognition and reflection) to add unique details to this instance. Reflection can be thought of as a conscious tool to augment recognition. So a typical model instance will be based on recognition of a variety of concepts/models, some of which will overlap and even contradict each other. Every model instance thus contains a set of formal systems, so I generally call it a constellation of models rather than a model instance.

We reason with a model constellation by using logic within each component model and then using statistical means to weigh them against each other. The critical aspect of the whole arrangement is that it sets up formal systems in which logic can be applied. Beyond that, statistical techniques provide the huge amount of flexibility needed to line up formal systems to real-world situations. The whole trick of the mind is to represent the external world with internal models and to run simulations on those models to predict what will happen externally. We know that all animals have some capacity to generalize to concepts and models because their behavior depends on being able to predict the future (e.g. where food will be). Most animals, but humans in particular, can extend their knowledge faster than their own experience allows by sharing generalizations with others via communication and language, which have genetic cognitive support. And humans can extend their knowledge faster still through science, which formally identifies objective models.

So what steps can we take to increase the objectivity of what goes on in our minds, which has some objective elements in its use of formal models, but which also has many subjective elements that help form and interpret the models? Devising software that could run mental models would help because it could avoid fallacies and guard against biases. It would still ultimately need to prioritize using preferences, which are intrinsically subjective, but we could at least try to be careful and fair setting them up. Although it could guard against the abuses of bias, we have to remember that all generalizations are a kind of bias, being arguments for one way of organizing information over another. We can’t write software yet that can manage concepts or models, but machine learning algorithms, which are statistical in nature, are advancing quickly. They are becoming increasingly generalized to behave in ever more “clever” ways. Since concepts and models are themselves statistical entities at their core, we will need to leverage machine learning as a starting point for software that simulates the mind.

Still, there is much we can do to improve our objectivity of thought short of replacing ourselves with machines, and science has been refining methods to do it from the beginning. Science’s success depends critically on its objectivity, so it has long tried to reject subjective biases. It does this principally by cultivating a culture of objectivity. Scientists try to put opinion aside to develop hypotheses in response to observations. They then test them with methods that can be independently confirmed. Scientists also use peer review to increase independence from subjectivity. But what keeps peers from being subjective? In his 1962 classic, The Structure of Scientific Revolutions11, Thomas Kuhn noted that even a scientific community that considers itself objective can become biased toward existing beliefs and will resist shifting to a new paradigm until the evidence becomes overwhelming. This observation inadvertently opened a door which postmodern deconstructionists used to launch the science wars, an argument that sought to undermine the objective basis of science, calling it a social construction. To some degree this is undeniable, which has left science with a desperate need for a firmer foundation. The refutation science has fallen back on for now was best put by Richard Dawkins, who noted in 2013 that “Science works, bitches!”12. Yes, it does, but until we establish why we are blustering much like the social constructionists. The reason science works is that scientific methods increase objectivity while reducing subjectivity and relativism. It doesn’t matter that they don’t (and in fact can’t) eliminate it. All that matters is that they reduce it, which distinguishes science from social construction by directing it toward goals. Social constructions go nowhere, but science creates an ever more accurate model of the world. So, yes, science is a social construction, but one that continually moves closer to truth, if truth is defined in terms of knowledge that can be put to use. In other words, from a functional perspective, truth just means increasing the amount and quality of useful information. It is not enough for scientific communities to assume best efforts will produce objectivity, we must also discover how preferences, biases, and fallacies can mislead the whole community. Tversky and Kahneman did groundbreaking work exposing the extent of cognitive biases in scientific research, most notably in their 1971 paper, “Belief in the law of small numbers.”1314. Beyond just being aware of biases, scientists should not have to work in situations with a vested interest in specific outcomes. This can potentially happen in both public and private settings, but is more commonly a problem when science is used to justify a commercial enterprise.

5. Orienting science (esp. cognitive science) with form & function dualism and pragmatism

The paradigm I am proposing to replace physicalism, rationalism, and empiricism is a superset of them. Form & function dualism embraces everything physicalism stands for but doesn’t exclude function as a form of existence. Pragmatism embraces everything rationalism and empiricism stand for but also includes knowledge gathered from statistical processes and function.

But wait, you say, what about biology and the social sciences: haven’t they been making great progress within the current paradigm? Well, they have been making great progress, but they have been doing it using an unarticulated paradigm. Since Darwin, biology has pursued a function-oriented approach. Biologists examine all biological systems with an eye to the function they appear to be serving, and they consider the satisfaction of function to be an adequate scientific justification, but it isn’t under physicalism, rationalism or empiricism. Biologists cite Darwin and evolution as justification for this kind of reasoning, but that doesn’t make it science. The theory of evolution is unsupportable under physicalism, rationalism, and empiricism alone, but instead of acknowledging this metaphysical shortfall some scientists just ignore evolution and reasoning about function while others just embrace it without being overly concerned that it falls outside the scientific paradigm. Evolutionary function occupies a somewhat confusing place in reasoning about function because it is not teleological, meaning that evolution is not directed toward an end or shaped by a purpose but rather is a blind process without a goal. But this is irrelevant from an informational standpoint because information never directs toward an end anyway, it just helps predict. Goals are artifacts of formal systems, and so contribute to logical but not statistical information management techniques. In other words, goals and logic are imaginary constructs; they are critical for understanding the mind but can be ignored for studying evolution and biology, which has allowed biology to carry on despite this weakness in its foundation.

The social sciences, too, have been proceeding on an unarticulated paradigm. Officially, they are trying to stay within the bounds of physicalism, rationalism, and empiricism, but the human mind introduces a black box, which is what scientists call a part of the system that is studied entirely through its inputs and outputs without any attempt to explain the inner workings. Some efforts to explain it have been attempted. Pavlov and Skinner proposed that behaviorism could explain the mind as nothing more than operant conditioning, which sounded good at first but didn’t explain all that minds do. Chomsky refuted it in a rebuttal to Skinner’s Verbal Behavior by explaining how language acquisition leverages innate linguistic talents15. And Piaget extended the list of innate cognitive skills by developing his staged theory of intellectual development. So we now have good reason to believe the mind is much more than conditioned behavior and employs reasoning and subconscious know-how. But that is not the same thing as having an ontology or epistemology to support it. Form & function dualism and pragmatism give us the leverage to separate the machine (the brain) from its control (the mind) and to dissect the pieces.

Expanding the metaphysics of science has a direct impact across science and not just regarding the mind. First, it finds a proper home for the formal sciences in the overall framework. As Wikipedia says, “The formal sciences are often excluded as they do not depend on empirical observations.” Next, and critically, it provides a justification for the formal sciences to be the foundation for the other sciences, which are dependent on mathematics, not to mention logic and hypotheses themselves. But the truth is that there is no metaphysical justification for invoking formal sciences to support physicalism, rationalism, and empiricism. With my paradigm, the justification becomes clear: function plays an indispensable role in the way the physical sciences leverage generalizations (scientific laws) about nature. In other words, scientific theories are from the domain of function, not form. Next, it explains the role evolutionary thinking is already having in biology because it reveals how biological mechanisms use information stored in DNA to control life processes through feedback loops. Finally, this expanded framework will ultimately let the social sciences shift from black boxes to knowable quantities.

But my primary motivation for introducing this new framework is to provide a scientific perspective for studying the mind, which is the domain of cognitive science. It will elevate cognitive science from a loose collaboration of sciences to a central role in fleshing out the foundation of science. Historically the formal sciences have been almost entirely theoretical pursuits because formal systems are abstract constructs with no apparent real-world examples. But software and minds are the big exceptions to this rule and open the door for formalists to study how real-world computational systems can implement formal systems. Theoretical computer science is a well-established formal treatment of computer science, but there is no well-established formal treatment for cognitive science, although the terms theoretical cognitive science and computational cognitive science are occasionally used. Most of what I discuss in this book is theoretical cognitive science because most of what I am doing is outlining the logic of minds, human or otherwise, but with a heavy focus on the design decisions that seem to have impacted earthly, and especially human, minds. Theoretical cognitive science studies the ways minds could work, looking at the problem from the functional side, and leaves it as a (big) future exercise to work out how the brain actually brings this sort of functionality to life.

It is worth noting here that we can’t conflate software with function: software exists physically as a series of instructions, while function exists mentally and has no physical form (although, as discussed, software and brains can produce functional effects in the physical world and this is, in fact, their purpose). Drew McDermott (whose class I took at Yale) characterized this confusion in the field of AI like this (as described by Margaret Boden in Mind as Machine):

A systematic source of self-deception was their common habit (made possible by LISP: see 10.v.c) of using natural-language words to name various aspects of programs. These “wishful mnemonics”, he said, included the widespread use of “UNDERSTAND” or “GOAL” to refer to procedures and data structures. In more traditional computer science, there was no misunderstanding; indeed, “structured programming” used terms such as GOAL in a liberating way. In Al, however, these apparently harmless words often seduced the programmer (and third parties) into thinking that real goals, if only of a very simple kind, were being modelled. If the GOAL procedure had been called “G0034” instead, any such thought would have to be proven, not airily assumed. The self-deception arose even during the process of programming: “When you [i.e. the programmer] say (GOAL… ), you can just feel the enormous power at your fingertips. It is, of course, an illusion” (p. 145). 16

This begs the million-dollar question: if an implementation of an algorithm is not itself function, where is the function, i.e. real intelligence, hiding? I am going to develop the answer to this question as the book unfolds, but the short answer is that information management is a blind watchmaker both in evolution and the mind. That is, from a physical perspective the universe can be thought of as deterministic, so there is no intelligence or free will. But the main thrust of my book is that this doesn’t matter because algorithms that manage information are predictive and this capacity is equivalent to both intelligence and free will. So if procedure G0034 is part of a larger system that uses it to effectively predict the future, it can fairly also be called by whatever functional name you like that describes this aspect. Such mnemonics are actually not wishful. It is no illusion that the subroutines of a self-driving car that get it to its destination in one piece do wield enormous power and achieve actual goals. This doesn’t mean we are ready to start programming goals to the level human minds conceive them (and certainly not UNDERSTAND!), but function, i.e. predictive power, can be broken down into simple examples and implemented using today’s computers.

What are the next steps? My main point is that we need start thinking about how minds achieve function and stop thinking that a breakthrough in neurochemistry will magically solve the problem. We have to solve the problem by solving the problem, not by hoping a better understanding of the hardware will explain the software. While the natural sciences decompose the physical world from the bottom up, starting with subatomic particles, we need to decompose the mental world from the top down, starting (and ending) with the information the mind manages.

An Overview of What We Are

[Brief summary of this post]

What are we? Are we bodies or minds or both? Natural science tells us with fair certainty that we are creatures, one type among many, who evolved over the past few billion years in an entirely natural and explainable way. I certainly endorse broad scientific consensus, but this only confirms bodies, not minds. Natural science can’t yet confirm the existence of minds; we can observe the brain, by eye or with instruments, but we can’t observe the mind. Everything we know (or think we know) about the mind comes from one of two sources: our own experience or hearsay. However comfortable we are with our own minds, we can’t prove anything about the experience. Similarly, everything we learn about the world from others is still hearsay, in the sense that it is information that can’t be proven. We can’t prove things about the physical world; we can only develop pretty reliable theories. And knowledge itself, being information and the ability to apply it, only exists in our minds. Some knowledge appears instinctively, and some is acquired through learning (or so it seems to us). Beyond knowledge, we possess senses, feelings, desires, beliefs, thoughts, and perspectives, and we are pretty sure we can recognize these things in others. All of these mental words mean something about our ability to function in the world, and have no physical meaning in and of themselves. And not incidentally, we also have physical words that let us understand and interact with the physical world even though these words are also mental abstractions, being generalizations about kinds or instances of physical phenomena. We can comfortably say (but can’t prove) that we have a very good understanding of a mentally functional existence that is quite independent of our physical existence, an understanding that is itself entirely mentally functional and not physical. It is this mentally functional existence, our mind, that we most strongly identify with. When we are discussing any subject, the “we” doing the discussing is our minds, not our bodies. While we can identify with our bodies and recognize them as an inseparable possession, they, including our brains, are at least logically distinct entities from our minds. We know (from science) that the brain hosts our mind, but that is irrelevant to how we use our minds (excepting issues concerning the care of our heads and bodies) because our thoughts are abstractions not bound (except through indirect reference) to the physical world.

Given that we know we are principally mental beings, i.e. that we exist more from the perspective of function than form, what can we do to develop an understanding of ourselves? All we need to do is approach the question from the perspective of function rather than form. We don’t need to study the brain or the body; we need to study what they do and why. Just as homologous evolution caused eyes to evolve independently about 50-100 times, all our brain functions are evolving because of their value rather than because of their mechanism. Function drives evolution, not form, although form constrains what can be achieved.

But let’s consider the form for a moment before we move on to function. Observations of the brain will eventually reveal how it works in the same way dissection of a computer would. This will illuminate all the interconnections, and even which areas specialize in what kind of tasks. Monitoring neural activation alone could probably even get to the point where one could predict the gist of our thoughts with fair accuracy by correlating areas of neural activity to specific memories and mental states. But that would still be a parlor trick because such a physical reading would not reveal the rationale for the logical relationships in our cognitive models. The physical study of the brain will reveal much about the constraints of the system (the “hardware”), including signal speeds, memory storage mechanisms, and areas of specialized functions, but could it trace our thoughts (the “software”)? To extend the computer analogy, one can study software by doing a memory dump, so a similar memory reading ability for brains could reveal thoughts. But it is not enough to know the software or the thoughts; one needs to know what function is being served, i.e. what the software or thoughts do. A physical examination can’t reveal that; it is a mental phenomenon that can be understood only by reasoning out what it does from a higher-level (generalized) perspective and why. One can figure out what software does from a list of instructions, but one can’t see the larger purposes being served without asking why, which moves us from form to function, from physical to mental. So a better starting point is to ask what function is being served, from which one can eventually back out how the hardware and software do it. Since we are far from being able to decode the hardware or software of the brain (“wetware”) in much detail anyway, I will adopt this more direct functional approach.

From the above, we have finally arrived at the question we need to ask: What function do minds serve? The answer, for which I will provide a detailed defense later on, is that the function of the brain is to provide centralized, coordinated control of the body, and the function of the conscious mind is to provide centralized, coordinated control of the brain. That brains control bodies is, by now, not a very controversial stance. The rest of the body provides feedback to the brain, but the brain ultimately decides. The gut brain does a lot of “thinking” for itself, passing along its hungers and fears, but it doesn’t decide for you. That the conscious mind controls the brain is intuitively obvious but hard to prove given that our only primary information source about the mind is the mind itself, i.e. it is subjective instead of objective. However, if we work from the assumption that the brain controls the body using information management, which is to say the application of algorithms on data, then we can define the mind as what the brain is doing from a functional perspective. That is, the mind is our capacity to do things.

The conscious mind, however, is just a subset of the mind, specifically including everything in our conscious awareness, from sensory input to memories, both at the center of our attention and in a more peripheral state of awareness. We feel this peripheral awareness both because we can tell it is there without dwelling on it and because we often do turn our attention to it, at which point it happily becomes the center. The capacity of our mind to do things is much larger than our conscious awareness, including all things our brains can do for which we don’t consciously sense the underlying algorithm. Statistically, this includes almost everything our brains do. The things we use our minds to do which we can’t explain are said to be done subconsciously, by our subconscious mind. We only know the subconscious mind is there by this process of elimination: we can do it, but we are not aware of how we do it or sometimes that we are doing it at all.

For example, we can move, talk, and remember using our (whole) mind, but we can’t explain how we do them because they are controlled subconsciously, and the conscious mind just pulls the strings. Any explanations I might attempt of the underlying algorithms behind these actions sound like they are at the puppeteer level: I tell my body to move, I use words to talk, I remember things by thinking about them. In short, I have no idea how I really do it. The explanations or understandings available to the conscious mind develop independently of the underlying subconscious algorithms. Our conscious understanding is based only on the information available to conscious awareness. While we are aware of much of the sensory data used by the brain, we have limited access to the subconscious processing performed on that data, and consequently limited access to the information it contains. What ends up happening is that we invent our own view of the world, our own way of understanding it, using only the information we can access through awareness and the subconscious and conscious skills that go with it. What this means is that our whole understanding of the world (including ourselves) is woven out of information we derive from our awareness and not from the physical world itself, which we only know second-hand. Exactly like a sculptor, we build a model of the world, similar to it in as many ways as we can make it feel similar, but at all times just a representation and not the real thing. While we evolved to develop this kind of understanding, it depends heavily on the memories we record over our lifetimes (both consciously accessible and subconsciously not). As the mind develops from infancy, it acquires information from feedback that it can put to use, and it thinks of this information as “knowledge” because it works, i.e. it helps us to predict and consequently to control. To us, it seems that the mind has a hotline to reality. Actually, though, the knowledge is entirely contextual within the mind, not reality itself but only representative of it. But by representing it the contexts or models of the conscious mind arise: the conscious mind has no choice but to believe in itself because that is all it has.

Speaking broadly, subconscious algorithms perform specialized informational tasks like moving a limb, remembering a word, seeing a shape, and constructing a phrase. Consciously, we don’t know how they do it. Conscious algorithms do more generalized tasks, like thinking of ways to find food or making and explaining plans. We know how we do these things because we think them through. Conscious algorithms provide centralized, coordinated control of subconscious (and other conscious) algorithms. Only the top layer of centralized control is done consciously; much can be done subconsciously. For example, all our habitual behavior starts under conscious development and is then delegated to the subconscious going forward. As the control central, though, the buck stops with the conscious mind; it is responsible for reviewing and approving, or, in the case of habitual behavior, preapproving, all decisions. Some recent studies impugn this decisive capacity of the conscious mind with evidence that we make decisions before we are consciously aware that we have done so.1 But that doesn’t undermine the role of consciousness, it just demonstrates that to operate with speed and efficiency we can preapprove behaviors. Ideally, the conscious mind can make each sort of decision just once and self-program to reapply that decision as needed going forward without having to repeat the analysis. It is like a CEO who never pulls triggers himself but has others to do it for him, but continually monitors to see if things are being done right.

I thus conclude that the conscious mind is a subprocess of the mind that exists to make decisions and that it does it using perspectives called knowledge that are only meaningful locally (i.e. in the context of the information under its management) and that these contexts are distilled from information fed to it by subconscious processes. The conscious mind is separate from the subconscious mind for practicality reasons. The algorithmic details of subconscious tasks are not relevant to centralized control. We subconsciously metabolize, pump blood, breathe, blink, balance, hear, see, move, etc. We have conscious awareness of these things only to the degree we need to to make decisions. For example, we can’t control metabolization and heartbeat (at least without biofeedback), and we consequently have no conscious awareness of them. Similarly, we don’t control what we recognize. Once we recognize something, we can’t see it as something else (unless an alternate recognition occurs). But we need to be aware of what we recognize because it affects our decisions. We breathe and blink automatically, but we are also aware we are doing it so we can sometimes consciously override it. So the constant stream of information from the subconscious mind that flows past our conscious awareness is just the set we need for high-level decisions. The conscious mind is unaware how the subconscious does these things because this extraneous information would overly complicate its task, slowing it down and probably compromising its ability to lead. We subjectively know the limits of our conscious reach, and we can also see evidence of all the things our brains must be doing for us subconsciously. I suspect this separation extends to the whole animal kingdom, which is nearly all comprised of bilateral animals having one brain. Octopuses are arguably an exception as they have separate brains for each arm, but the central octopus brain must still have some measure of high-level control over them, perhaps in the form of an awareness, similar to our consciousness. Whether each arm also has some degree of consciousness is an open question.2 Although a separate consciousness process is not the only possible solution to centralized control, it does appear to be the solution evolution has favored, so I will take it as my working assumption going forward.

One can further subdivide the subconscious mind along functional lines into what are called modules, which are specialized functions that also seem to have specialized physical areas of the brain that support them. Steven Pinker puts it this way:

The mind is what the brain does; specifically, the brain processes information, and thinking is a kind of computation. The mind is organized into modules or mental organs, each with a specialized design that makes it an expert in one arena of interaction with the world. 3
The mind is a set of modules, but the modules are not encapsulated boxes or circumscribed swatches on the surface of the brain. The organization of our mental modules comes from our genetic program, but that does not mean that there is a gene for every trait or that learning is less important than we used to think.4

Positing that the mind has modules doesn’t tell us what they are or how they work. Machines are traditionally constructed from parts that serve specific purposes, but design refinements (e.g. for miniaturization) can lead to a streamlining of parts that are fewer in number, but that holistically serve more functions. Having been streamlined by countless generations, the modules of the mind can’t be as easily distinguished along functional boundaries as the other parts of the body because they all perform information management in a highly collaborative way. But if we accept that any divisions we make are preliminary, we can get on with it without getting too caught up in the details. Drawing such lines is reverse engineering. Evolution engineered us, explaining what it did is reverse engineering. Ideally one learns enough from reverse engineering to build a duplicate mechanism from scratch. But living things were “designed” from trillions of small interactions spread over billions of years. We can’t identify those interactions individually, and in any event, natural selection doesn’t select for individual traits but for entire organisms, so even with all the data one would be hard-pressed to be sure what caused what. However, if one generalizes, that is, if one applies statistical reasoning, one can distinguish functional advantages of one trait over another. And considering that all knowledge and understanding are the product of such generalizing, it is a reasonable strategy. Again, it is not the objective of knowledge to describes things “as they are,” only to create models or perspectives that abstract or generalize certain features. So we can and should try to subdivide the mind into modules and guess how they interact, with the understanding that there is more than one way to skin this cat and greater clarity will come with time.

Subdividing the mind into consciousness and a number of subconscious components will do much to elucidate how the mind provides its centralized control function, but the next most critical aspect to consider is how it manages information. Information derives from the analysis of data, the separation of useful data (the wheat) from noisy data (the chaff). Our bodies use at least two physical mechanisms to record information: genes and memory. Genes are nature’s official book of record, and many mental functions have extensive instinctive support encoded by genes. We have fully decoded all our genes and have identified some functions of some of them. Genes either code for proteins or they help or regulate those that do. Their function can be viewed narrowly as a biochemical role or more broadly as the benefit conferred to the organism. We are still a long way off from connecting the genes to the biochemical roles, and further still from connecting to benefits. Even with good explanations for everything questions will always remain because billions of years of subtlety are coded into genes, and models for understanding invariably generalize that subtlety away.

Memory is an organism’s book of record, responsible for preserving any information it gleans from experience, a process also called learning. We don’t yet understand the neurochemical basis of memory, though we have identified some of the chemicals and pathways involved. Nurture (experience) is often steered by nature (instinct) to develop memory. Some of our instinctive skills work automatically without memory but must leverage memory for us to achieve mastery of a learned behavior. We are naturally inclined to learn to walk and talk but are born with no memory of steps or words. So we follow our genetic inclinations, and through practice we record models in memory that help us perform the behaviors reliably.

Genes and memory store information of completely incompatible types and formats. Genetic information encodes chemical structures (either mRNA or proteins) which translate to function mostly through proteins and gene regulation. Memory encodes objects, events and other generalizations which translate to function through indirection, mostly by correlating memory with reality. Genetic information is physical and is mechanically translated to function. Remembered information is mental and is indirectly or abstractly translated to function. While both ultimately get the job done, the mind starts out with no memory as a tabula rasa (blank slate) and assembles and accumulates memory as a byproduct of cogitation. Many algorithmic skills, like vision processing, are genetically prewired, but on-the-job training leverages memory (e.g. recognition of specific objects). In summary, genes carry information that travels across generations while memory carries information transient to the individual.

I mentioned before that culture is another reservoir of information, but it doesn’t use an additional biological mechanism. While culture depends heavily on our genetic nature, significantly on language, we reserve the word culture for additions we make beyond our nature and ourself. Language is an innate skill; a group of children with no language can create a completely vocabulary and grammar themselves in a few years. Therefore, cultural information is not stored in genes but only in memory, and it is also stored in artifacts as a form of external memory. Each of us forms a unique set of memories based on our own experience and our exposure to culture. What an apple is to each of us is a unique derivation of our lifetime exposure to apples, but we all share general ideas (knowledge) about what one can do with apples. We create memories of our experiences using feedback we ourselves collect. Our memory of culture, on the other hand, is partially based on our own experiences and partially on the underlying cultural information others created. Cultural institutions, technologies, customs, and artifacts have ancient roots and continually evolve. Culture extends our technological and psychological reach, providing new ways to control the world and understand our place in it. While cultural artifacts mediate much of the transmission of culture, most culture is acquired from direct interaction with other people via spoken language or other activities. Culture is just a thin veneer sitting on top of our individual memories, but it is the most salient part to us because it encodes so much of what we can share.

To summarize so far, we have conscious and subconscious minds that manage information using memory. The conscious mind is distinct from the subconscious as the point where relevant information is gathered for top-level centralized control. But why are conscious minds aware? Couldn’t our top-level control process be unaware and zombie-like? No, it could not, and the analogy to zombies or robots reveals why. While we can imagine an automaton performing a task effectively without consciousness, as indeed some automated machines do, we also know that they lack the wherewithal to respond to unexpected circumstances. In other words, we expect zombies and robots to have rigid responses and to be slow or ineffective in novel situations. This intuition we have about them results from our belief that simple tasks can be automated, but very general tasks require generalized thinking, which in turn requires consciousness. I’m going to explain why this intuition is sound and not just a bias, and in the process we will see why the consciousness process must be aware of what it is doing.

I have so far described the consciousness process as being a distinct subprocess of the mind which is supplied just the information relevant to high-level decisions from a number of subconscious processes, many of them sensory but also memory, language, spatial processing, etc. Its task is to make high-level decisions as efficiently and efficaciously as possible. I can’t prove that this design is the only possible way of doing things, but it is the way the human mind is set up. And I have spoken in general about how knowledge in the mind is contextual and is not identical to reality but only representative of it. But now I am going to look closer at how that representative knowledge causes a mind to “believe in itself” and consequently become aware. It is because we create virtual worlds (called mental models, or models for short) in our heads that look the same as the outside world. We superimpose these on the physical world and correlate them so closely that we can usually ignore the distinction. But they could not be more different. One of them is out there, and the other in here. One exists only physically, the other only mentally (albeit with the help of a physical computational mechanism, the brain). One is detailed down to atoms and then quarks, while the other is a network of generalizations with limited detail, but extensive association. For this reason, a model can be thought of as a simplified, cartoon-like representation5 of physical reality. Within the model, one can do simple, logical operations on this abridged representation to make high-level decisions. Our minds are very handy with models; we mostly manage them subconsciously and can recognize them much the same way we recognize objects. We automatically fit the world to a constellation of models we manage subconsciously using model recognition.

So the approach consciousness uses to make top level decisions is essentially to run simulations: it builds models that correlate well to physical conditions and then projects the models into the future to simulate what will happen. Consciousness includes models of future possibilities and models of current and past experiences as we observed them. We can’t remember the actual past as it actually was, only how we experienced it through our models. All our knowledge is relative to these models, which in turn relate indirectly to physical reality. But where does awareness fit in? Awareness is just the data managed by this process. We are aware of all the information relevant to top-level decisions because our conscious selves are this consciousness process in the brain. Not all the data within our awareness is treated equally. Since much more information is sensed and recognized than is needed for decisions, the data is funneled down further through an attention process that focuses on just select items in consciousness.6 As I noted before, we can apply our focusing power on anything within our conscious awareness at will to pull it into attention, but our subconscious attention process continually identifies noteworthy stimuli for us to focus on, and it does it by “listening” for signals that stand out from the norm. We know from experience that although we are aware of a lot of peripheral sensory information and peripheral thoughts floating around in our heads at any given point in time, we can only actively think about one thing at a time, in what seems to us as a train of thought where one thought follows another. This linear, plodding approach to top-level decision making ensures that the body will make just one coordinated action at a time because we don’t have to compete with ourselves like a committee every time we do something.

Let’s think again about whether minds could be robotic again. Self-driving cars, for example, are becoming increasingly capable of executing learned behaviors, and even expanding their proficiency dynamically, without any need for awareness, consciousness, reasoning, or meaning. But even a very good learned behavior falls far short of the range of responses that animals need to compete in an evolutionary environment. Animals need a flexible ability to assess and react to situations in a general way, that is, by considering a wide range of past experience. The modeling approach I propose for consciousness can do that. If we programmed a robot to use this approach, it would both internally and externally behave as if it were aware of the data presented to it, which is wholly analogous to what we do. It will have been programmed with a consciousness process that considers access to data “awareness”. Could we conclude that it had actually become aware? I think we could because it meets the logical requirements, although this doesn’t mean robotic awareness would be as rich an experience of awareness as our own. A lot goes into the richness of our experience from billions of years of tweaks that would take us a long time to replicate faithfully in artificial minds. But it is presumptuous of us to think that our awareness, which is entirely a product of data interpretation, is exclusive just because we
are inclined to feel that way.

Let me talk for a moment about that richness of experience. How and why our sensory experiences (called qualia) feel the way they do is what David Chalmers has famously called the hard problem of consciousness. The problem is only hard if you are unwilling to see consciousness as a subroutine in the brain that is programmed to interpret data as feelings. It works exactly the way it does because it is the most effective way that has evolved to get bodies to take all the steps they need to survive. As will be discussed in the next section, qualia are an efficient way to direct data from many external channels simultaneously to the conscious mind. The channels and the attention process focus the relevant data, but the quality or feeling of the qualia results from subconscious influences the qualia exert. Taste and smell simplify chemical analyses down for the conscious mind into a kind of preference. Color and sound can warn us of danger or calm us down. These qualia seem almost supernatural but they actually just neatly package up associations in our minds so we will feel like doing the things that are best for us. Why do we have a first-person experience of them? Here, too, it is nothing special. First-person is just the name we give to this kind of processing. If we look at our, or someone else’s, conscious process more from a third-person perspective we can see that what sets it apart is just the flood of information from subconscious processes giving us a continuous stream of sensations and skills that we take for granted. First person just means being connected so intimately to such a computing device.

Now think about whether robots can be conscious. Self-driving cars use a specialized algorithm that consults millions of hours of driving experience to pick the most appropriate responses. These cars don’t reason out what might happen in different scenarios in a general way. Instead, they use all that experience to look up the right answer, more or less. They still use internal models for pedestrians, other cars, roads, etc, but once they have modeled the basic circumstances they just look up the best behavior rather than reasoning it out generally. As we start to build robots that need more flexibility we may well design the equivalent of a conscious subprocess, i.e. a higher-level process that reasons with models. If we also use the approach of giving it qualia that color its preferences around its sensory inputs in preprogrammed (“subconscious”) ways to simplify the task at the conscious level, then we will have built a consciousness similar to our own. But while we may technically meet my definition of consciousness and while such a robot may even be able to convince people into thinking it is human sometimes (i.e. pass the Turing test), that alone won’t mean it experiences qualia anywhere near as rich as our own, and that is because we have more qualia which encode more preferences in a highly interconnected and seamless way following billions of years of refinements. Brains and bodies are an impressive accomplishment. But they are ultimately just machines, and it is theoretically possible to build them from scratch, though not with the approaches to building we have today.

The Certainty Engine

The Certainty Engine: How Consciousness Arose to Drive Decisions Through Rationality

The mind’s organization as we experience it revolves around the notion of certainty. It is a certainty engine. It is designed so as to enable us to act with the full expectation of success. In other words, we don’t just act confidently because we are brash, but because we are certain. It is a surprising capacity, given that we know the future is unknowable. We know we can’t be certain about the future, and yet at the same time we feel certain. That feeling comes from two sources, one logical and one psychological.

Logically, we break the world down into chunks which follow rules of cause and effect. We gather these chunks and rules into mental models (models for short) where certainty is possible because we make the rules. When we think logically, we are using these model models to think about the physical world, because logic, and cause and effect, only exist in the models; they exist mentally but not physically. Cause and effect are just illusions of the way we describe things — very near and dear to our hearts — but not scientific realities. The universe follows its clockwork mechanism according to its design, and any attempt to explain what “caused” what after the fact is going to be a rationalization, which is not necessarily a bad thing, but it does necessarily mean simplifying down to an explanatory model in which cause and effect become meaningful concepts. Consequently, if something is true in a model, then it is a logical certainty in that model. We are aware on some level that our models are simplifications that won’t perfectly match the physical world, but on another level, we are committed to our models because they are the world as we understand it.

Psychologically, it wouldn’t do for us to be too scared to ever act for fear of making a mistake, so once our confidence reaches a given threshold we leap. In some of our models we will succeed while in others we will fail. Most of our actions succeed. This is because most of our decisions are habitual and momentary, like putting one foot in front of the other. Yes, we know we could stub our toe on any step, and we have a model for that, but we rarely think about it. Instead, we delegate such decisions to our subconscious minds, which we trust both to avoid obstacles and to alert us to them as needed, meaning to the degree avoidance is more of a challenge than the subconscious is prepared to handle. For any decision more challenging than habit can handle we try to predict what will happen, especially with regard to what actions we can take to change the outcome. In other words, we invoke models of cause and effect. These models stipulate that certain causes have certain effects, so the model renders certainty. If I go to the mailbox to get the mail and the mailman has come today, I am certain I will find today’s mail. Our plans fail when our models fail us. We didn’t model the situation well enough, either because there were things we didn’t know or conclusions that were insufficiently justified. The real world is too complicated to model perfectly, but all that matters is that we model it well enough to produce predictions that are good enough to meet our goals. Our models simplify to imply logical outcomes that are more likely than chance to come true. This property, which separates information from noise, is why we believe a model, which is to say we are psychologically prepared to trust the certainty we feel about the model enough to act on it and face the consequences.

What I am going to examine how this kind of imagination arose, why it manifests in what we perceive as consciousness, and what it implies for how we should lead our lives.

To the fundamental question, “Why are we here?”, the short answer is that we are here to make decisions. The long answer will fill this book, but to elaborate some, we are physically (and mentally) here because our evolutionary strategy for survival has been successful. That mental strategy, for all but the most primitive of animals, includes being conscious with both awareness and free will, because those capacities help with making decisions, which translates to acting effectively. Decision-making involves capturing and processing information, and information is the patterns hiding in data. Brains use a wide variety of customized algorithms, some innate and some learned, to leverage these patterns to predict the future. To the extent these algorithms do not require consciousness I call them subrational. If all of them were subrational then there would be no need for subjective experience; animals could go about their business much like robots without any of the “inner life” which characterizes consciousness. But one of these talents, reasoning, mandates the existence of a subjective theater, an internal mental perspective which we call consciousness, that “presents” version(s) of the outside world to the mind for consideration as if they were the outside world. All but the simplest of animals need to achieve a measure of the certainty of which I have spoken and to do that they need to model worlds and map them to reality. This capacity is called rationality. It is a subset of the reasoning process, with the balance being our subrational innate talents, which proceed without such modeling (though some support it or leverage it). Rationality mandates consciousness, not as a side effect but because reasoning (which needs rationality) is just another way of describing what consciousness is. That is, our experience of consciousness is reasoning using the faculties we possess that help us do so.

At its heart, rationality is based on propositional logic, a well-developed discipline that consists of propositions and rules that apply to them. Propositions are built from concepts, which are references that can be about, represent, or stand for things, properties and states of affairs. Philosophers call this “aboutness” that concepts possess “intentionality”, and divide mental states into those that are intentional and those are merely conscious, i.e. feelings, sensations and experiences in our awareness1. To avoid confusion and ambiguity, I will henceforth simply call intentional states “concepts” and conscious states “awareness”. Logic alone doesn’t make rationality useful; concepts and conclusions have to connect back to the real world. To accomplish this they are built on an extensive subrational infrastructure, and understanding that is a big part of understanding how the mind works.

So let’s look closer at the attendant features of consciousness and how they contribute to rationality. Steven Pinker distinguishes four “main features of consciousness — sensory awareness, focal attention, emotional coloring, and the will.”2 The first three of these are subrational skills and the last is rational. Let’s focus on subrational skills for now, and we will get to the rational will, being the mind’s control center, further down. The mind also has many more kinds of subrational skills, sometimes called modules. I won’t focus too much on exact boundaries or roles of modules as that is inherently debatable, but I will call out a number of abilities as being modular. Subrational skills are processed subconsciously, so we don’t consciously sense how they work; they appear to work magically to us. We do have considerable conscious awareness and sometimes control over these subrational skills, so I don’t simply call them “subconscious”. I am going to briefly discuss our primary subrational skills.

First, though, let me more formally introduce the idea of the mind as a computational engine. The idea that computation underlies thinking goes back at least 500 years to Thomas Hobbes who said “by reasoning, I understand computation. And to compute is to collect the sum of many things added together at the same time, or to know the remainder when one thing has been taken from another. To reason therefore is the same as to add or to subtract.” Alan Turing and Claude Shannon developed theories of computability and information from the 1930’s to the 1950’s that led to Hilary Putnam formalizing the Computational Theory of Mind (CTM) in 1961. As Wikipedia puts it, “The computational theory of mind holds that the mind is a computation that arises from the brain acting as a computing machine, [e.g.] the brain is a computer and the mind is the result of the program that the brain runs”. This is not to suggest it is done digitally as our computers do it; brains use a highly customized blend of hardware (neurochemistry) and software (information encoded with neurochemistry). At this point we don’t know how it works except for some generalities at the top and some details at the bottom. Putnam himself abandoned CTM in the 1980’s, though in 2012 he resubscribed to a qualified version. I consider myself an advocate of CTM, provided it is interpreted from a functional standpoint. It does not matter how the underlying mechanism works, what matters is that it can manipulate information, which as I have noted does not consist of physical objects but of patterns that help predict the future. So nothing I am going to say in this book is dependent on how the brain works, though it will not be inconsistent with it either. While we have undoubtedly learned some fascinating things about the brain in recent years, none of it is proven and in any case it is still much too small a fraction of the whole to support many conclusions. So I will speak of the information management done in the brain as being computational, but that doesn’t imply numerical computations, it only implies some mechanism that can manage information. I believe that because information is abstract, the full range of computation done in human minds could be done on digital computers. At the same time, different computing engines are better suited to different tasks because the time it takes to compute can be considerable, so to perform well an engine must be finely tuned to its task. For some tasks, like math, digital computers are much better suited than human brains. For others, like assessing sensory input and making decisions that match that input against experience, they are worse (for now). Although we are a long way from being able to tune a computer as well to its task as our minds are to our task of survival, computers don’t have to match us in all ways to be useful. Our bodies are efficient, mobile, self-sustaining and self-correcting units. Computers don’t need to be any of those things to be useful, though it helps and we are making improvements in these areas all the time.

So knowing that something is computed and knowing how it is done are very different things. We still only have vague ideas about the mechanisms, but we can still deduce much about how it works just by knowing it is computational. We know the brain doesn’t use digital computing, but there are many approaches to information processing and the brain leverages a number of them. Most of the deductions I will promote here center around the distinction between computations done consciously (and especially under conscious attention) and those done subconsciously. We know the brain performs much information processing of which we have no conscious awareness, including vision, associative memory lookup, language processing, and metabolic regulation, to name a few kinds. We know the subconscious uses massively parallel computing, as this is the only way such tasks could be completed quickly and thoroughly enough. Further, we know that the conscious mind largely feels like a single train of thought, though it can jump around a lot and can sense different kinds of things at the same time without difficulty.

Looking at sensory awareness, we internally process sensory information into qualia (singular quale, pronounced kwol-ee), which is how each sense feels to us subjectively. This processing is a computation and the quale is a piece of data, nothing more, but we are wired to attach a special significance to it subjectively. We can think of the qualia as being data channels into our consciousness. Consciousness itself is a computational process that interprets the data from each these channels in a different way, which we think of as a different kind of feeling, but which is really just data from a different channel. Beyond this raw feel we recognize shapes, smells, sounds, etc. via the subrational skills of recollection and recognition, which bring experiences and ideas we have filed away back to us based on their connections to other ideas or their characteristics. This information is fed through a memory data channel. Interestingly, the memory of qualia has some of the feel of first-hand qualia, but is not as “vivid” or “convincing”, though sometimes in dreams it can seem to be. This is consistent with the idea that our memory can hold some but not all of the information the data channels carried.

Two core subrational skills let us create and use concepts: generalizing and modeling. Generalization is the ability to recognize patterns and to group things, properties, and ideas into categories called concepts. I consider it the most important mental skill. Generalizations are abstractions, not of the physical world but about it. A concept is an internal reference to a generalization in our minds that lets us think about the generalization as a unit or “thing”. Rational thought in particular only works with concepts as building blocks, not with sensations or other kinds of preconceptual ideas. Modeling itself is a subrational skill that builds conceptual frameworks that are heavily supported by preconceptual data. We can take considerable conscious control of the modeling process, but still the “heavy lifting” is both subrational and subconscious, just something we have a knack for. It is not surprising; our minds make the work of being conscious seem very easy to us so that we can focus with relative ease on making top-level decisions.

There are countless ways we could break down our many other subrational skills, with logical independence from each other and location in the brain being good ones. Harvard psychologist Howard Gardner identified eight types of independent “intelligences” in his 1983 book Frames of Mind: The Theory of Multiple Intelligences3: musical, visual-spatial, verbal-linguistic, logical-mathematical, bodily, interpersonal, intrapersonal and naturalistic. MIT neuroscientist Nancy Kanwisher in 2014 identified specific brain regions that specialize in shapes, motion, tones, speech, places, our bodies, face recognition, language, theory of mind (thinking about what other people are thinking), and “difficult mental tasks”.4 As with qualia and memory, most of these skills interact with consciousness via their own kind of data channel.

Focus itself is a special subrational skill, the ability to weigh matters pressing on the mind for attention and then to give focus to those that it judges most important. Rather than providing an external data channel into consciousness, focus controls the data channel between conscious awareness and conscious attention. Focusing itself is subrational and so its inner workings are subconscious, but it appears to select the thoughts it sends to our attention by filtering out repetitive signals and calling attention to novel ones. We can only apply reasoning to thoughts under attention, though we can draw on our peripheral awareness of things out of focus to bring them into focus. While focus works automatically to bring interesting items to our attention, we have considerable conscious control to keep our attention on anything already there.

Drives are another special kind of subrational skill that can feed consciousness through data channels with qualia of their own. A drive is logically distinct from the other subrational skills in that it creates a psychological need, a “negative state of tension”, that must be satisfied to alleviate the tension. Drives are a way of reducing psychological or physiological needs to abstractions that can be used to influence reasoning, to motivate us:

A motive is classified as an “intervening variable” because it is said to reside within a person and “intervene” between a stimulus and a response. As such, an intervening variable cannot be directly observed, and therefore, must be indirectly observed by studying behavior.5

Just rationally thinking about the world using models or perspectives doesn’t by itself give us a preference for one behavior over another. Drives solve that problem. While some decisions, such as whether our heart should beat, are completely subconscious and don’t need motivation or drive, others are subconscious yet can be temporarily overridden consciously, like blinking and breathing. These can be called instinctive drives because we start to receive painful feedback if we stop blinking or breathing. Others, like hunger, require a conscious solution, but the solution is still clear: one has to eat. Emotions have no single response that can resolve them, but instead provide nuanced feedback that helps direct us to desirable objectives. Our emotional response is very context-sensitive in that it depends substantially on how we have rationally interpreted, modeled and weighed our circumstances. But emotional response itself is not rational; an emotional response is beyond our conscious control. Since it depends on our rational evaluation of our circumstances, we can ameliorate it by reevaluating, but our emotions have access to our closely-held (“believed”) models and can’t be fooled by those we consider only hypothetically.

We have more than just one drive (to survive) because our rational interactions with the world break down into many kinds of actions, including bodily functions, making a living, having a family, and social interactions.6 Emotions provide a way of encoding beneficial advice that can be applied by a subjective, i.e. conscious, mind that uses models to represent the world. In this way, drives can exert influence without simply forcing a prescribed instinctive response. And it is not just “advice”; emotions also insulate us from being “reasonable” in situations where rationality would hurt more than help. Our faces betray our emotions so others can trust us.7 Romantic love is a very useful subrational mechanism for binding us to one other person as an evolutionary strategy. It can become frustratingly out of sync with rational objectives, but it has to have a strong, irrational, even mad, pull on us if it is to work.8

Although our conscious awareness and attention exist to support rationality, this doesn’t mean people are rational beings. We are partly rational beings who are driven by emotions and other drives. Rather than simply prescribing the appropriate reaction, drives provide pros and cons, which allow us to balance our often conflicting drives against each other by reasoning out consequences of various solutions. For any system of conflicting interests to persist in a stable way, one has to develop rules of fair play or each interest will simply fight to the death, bringing the system down. Fair play, also known as ethics, translates to respect: interests should respect each other to avoid annihilation. This applies to our own competing drives and interpersonal relationships. The question is, how much respect should one show, on a scale of me first to me last? Selfishness and cooperation have to be balanced in each system accordingly. The ethical choice is presumably one that produces a system that can survive for a long time. And living systems all embrace differing degrees of selfishness and cooperation, proving this point. Since natural living systems have been around a long time, they can’t be unethical by this definition, so any selfishness they contain is justified by this fact. Human societies, on the other hand, may overbalance either selfishness or cooperation, leading to societies that fail, either by actually collapsing or by under-competing with other societies, which eventually leads to their replacement.

And so it is that our conscious awareness becomes populated with senses, memories, emotions, language, etc, which are then focused by our power of attention for the consideration of our power of reasoning. Of this Steven Pinker says:

The fourth feature of consciousness is the funneling of control to an executive process: something we experience as the self, the will, the “I.” The self has been under assault lately. The mind is a society of agents, according to the artificial intelligence pioneer Marvin Minsky. It’s a large collection of partly finished drafts, says Daniel Dennett, who adds, “It’s a mistake to look for the President of the Oval Office of the brain.”
The society of mind is a wonderful metaphor, and I will use it with gusto when explaining the emotions. But the theory can be taken too far if it outlaws any system in the brain charged with giving the reins or the floor to one of the agents at a time. The agents of the brain might very well be organized hierarchically into nested subroutines with a set of master decision rules, a computational demon or agent or good-kind-of-homunculus, sitting at the top of a chain of command. It would not be a ghost in the machine, just another set of if-then rules or a neural network that shunts control to the loudest, fastest or strongest agent one level down.9
The reason is as clear as the old Yiddish expression, “You can’t dance at two weddings with only one tuches.” No matter how many agents we have in our minds, we each have exactly one body. 10

While it may only be Pinker’s fourth feature, it is the whole reason for consciousness. We have a measure of conscious awareness and control over our subrational skills only so that they can help with reasoning and thereby allow us to make decisions. This culmination into a single executive control process is a logical necessity given one body, but that it should be conscious or rational is not so much necessary as useful. Rationality is a far more effective way to navigate an uncertain world than habit or instinct. Perhaps we don’t need to create a model to put one foot in front of the other or chew a bite of food. But paths are uneven and food quality varies. By modeling everything in many degrees of detail and scope, we can reason out solutions better than more limited heuristical approaches of subrational skills. Reasoning brings power, but it can only work if the mind can manage multiple models and map them to and from the world, and that is a short description of what consciousness is. Consciousness is the awareness of our senses, the creation (modeling) of worlds based on them, and the combined application of rational and subrational skills to make decisions. Our decisions all have some degree of rational oversight, though we can, and do, grant our subrational skills (including learned behaviors) considerable free reign so we can focus our rational energies on more novel aspects of our circumstances.

Putting the shoe on the other foot, could reasoning exist robotically without the inner life which characterizes consciousness? No, because what we think of as consciousness is mostly about running simulations on models we have created to derive implications and reactions, and measuring our success with sensory feedback. It would feel correct to us to label a robot doing those things as conscious, and it would be able to pass any test of consciousness we cared to devise. It, like us, would metaphorically have only one foot in reality while its larger sense of “self” would be conjecturing and tracking how those conjectures played out. For the conscious being, life is a game played in the head that somewhat incidentally requires good performance in the physical world. Of course, evolved minds must deliver excellent performance as only the fittest survive. A robot consciousness, on the other hand, could be given different drives to fit a different role.

To summarize, one can draw a line between conscious beings and those lacking consciousness by dividing thoughts into a conceptual layer and the support layers beneath it. In the conceptual layer, information has been generalized into packets called concepts which are organized into models which gather together the logical relationships between concepts. The conceptual layer itself is an abstraction, but it connects back to the real world whenever we correlate our models with physical phenomena. This ability to correlate is another major subrational skill, though it can be considered a subset of our modeling ability. Beneath the conceptual layer are preconceptual layers or modules, which consists of both information and algorithms that capture patterns in ways that have proven useful. While the rational mind only sees the conceptual layer, some subrational modules use both preconceptual and conceptual data. Emotions are the most interesting example of a subrational skill that uses conceptual data: to arrive at an emotional reaction we have to reason out whether we should feel good or bad, and once we have done that, we experience the feeling so long as we believe the reasoning (though feelings will fade if their relevance does). Only if our underlying reasoning shifts will our feelings change. This will happen quickly if we discover a mistake, or slowly as our reasoned perspective evolves over time.

One can picture the mind then as a tree, where the part above the ground is the conceptual layer and the roots are the preconceptual layers. Leaves are akin to concepts and branches to models. Leaves and branches are connected to the roots and draw support from them. The above-ground, visible world is entirely rational, but reasoning without connecting back to the roots would be all form and no function. So, like a tree “feeling” its roots, our conscious awareness extends underground, anchoring our modeled creations back to the real world.

An overview of computation and the mind

[Brief summary of this post]

I grew up in the 60’s and 70’s with a tacit understanding that thinking was computing and that artificial intelligence was right around the corner. Fifty years later we have algorithms that can simulate some mental tasks, but nothing close to artificial intelligence, and no good overall explanation of how we think. Why is that? It’s just that the problem is harder than we first thought. We will get there, but we need to get a better grasp of what the problem is and what would qualify as a solution. Our accomplishments over seventy or so years of computing include developing digital computers, making them much faster, demonstrating that they can simulate anything requiring computation, and devising some nifty algorithms. Evolution, on the other hand, has spent over 550 million years working on the mind. Because it is results-oriented, it has simultaneously developed better hardware and software to deliver minds most capable of keeping animals alive. The “hardware” includes the neurons and their electrochemical mechanisms, and the “software” includes a memory of things, events, and behaviors that supports learning from experience. If the problem we are trying to solve is to use computers to perform tasks that only humans have been able to do, then we have laid all the groundwork. More and more algorithms that can simulate many human accomplishments will appear as our technology improves. But my interest is in explaining how the brain manages information to solve problems and why and how brains have minds. To solve that problem, let’s take a top-down or reverse-engineered view of computation.

Computation is not just manipulation of numbers by rules or data by instructions. More abstractly, the functional conception of computation is a process performed on inputs that yields outputs that can be used to reduce uncertainty, which can be used in feedback loops to achieve predictable outcomes. Any input or output that can reduce uncertainty is said to contain information. White noise is data that is completely free of usable information. Minds, in particular, are computers because they use inputs from the environment and their own experience to produce output actions that facilitate their survival. If we agree that minds are processing information in this way, and exclude the possibility that supernatural forces assist them, then we can conclude that we need a computational theory of mind (CTM) to explain them.

Reverse engineering all the algorithms used by the brain down to the hardware and software mechanisms that support them is a large project. My focus is a top-down explanation, so I am going to focus just on the algorithms involved at the highest level of control that decide what we do. Most of the hard work, from a computational perspective, happens at the lower levels, so we will need to have a sense of what those levels are doing, but we won’t need to consider too closely how they do it. This is good because we don’t know much about the mechanics of brain function yet. What we do know doesn’t explain how it works so much as provide physical constraints with which any explanatory theory must be consistent. I will discuss some of those constraints in later in The process of mind.

At this point, I have to ask you to take a computational leap of faith with me. I will try to justify it as we go, but it is a hard point to prove, so once the groundwork has been laid we will have to evaluate whether we have enough evidence to prove it. The leap is this: the mind makes top-level decisions by reasoning with meaningful representations. This leap has a fascinating corollary: consciousness only exists to help this top-level representational decision-making process. Intuitively, this makes a lot of sense — we know we reason by considering propositions that have meaning to us, and we feel such reasoning directly supports many of our decisions. And our feeling of awareness or consciousness seems to be closely related to how we make decisions. But I need to explain what I mean by this from an objective point of view.

The study of meaning is called semantics. Semantics defines meaning in terms of the relationship between symbols, also called signifiers or representation, and what they refer to, also called referents or denotation. A model of the mind based on these kinds of relationships is called a representational theory of mind (RTM). I propose that these relationships form the meaningful representations that are the basis of all top-level reasoned decisions. I do not propose that everything in the mind is representational; most of what the mind does and experiences is not representational. RTM only applies to this top-level reasoning process. Some examples of information processing that are not representational include raw sensory perception, habitual behavior, and low-level language processing. To the extent we feel our senses without forming any judgments those feelings are free of meaning; they just are. So instrumental music consequently has no meaning. And to the extent we behave in a rote fashion, performing an action based on intuition or learned behavior without forming judgments, those actions are free of meaning, they just happen. Perhaps when we learned those behaviors we did form judgments, and if we recall those judgments when we use the behaviors then there is some residual meaning, but the meaning has become irrelevant and can be, and often will be, forgotten. People who tie their shoelaces by rote with no notion as to why the actions produce a knot have no detailed representation of knots; they just know they will end up with a knot. So much “intelligent” or at least clever behavior can take place without meaning. Finally, although language a critical tool, perhaps the critical tool, in supporting representational reasoning, as words and sentences can be taken as directly representing concepts and propositions, it only achieves this success through subconscious skills that understand grammar and tie concepts to words.

Importantly, just as we can tie knots by rote, we could, in theory, live our whole lives by rote without reasoning with represented objects (i.e. concepts) and, consequently, without conscious awareness. We would first need to be trained how to handle every situation we might encounter. While we don’t know how we could do that for people, we can train computers using an approach called machine learning. By training a self-driving car using feedback from millions of real-life examples, the algorithm can be refined to solve the driving problem from a practical standpoint without representation. Sure, the algorithm could not do as well as a human in entirely new conditions. For example, a human driver could quickly adapt to driving on the left side of the road in England, but the self-driving car might need special programming to flip its perspective. Also note that such algorithms do typically use representation for some elements, e.g. vehicles and pedestrians. But they don’t reason using these objects; they just use them to look up the best learned behaviors. So some algorithmic use of representation is not the same as using representation to support reasoning.

People can’t live their lives entirely by rote as we encounter novel situations almost continuously, so learned behavior is used more as another input to the reasoning process than as a substitute for it. Perhaps Mother Nature could have devised another way for us to solve problems as flexibly as reasoning can, but if so, we don’t know what that way might be. Furthermore, we appear quite unable to take actions that are the product of our top-level reasoning without explicit conscious awareness and attention in the full subjective theater of our minds. This experience, in surround-sound technicolor, is not at all incidental to the reasoning process but exists to provide reasoning with the most relevant information at all times.

Note that language is entirely representational by its very nature. Every word represents something. Just what words represent is more complex. Words are a window into both representational and nonrepresentational knowledge. They can be used in a highly formalized way to represent specific concepts and propositions about them in a logical, reasoned way. Or they can be used more poetically to represent feelings, impressions or mood. In practice, they will evoke different concepts and connotations in different people in different contexts as our minds interpret language at both conscious and subconscious levels. My focus on language will be more toward the rational support it provides consciousness to support reasoning with concepts, many of which represent things or relationships in the real world.

The top-down theory of mind (TDTM) I am proposing says that all mental functions use CTM, but only some processes use RTM. Further, I propose that consciousness exists to support reasoning, which critically depends on RTM while also seamlessly integrating with nonrepresentational mental capacities. While I am not going to review other theories at this time, conventional RTM theories propose that meaning ends with representation, while I say it is only the outer layer of the onion. Similarly, associative or connectionist theories explain memory and learned behavior with limited or no use of representation, as I have above, but do not propose a process that can compete with reasoning.

While the above provides some objective basis for reasoning as a flexible mental tool and consciousness as a way to efficiently present relevant information to the reasoning process, it does not say why we experience consciousness the way we do. We know we strive tenaciously and are fairly convinced, if we ask ourselves why, that it is because we have desires. That is, it is not because we know we have to survive and reproduce to satisfy evolution, but because the things we want to do usually include living and procreating. So apparently, working on behalf of our desires is a subjective equivalent to the objective struggle for survival. But why do we want things instead of just selecting actions that will best propagate our genes? Why does it feel like we’ve each got a quarter in the greatest video game ever made, a virtual reality first-person shooter that takes virtual reality to a whole new level — let’s call it “immersive reality” — in which we are not just playing the game, we are the game? Put simply, it is because life is a game, so it has to play like one. A game is an activity with goals, rules, challenges, and interaction. The imperatives of evolution create the goals and rules evolve around them. But the rules that develop are abstract ideas that don’t need a physical basis; they just need to get the job done.

The game of life has one main goal: survive. Earth’s competitive environment added a critical subgoal: perform at the highest level of efficiency and efficacy. And sexual selection, whose high evolutionary cost seems to be offset by the benefit of greater variation, led to the goal of sexual reproduction. But what could make animals pursue these goals with gusto? The truth is, animals don’t need to know what the goals are, they just need to act in such a way that they are attained. You could say theirs not to reason why, theirs but to do and die, in the sense that it doesn’t help animals in their mission to know why they eat certain foods, struggle relentlessly, or select certain mates. But it is crucial that they eat just the right amount of the foods that best sustain them and select the fittest mates. This is the basis of our desires. They are, objectively, nothing more than rules instructing us to prioritize certain behaviors to achieve certain goals. We are not innately aware of the ultimate goals, although as humans who have figured out how natural selection works, we now know what they are.

Our desires don’t force our hand; they only encourage us to prioritize our actions appropriately. We develop propositions about them that exist for us just as much as physical objects; they are part of our reality, which is a combination of physical and ideal. Closely held propositions are called beliefs. Beliefs founded in desires are subjective beliefs while beliefs founded in sensory perception are objective beliefs. Subjective beliefs could never be proven (as desires are inherently variable), but objective beliefs are verifiable. We learn strategies to fulfill desires. We learn many elements of the strategies we use to fulfill our most basic desires by following innate cues (instincts) for skills like mimicry, chewing, breathing, and so on. While we later develop conscious and reasoning control over these mostly innate strategies, it is only to act as a top-level supervisory capacity. So discussions of this top reasoning level are not intended to overlook the significance of well-developed underlying mechanisms that do the “real work”, much the way a company can function fairly well for a while without its CEO. With that caveat in mind, when we apply logical rules to propositions based on our senses, desires, and beliefs the implications spell out our actions. After we have developed detailed strategies for eating and mating, we still need to apply conscious reasoning all the time to apply prioritization to that cacophony of possibilities. We don’t need to know our evolutionary goals because our desires and the beliefs and strategies that follow from them are extremely well tuned to lead us to behaviors that will fulfill them.

Desires are fundamentally nonrepresentational in that they are experienced viscerally on a scale with greater or lesser force. They are not qualia themselves but the degree to which each quale appeals to us, which varies as a function of our metabolic state. So when we feel cold, we want warmth, and when we feel hunger, we want food. They are aids to prioritization and steer the decision-making process (both through reasoning and at levels below that). To reason with desires and subjective beliefs, we interpret them as weighted propositions using probabilistic logic. Because all relevant beliefs, desires, sensory qualia and memories are processed concurrently by consciousness, they all contribute to a continuous prioritization exercise that allows us to accomplish many goals appropriately despite having a serial instrument (one body). In other words, we have distinct qualia and as needed desires for them for the express purpose of ensuring all the relevant data is on the table before we make each decision.

So what is consciousness, exactly? Consciousness is a segregated aspect of the mind that uses qualia, memory, and expertise to make reasoned decisions. From a computational perspective, this means it is a subroutine fed data through custom data channels (in both nonrepresentational and representational ways) that has customized ways to process it. The nonrepresentational forms support whims or intuitions, and the representational forms support reasoned decisions. Importantly, reason has the last word; we can’t act, or at least not for very long, without support from our reasoning minds. Conversely, the conscious mind doesn’t exactly act itself, it delegates actions to the subconscious for execution, analogously to a manager and his employees. From a subjective perspective, the segregation of consciousness from the rest of the mind creates the subjective perspective or theater of mind. It seems to us to be a seamless projection of the external world into our heads because it is supposed to. We interpret what is actually a jumble of qualia as a smoothly flowing movie because the mandate to continuously prioritize and decide requires that we commit to the representation being the reality. To hesitate in accepting imagination as reality would lead to frequent and costly delays and mistakes. We consequently can’t help but believe that the world that floods into our conscious minds on qualia channels is real. It is not physically real, of course, but wetware really is running a program in our minds and that is real, so we can say that the world of our imagination, our conduit to the ideal world, is real as well, though in a different way.

Our objective beliefs, supported by our sensory qualia and memory, meet a very high objective standard, while our subjective beliefs, supported by our desires, are self-serving and only internally verifiable. Because our selfish needs often overlap with those of others and the ecosystem at large, they can often be fulfilled without stepping on any toes, but competition is an inescapable part of the equation. Our subjective beliefs give us a framework for prioritizing our interactions with others based entirely on abstracted preferences rather than literal evolutionary goals, based on desires tuned by evolution to achieve those goals. In other words, blindly following our subjective beliefs should result in self-preservation and the preservation of our communities and ecosystems. However, humans face a special challenge because we are no longer in the ancestral environment for which our desires are tuned, and we have free will and know how to use it. While this is a potential recipe for disaster, we will ultimately best satisfy our desires by artificially realigning them with evolutionary objectives. While our desires are immutable, the beliefs and strategies we develop to fulfill them are open to interpretation. In other words, we can use science and other tools of reasoning to help us adjust our subjective beliefs, through laws if necessary, to fulfill our desires in a way that is compatible with a sustainable future.

I call the portion of the conscious mind dedicated to reasoning the “single stream step selector”, or SSSS. While “just” a subprocess of the mind, it is the part of our conscious minds that we identify with most. The SSSS exercises free will in making decisions in both a subjective and objective sense. Subjectively we feel we are freely selecting from among possible worlds. We are also objectively free in a few ways, most significantly because our behavior is unpredictable, being driven by partially chaotic forces in our brains. Secondly, and more significantly to us as intelligent beings, our actions are optimized selections leveraging information management, i.e. computation, which doesn’t happen by chance or in simple natural systems. So without violating the determinism of the universe we nevertheless make things happen that would never happen without huge computational help.

The process of making decisions is much more involved than simply weighing propositions. Propositions in isolation are meaningless. What gives them meaning is context. Computationally, context is all the relationships between all the symbols used by the propositions. These relationships are the underlying propositions that set the ground rules for the propositions in question. Subjectively, a context is a model or possible world. Whenever we imagine a situation working according to certain rules, we have established a model in our minds. If the rules are somewhat flexible or not nailed down, this can be thought of as establishing a range of models. We keep a primary model (really a set of models covering different aspects) for the way we think the world actually is. We create future models for the ways we think things might go. We expect one of those future models to become real, in the sense that it should in time line up with the actual present within our limits of accuracy and detail. We keep a past model (again, really a set) for the way we think things were. Internally, our models support considerable flexibility, and we adapt them all the time as new information becomes available. Externally, at the moment we decide to do something, we have committed to a specific model and its implications. That model itself can be a weighted combination of several models that may be complementary or antagonistic to each other, but in any case, we are taking a stand. We have done an evaluation, either off the cuff or with deep forethought, of all the relevant information, using as many models as seem relevant to the situation and building new models we haven’t used before as we go if needed.

Viewed abstractly, what the mind is creating inside is a different kind of universe, a mental one instead of a physical one. Our mental universe is a special case of an ideal universe, in which ideas comprise reality. One could argue that the conscious and subconscious realms comprise distinct ideal universes which overlap in places. And one could argue that mathematical systems and scientific theories and our own models each comprise their own ideal world, bound by the rules that define them. Ideal worlds can be interesting in their own right for reasons abstracted from practical application, but their primary role is to help us predict real world behavior. To do this, we have to establish a mapping or correlation between the model and what we observe. Processing feedback from our observations and actions is called learning. We are never fully convinced our methods are perfect, so we are always evaluating how well they work and refining them. This approach of continuous improvement was successfully applied by Toyota (where it is called kaizen), but we do it automatically. It is worth noting at this point that the above argument solves the classic mind-body problem of how mental states are related to physical states, that is, how the contents of our subjective lives relate to the physical world. The answer I am proposing, a union of an ideal universe and a physical one, goes beyond this discussion on computation, but I will speak more on it later.

We have no access to our own programming and can only guess how the program is organized. But that’s ok; we are designed to function without knowing how the programming works or even that we are a program. We experience the world as our programming dictates: we strive because we are programmed to strive, and our whole UI (user interface) is organized to inspire us to relentlessly select steps that will optimize our chances of staying in the game. “Inspire” is the critical word here, meaning both “to fill with an animating, quickening, or exalting influence” (as a subjective word) and “to breathe in” (as an objective word). Is it a mystical force or a physical action? It sits at the confluence of the mental and physical worlds, capturing the essence of our subjective experience of being in the world and our physical presence in the world that comes one breath at a time. The physical part seems easy enough to understand, but what is the subjective part?

How does a program make the world feel the way it does to us? Yes, it’s an illusion. Nothing in the mind is real, after all, so it has to be an illusion. But it is not incidental or accidental. It all stems from the simple fact that animals can only do one thing at a time (or at least their bodies can only engage in one coordinated set of actions at a time). Most of all, they can’t be in two places at the same time. The SSSS must take one step at a time, and then again in an endless stream. But why should this requirement lead to the creation of a subjective perspective with a theater of mind? It follows from the way logic works. Logic works with propositions, not with raw data from the real world. The real world itself does not run by reasoning through logical propositions; it works simply because a lot of particles move about on their own trajectories. Although we believe they obey strict physical laws, their movements can’t be perfectly foretold. First, it would violate the Heisenberg Uncertainty Principle to know the exact position and bearing of each particle, as that would eclipse their wave-like nature. And secondly, the universe is happening but not “watching itself happen”. This argument, called Laplace’s demon, is the idea that someone (the demon) who knew the precise location and momentum of every atom in the universe could predict the future. It is now considered impossible on several grounds. But while the physical universe precludes exact prediction, it does not preclude approximate prediction, and it is through this loophole that the concept of a reasoning mind starts to emerge.

Think back to the computational leap I am trying to prove: the mind makes top-level decisions by reasoning with meaningful representations. I can’t prove that reasoning is the only way to control bodies at the top level, but I have argued above that it is the way we do it. But how exactly can reasoning help in a world of particles? It starts, before reasoning enters the picture, with generalization. The symbols we represent don’t exist as such in the physical world. We represent physical objects with idealized representations (called concepts) that include the essential characteristics of those objects. Generalization is the ability to recognize patterns and to group things, properties, and ideas into categories reflecting that similarity. It is probably the most important and earliest of all mental skills. But it carries a staggering implication: it shapes the way minds interpret the world. We have a simplified, stripped down view of the world, which could fairly be called a cartoon, that subdivides it into logical components (concepts, which include objects and actions) around which simple deductions can be made to direct a single body. While my thrust is to describe how these generalized representations support reason, they also support associative approaches like intuition and learned behavior. The underlying mechanism is feedback: generalized information about past patterns can help predict what patterns will happen again.

Reasoning takes the symbols formed from generalized representations and develops propositions about them to create logical scenarios called models or possible worlds. Everything I have written above drives to this point. A model is an idealization with two kinds of existence, ideal and physical, which are independent. For example, 1+1=2 according to some models of arithmetic, and this is objectively and unalterably true, independent of the physical world or even our ability to think it. Ideal existence is a function of relationships. On the other hand, a model can physically exist using a computer (e.g. biological or silicon) to implements it, or on paper or other recorded form which could later be interpreted by a computer. Physical existence is a function of spacetime, which in this case takes the form of a set of instructions in a computer. To use models, we need to expect that the physical implementation is done well so that we can focus on what the model says ideally. In other words, we need a good measure of trust in the correlation from the ideal representation to the physical referent. While we are not stupid and we know that perception is not reality, we are designed to trust the theater we interact with implicitly, both because it spares us from excess worry and because that correlation is very dependable in practice.

The ideal and physical worlds are independent of each other and might always have remained so were it not for the emergence of the animal mind some 550 million years ago. The upgrades we received in the past 4 million years with the rise of the Australopithecus and Homo genera are the most ambitious improvements in a long time, but animal minds were already quite capable. We’re just version 10.03 or so in a long line of impressive earlier releases. Animal minds probably all model the world using representation, which, as noted, captures the essential characteristics of referents, as well as rules about how objects and their properties behave relative to each other in the model. Computationally, minds use data structures that represent the world in a generalized or approximate way by recording just the key properties. All representations are formed by generalizing, but while some remain general (as with common nouns), some are tracked as specific instances (and optionally named, as with proper nouns). For that matter, generalizations can be narrow or broad for detailed or summary treatments of situations. For any given circumstance the mind draws together the concepts (being the objects and their characteristics) that seem relevant to the level of detail at hand so it can construct propositions and draw logical conclusions in a single stream. We weigh propositions using probabilistic logic and consider multiple models for every situation, which improves our flexibility. This analysis creates the artificial world of our conscious experience, the cartoon. This simplified logical view seamlessly overlays with our sensory perceptions, which pull the quality of the experience up from a cartoon to photorealistic quality.

If the SSSS is the reason we run a simplified cartoon of the world in our conscious minds, that may explain why we have a subjective experience of consciousness, but it still doesn’t explain why it feels exactly the way it does. The exact feel is a consequence of how data flows into our minds. To be effective, the conscious mind must not overlook any important source of information when making a decision. For example, any of our senses might provide key information at any time. For this reason, this information is fed to the conscious mind through sensory data channels called qualia, and each quale (kwol-ee, the singular) is a sensory quality, like redness, loudness or softness. Some even blend to create, for example, the sensation of a range of colors. The channels provide a streaming experience much like a movie. While the SSSS focuses on just the aspects most relevant to making decisions, it has an awareness of all the channels simultaneously. So it is capable of processing inputs in parallel even though it must narrow its outputs to a single stream of selected steps.

But why do data channels “feel” like something? First, we have to keep in mind that as substantial as our qualia feel, it is all in our heads, meaning that it is ultimately just information and not a physical quality. There is no magic in the brain, just information processing. A lot of information processing goes into creating the conscious theater of mind; it is no coincidence that it seems beautiful to us. Millions of years went into tailoring our conscious experience to allow all the qualia to be distinct from each other and to inform the reasoning process in the most effective way. Any alteration to that feel would affect our ability to make decisions. How should hot and cold feel? It doesn’t really matter what they feel like so long as you can tell them apart. Surprisingly, out of context, people can confuse hot with cold, because they use the same quale channel and we use them in a highly contextual way. Specifically, If you are cold, warmth should feel good, and if you are hot, coolness should feel good. And lo and behold, they do feel that way. Much of the rich character we associate with qualia comes not from the raw sensory feel itself but from the contextual associations we develop from genetic predispositions and years of experience. So red and loud will seem a bit scarier and alarming than blue or quiet, and soft will seem more soothing than rough. Ultimately, that qualia feel so rich and fit together seamlessly into a smooth movie-like experience proves that extensive parallel subconscious computational support goes into creating them.

Beyond sensory qualia, data channels carry other streams of information from subconscious processing into our conscious awareness. These streams enhance the conscious experience with emotion, recognition, and language. The subconscious mind evaluates situations, and if it finds cause for sadness (or other emotional content), then it informs the conscious mind, which then feels that way. We feel emotional qualia as distinctly as sensory qualia, and the base emotions seem to have distinct channels as we can feel multiple emotions at once. Recognition is a subconscious process that scans our memory matching everything we see and experience to our store of objects and experiences (concepts). It provides us with a live streaming data lookup service that tells us what we are looking at along with many related details, all automatically. We think of language as a conscious process, but only a small part is conscious. A processing engine hidden to our conscious minds learns the rules of our mother tongue (and others if we teach it), and it can generate words and sentences that line up with the propositions flowing through the SSSS, or parse language we hear or read into such propositions. Language processing is a kind of specialized recognition channel that connects symbols to meanings. The goal is for the conscious mind to have a simple but thorough awareness of the world, so everything not directly relevant to conscious decision making is processed subconsciously so as not to be a distraction. Desires don’t have their own qualia but instead add color to sensory and emotional qualia. Computationally this means additional information about prioritization comes through the qualia data channels. Desires come through recognition data channels (memory) as beliefs. Beliefs are desires we have committed to memory in the sense that we have computed our level of desire and now remember it. As noted above, recall that desires and beliefs are the only factors that influence how we prioritize our actions.

While we are born with no memories, and consequently all recognition and language are learned, we are so strongly inclined to learn to use our subconscious skills that all humans raised in normal environments will learn how without any overt training. We thus learn to recognize objects and experience appropriate emotions in context whether we like it or not. Similarly, we can’t suppress our ability to understand language. Interestingly, lack of conscious control over our emotions has been theorized to help others “see” our true feelings, which greatly facilitates their ability to trust us and work for both parties’ best interests. Other subconscious skills also include facility with physics, psychology, face recognition and more, which flow into our consciousness intuitively. We are innately predisposed to learn these skills and once trained we use them miraculously without conscious thought. The net result of all these subconsciously produced data channels is that the conscious mind is fed an artificial but very informative and largely predigested movie of the world, so much so that our conscious minds can, if they like, just drift on autopilot enjoying the show with little or no effort.

Lots of information flows into the conscious mind on all these data channels. It is still too much for the SSSS to process using modeling and logical propositions. So while we have a conscious awareness of all of it, attention is a specialized ability to focus on just the items relevant to the decision-making process. Computationally, what attention does is fill the local variable slots of the SSSS process with the most relevant items from the many data channels flowing into the conscious mind. So just as you can only read words at the focal point of your vision, you can only do logic on items under conscious attention, though you retain awareness of all the data channels analogously to peripheral vision. Further, since those items must be representational, data from sensory or emotional qualia must first be processed into representations through recognition channels. We can shift focus to senses and emotions, e.g. to consciously control breathing, blinking, or laughing, through representations as well. It is similar for learned behaviors. We can not only walk and chew gum at the same time, we can also carry on a conversation that engages most of our attention. Same for when we are tying our shoes or driving. But to stay on the lookout for novel situations, we retain conscious awareness of them and can bring them to attention if needed. Conscious focus is how we flexibly handle the most relevant factors moment to moment. Deciding what to focus on is a complex mental task itself that is handled almost entirely subconsciously.

The loss of smell in humans probably follows from the value in maintaining a simple logical model. Humans, and to a lesser degree other primates, have lost much of their ability to smell, which has probably been offset by improvements in vision, specifically in color and depth. That primates benefit more from better vision makes sense, but why did we lose so much variety and depth from smell perception? Disuse alone seems unlikely to explain so much loss considering rarely-used senses are still occasionally useful. The more likely explanation is that the sense of smell was a troublesome distraction from vision. That is, when forced to rely on vision primates did better than they would with both vision and smell. This can be explained by analogy to blind people, who develop other senses more keenly to compensate. Those forced to develop more keen visual senses could use them more effectively in many ways than those who trusted smell, which may turn out not to deliver as much benefit for primates, and especially humans. If you consider how much value we get from vision compared to smell, this seems like a good trade-off.

To summarize what I have said so far, the conscious mind has a broad subrational awareness of much sensory, emotional and recognition data. It can use intuition, learned behavior, and many subconscious skills but does so with conscious awareness and supervision. To consciously reason, the SSSS processes representations created by generalizing that data. The SSSS only reasons with propositions built on representations under conscious attention, i.e. those that are relevant. Innate desires are used to prioritize decisions, that is, they lead us to do things we want to do.

We know we are smarter than animals, but what exactly do we do that they can’t? Use of tools, language (and symbols in general), and self-awareness seem more like consequences of greater intelligence than its cause. The key underlying mental capacity humans have that other animals lack is directed abstract thinking. Only humans have the facility and penchant for connecting thoughts together in arbitrarily complex and generalized ways. In a sense, all other animals are trapped in the here and now; their reasoning capacities are limited to the problems at hand. They can reason, focus, imitate, wonder, remember, and dream but they can’t daydream, which is to say they can’t chain thoughts together at will just to see what might happen. If you think about it, it is a risky evolutionary strategy as daydreamers might just starve. But our instinctual drives have kept up with intelligence to motivate us to meet evolutionary requirements. Steven Pinker believes metaphors are a consequence of the evolutionary step that gave humans directed abstract thinking:

When given an opportunity to reach for a piece of food behind a window using objects set in front of them, the monkeys go for the sturdy hooks and canes, avoiding similar ones that are cut in two or made of string of paste, and not wasting their time if an obstruction or narrow opening would get in the way. Now imagine an evolutionary step that allowed the neural programs that carry out such reasoning to cut themselves loose from actual hunks of matter and work on symbols that can stand for just about anything. The cognitive machinery that computes relations among things, places, and causes could then be co-opted for abstract ideas. The ancestry of abstract thinking would be visible in concrete metaphors, a kind of cognitive vestige.

…Human intelligence would be a product of metaphor and combinatorics. Metaphor allows the mind to use a few basic ideas — substance, location, force, goal — to understand more abstract domains. Combinatorics allows a finite set of simple ideas to give rise to an infinite set of complex ones.1

Pinker believes the “stuff of thought” is sub-linguistic, and is only translated to/from a natural language for communication with oneself or others. That is, he does not hold that we “think” in language. But we can’t discuss thinking without distinguishing conscious and subconscious thought. Consciously, we only have access to the customized data channels our subconscious provides us to give us an efficient, logical interface to the world. In humans, a language data channel gives us conscious access to a subconscious ability to form or decompose linguistic representations of ideas. I agree with Pinker that the SSSS does not require language to reason, but language is a critical data channel integrally involved with advanced reasoning, i.e. directed abstract thinking. The SSSS processes many lines of thought across many models with many possible interpretations, which we can think of as being done in parallel (i.e. within conscious awareness) or in rotation (i.e. under conscious focus). But because language reduces thought to a single stream it provides a very useful way to simplify the logical processing of the SSSS down to one stream that can be put to action or used to communicate with oneself or others. Also, language is a memory aid and helps us construct more complex chains of abstract thought than could easily be managed without it, in much the same way writing amps up our ability to build longer and clearer arguments than can be sustained verbally. So the linguistic work of SSSS, i.e. conscious thought, works exclusively with natural language, but most of the real work (computationally speaking) of language is done subconsciously by processes that map meaning to words and words to meaning. Pinker somewhat generically calls the subconscious level of thinking “mentalese”, but this word is very misleading because it suggests a linguistic layer underlies reasoning when it doesn’t. Language processing is done by a specialized language center that feeds both natural language and its meaning to/from our conscious minds (the SSSS). And this center uses processing algorithms that can only process languages that obey the Universal Grammar (UG) Noam Chomsky described. But the language center does no reasoning; reasoning is a function of the SSSS, for which natural language is a tool that helps broker meanings.

So let’s consider metaphor again. The SSSS reasons with propositions built on representations that are themselves ultimately generalizations about the world. Metaphor is a generalized use of generalizations. It is a powerful tool of inductive reasoning in its own right that can help explain causation by analogy independent of its use in language. But language does make extensive use of metaphorical words and idioms as a tool of reasoning because a metaphor implies that explanations about the source will apply to the target. And more broadly, metaphors, like all ideas, are relational, defined in terms of each other, and ultimately joined to physical phenomena to anchor our sense of meaning. I agree with Pinker that metaphor provides a useful way to create words and idioms for ideas new to language and that these metaphors become partly or wholly vestigial when words or idioms are understood independent of their metaphorical origin. The words manipulate and handle derive from the skillful use of hands and yet are also applied to skillful use of the mind, and many mental words have physical origins and often retain their physical meanings, but we use them mentally without thinking of the physical meaning. But metaphorical reasoning is also well supported by language just because it is a powerful explanatory device.

An important consequence of directed abstract thinking and language is that humans have a vastly larger inventory of representations or concepts with which they can reason than other animals. We have distinct words for a small fraction of these, and most words are overloaded generalizations that we apply to a range of concepts we can actually distinguish more finely. We distinguish many kinds of parts and objects in our natural and artificial environments and many kinds of abstract concepts like health, money, self, and pomposity.

But what about language, tool use, and self-reflection? No one could successfully argue that chimps could do this as well as us if only they had generalized abstract thought. While generalized abstract thought is the underlying breakthrough that opened the floodgates of intelligence, it has co-evolved with language, manipulating hands and the wherewithal to use them, and a strong sense of self. Many genetic changes now separate our intellectual reach from our nearest relatives. Any animal can generalize from a strategy that has worked before to apply it again in similar circumstances, but only humans can reason at will about anything to proactively solve problems. Language magically connects words and grammar to meanings for us through subconscious support, but we are most familiar with how we consciously use it to represent and manipulate ideas symbolically. We can’t communicate most abstract ideas without using language, but even to develop ideas in our own mind though a chain of reasoning language is invaluable. Though our genetic separation from chimps is relatively small and recent, the human mind takes a second order qualitative leap into the ideal world that gives us unlimited access to ideas in principle because all ideas are relationships.

An overview of evolution and the mind

[Brief summary of this post]

The human mind arose from three somewhat miraculous breakthroughs:

1) Natural selection, a process dating back about 2 billion years that changes through adaptations in response to new environmental challenges

2) Animal minds, which opened up a new branch of reality: imagination. Feedback led to computation and representation, which enabled animals to flourish.

3) Directed abstract thinking, the special skill that lets people abstract away from the here and now to the anywhere and anywhen with great facility, giving us unlimited access to the world of imagination.

Of the four billion years we have spent evolving, about 600 million years (about 15%) has been as animals with minds, and at most 4 million years (about 0.1%) as human-like primates. That brief 4 million year burst may have changed 1% to 5% of our genes, which numerically is just fine tuning already well-established bodies and minds. Animals diverged into over a million animal species, but the appearance of directed abstract thinking in humans changed the playing field. Humans could survive not just in one narrow ecological niche, but in many niches, potentially flourishing anywhere on earth and ultimately squeezing out nearly all animal competition our size or bigger. Other mental capacities coevolved with and help support directed abstract thinking, like 3-D color vision, face recognition, generalized use of hands, language, and sophisticated cognitive skills like reasoning with logic, causation, time, and space. Directed abstract thinking is a risky evolutionary strategy because it can be used for nonadaptive purposes, such as the contemplating of navels, or even spiraling into analysis paralysis. To keep us on the straight and narrow, we have been equipped with enhanced senses and emotions that command our attention more than those found in other animals, for things like love, hate, friendship, food, sex, etc. The more pronounced development of sexual organs and behaviors in humans relative to other primates is well known 12, but the reasons are still speculative. I am suggesting one reason is to motivate us to pursue evolutionary goals (notably survival and reproduction) despite the distractions of “daydreaming”. Books, movies, TV, the internet, and soon virtual reality threaten our survival by fooling our survival instincts with simulacra of interactions with reality.

The mind is integrally connected to the mechanisms of life, so we have to look back to how life evolved to see why minds arose. While we don’t know the details of how life emerged, the latest theories fill some missing links better than before. Deep sea hydrothermal vents 3 may have provided the necessary precursors and stable conditions for early life to develop around 4 billion years ago, including at least these four:

(a) carbon fixation direct from hydrogen reacting with carbon dioxide,
(b) an electrochemical gradient to power biochemical reactions that led to ATP (adenine triphosphate) as the store of energy for biochemical reactions,
(c) formation of the “RNA world” within iron-sulfur bubbles, where RNA replicates itself and catalyzes reactions,
(d) the chance enclosure of these bubbles within lipid bubbles, and the preferential selection of proteins that would increase the integrity of their parent bubble, which eventually led to the first cells

From this point, life became less dependent on the vents and gradually moved away. These steps came next:

(e) expansion of biochemical processes, including use of DNA, the ability of cells to divide and the formation of cell organelles by capture of smaller cells by larger,
(f) a proliferation of cells that led eventually to LUCA, the “Last Universal Common Ancestor” cell about 3.5 billion years ago,
(g) multicellular life, which independently arose dozens of times, but most notably in fungi, algae, plants and animals about 1 billion years ago, and
(h) the appearance of sexual reproduction, which has also arisen independently many times, as a means of leveraging genetic diversity in heterogeneous environments.4 and resisting parasites 5. Whatever the reason, we have it.

The net result was the sex-based process of natural selection that Darwin identified. Lifeforms now had a biochemical capacity to encode feedback from the environment into genes that could express proteins that would result in improving the chances of survival.

Larger multicellular life diverged along two strategies: sessile and mobile. Plants chose the sessile route, which is best for direct conversion of solar energy into living matter. Animals chose mobility, which has the advantage of invincibility if one is at the top of the food chain, but the disadvantage of requiring complex control algorithms to do it. Animals further down the food chain are more vulnerable but require less sophisticated control. But how exactly did animals evolve the kind of control they need for a mobile existence? Sponges 6are the most primitive animals, having no neurons or indeed any organs or specialized cells. But they have animal-like immune systems and some capacity for movement in distress. Cnidarians (like jellyfish, anemones, and corals) feature diffuse nervous systems with nerve cells distributed throughout the body without a central brain, but often featuring a nerve net that coordinates movements of a radially symmetric body. What would help animals more, if it were possible, is an ability to move to food sources in a coordinated and efficient way. The radial body design seems limiting in this regard and may be why all higher animals are bilateral (though some, like sea stars and sea urchins, have bilateral larvae but radial adults). Among the bilateria, which arose about 550-600 million years ago, nearly all developed single centralized brains, presumably because it helps them coordinate their actions more efficiently, excepting a few invertebrates like the octopus, which has a brain for each arm, and a centralized brain to loosely administer. Independent eight-way control of arms comes in handy for an octopus; with practice, we can use our limbs independently in limited ways, but our attention can only focus on one at a time.

But how do nerves work, exactly? While we understand some aspects of neural function in detail, exactly how they accomplish what they do is still mostly unknown. Our knowledge of the mechanisms breaks down beyond a certain point, and we have to guess. But we can see the effects that nerves have: nerves control the body, and the brain is a centralized network of nerves that control the nerves that control the body. The existence of nerves and brains and indeed higher animals stands as proof that it is physically possible for a creature to move to food sources in a coordinated and efficient way, and indeed to enhance its chances of survival using centralized control. We can thus safely conclude, without any idea how they work, that the overall function of the brain is to provide centralized, coordinated control of the body.

For the most part, I will deal with the brain as a black box that controls the body and try to unravel its logical functions without too much regard as to its physical mechanisms. I will, however, try to be careful to take into account the constraints the brain’s structure entails. For example, we know brains must be fast and work continuously to be effective. To do this, they must employ a great deal of parallel processing to make decisions quickly. But let’s focus first on what they must do to control the body rather than how they do it.

To control a body so as to cause it to locate food sources, avoid dangers, and find mates requires that we start using verbs like “locate,” “avoid,” and “find”. We know minds can do these kinds of things while rocks and streams can’t, but how can we talk about them objectively, independent of the idea of minds? By observing their behavior. An animal’s body can move toward food as if it had a crystal ball predicting what it would find. It seems to have some way of knowing in advance where the food will be and animating its body so as to transport itself there. If rocks and streams can’t do it, how can animals?

The brain operates with a feedback loop of sensing, computing and acting. From an information standpoint, these steps correspond to data inputs, data processing, and data outputs. This is the crux of the computational theory of mind. When we speak of computation in this context, we are not referring to digital computation with 1’s and 0’s, but to any physical process that accomplishes information management. Information can be representational or nonrepresentational. Nonrepresentational information is just data that has value to the process that uses it. Raw sound or image data is nonrepresentational, as is much of the information supporting habitual behavior. Probably most of the information managed by the brain is nonrepresentational, but much of the information consciousness uses is representational. Representational information is grouped into concepts (e.g. objects) that describe essential and important characteristics of referents. Logical operations performed on the references are later applied back to the referents. For example, we recognize objects in raw image data by matching characteristics to our remembered representations of the objects.

At every moment the brain causes each part of the body to perform (or not perform) an action to produce the coordinated movement of the body toward a goal, such as locating a food source. Because there is only one body, and it can only be in one place at a time, the central brain must function as what I call a single-stream step selector, or SSSS, where a step is part of a chain of actions the animal takes to accomplish a goal. If the brain discerns new goals, it must prioritize them, though the body can sometimes pursue multiple goals in parallel. For example, we can walk, talk, eat, blink, and breathe at the same time. As I related in An overview of evolution and the mind, we prioritize goals in response to desires and subjective beliefs, which objectively and computationally are preference parameters that are well tuned to lead us to behaviors that coincide with the objectives of evolution (in the ancestral environment; they are not always so well tuned in modern times).

While we know the whole brain must function as an SSSS to achieve top-level coordination of the body, this doesn’t mean the SSSS has to be a special-purpose subprocess of the brain. For example, we can imagine building a robot with one overall program that tells it what to do next. But evolution didn’t do it that way. In animal minds, consciousness is a small subset of overall mental processing that creates a subjective perspective that is like an internal projection of the external physical landscape. It is a technique that works very well, regardless of whether other equally good ways of controlling the body might exist. As of now, we know that we can build robots without such a perspective, such as self-driving cars, but their responses are limited to situations they have learned to handle, which is nowhere near flexible enough to handle the life challenges all animals face. Learned behavior and reasoning are the only two top-level approaches to control that have a good degree of flexibility that I know of, but I can’t preclude the possibility of others. But we do know that animals use reasoning, which I believe mandates a simplified proposition-based logical perspective/projection of the world into a top-level portion of the mind that acts as an SSSS.

Brains use a lot of parallel processing. We know this is true for sensory processing because it provides useful sensory feedback in a fraction of a second, yet we know computationally that a non-parallel solution would be terribly slow. Real-time vision, for example, processes a large visual field almost instantly. Evolution will tend to exploit tools at its disposal if they provide a competitive advantage, so many kinds of operations in the brain use parallel processing. Associative memory, for instance, throws a pattern against every memory we have looking for matches. The computational cost of all those mismatches in just a few seconds is probably longer than our lifetimes, but that’s ok because our subconscious has nothing better to do and it doesn’t bother our conscious minds with the mismatches. Control of the body is another subconscious task using massively parallel processing. So sensing, memory, and motor control are highly parallel. But what about reasoning?

The SSSS is a subprocess of the brain that causes the body to do just one (coordinated) thing at a time, i.e. a serial set of steps. But while it produces actions serially, this does not prove that reasoning is strictly serial. Propositional logic itself is serial, but we could, in principle, think several trains of thought in parallel and then eventually act on just one of them. My guess, weighing the evidence from my own mind, is that the SSSS and our entire reasoning capacity is in fact strictly serial. Drawing on an analogy to computers, the SSSS has one CPU. It is, however, a multitasking process that uses context switching to shuffle its attention between many trains of thought. In other words, we pursue just one train of thought at a time but switch between many trains of thought about different topics floating around in our heads. Each train of thought has a set of associated memories describing what has been thought so far, what is currently under consideration, and goals. For the most part, we are aware of the trains we are running. For example, I have trains now for several aspects of what I am writing about, the temperature and lighting of my room, what the birds are doing at my bird feeder, how hungry I am, what I am planning to eat next, what is going on in the news, etc. These trains float at the edge of my awareness competing for attention, but my attention process keeps me on the highest prioritized task. But to prioritize them the attention process has to “steal cycles” from my primary task and cycle through them to see if they warrant more attention. It does that at a low level that doesn’t disturb my primary train of thought too much, but enough to keep me aware of them. When we walk, talk, and chew gum at the same time our learned behavior guides most of the walking and chewing, but we have to let these activities steal a few cycles from talking. We typically retain no memory of this low-level supervision the SSSS provides to non-primary tasks and may be so absorbed in our primary task we don’t seem to consciously realize we are lending some focus to the secondary tasks, but I believe we do interrupt our primary trains to attend to them. However, we are designed to prevent these interruptions from reducing our effectiveness at the primary task, for the obvious reason that quality attention to our primary task is vital to survival. The higher incidence of traffic accidents when people are using cell phones seems to confirm these interruptions. The person we are speaking to doesn’t expect us to be time-sharing them with another activity, which works out well so long as we can drive on autopilot (learned behavior). But when an unexpected driving situation requiring reasoning pops up we will naturally context switch to deal with it, but the other party doesn’t realize this and continues to expect our full attention. We may consequently fail to divert enough reasoning power to driving.

Why wouldn’t the mind evolve a capacity to reason with multiple tasks in parallel? I believe the benefits of serial processing with multitasking outweigh the potential benefits of parallel processing. First and foremost, serial processing allows for constant prioritization adjustments between processes. If processes could execute in parallel, this would greatly complicate deciding how to prioritize them. Having the mind dedicate all of its reasoning resources into a task that is known to be the most important at that moment is a better use of resources than going off in many directions and trying to decide later which was better to act on. Secondly, there isn’t enough demand for parallel processing at the top level to justify it. Associative memory and other subconscious processes require parallel processing to be fast enough, but since we do only need to do one thing at a time and our animal minds have been able to keep up with that demand using serial processing, parallel designs just haven’t emerged. While such a design has the potential to think much faster, achieving consensus between parallel trains is costly. This is the too-many-cooks-in-the-kitchen headache groups of people have when working together to solve problems. If the brain has a single CPU instead of many then parts of that CPU must be centrally located, and since consciousness goes back to the early days of bilateral life, some of those parts must be in the most ancient parts of the brain.

The brain controls the body using association-based and decision-based strategies. Association-based approaches use unsupervised learning through pattern recognition. It is unsupervised in the sense that variations in the data sets alone are used to identify patterns which are then correlated to desired effects. The mind then recognizes patterns and triggers appropriate actions. In this way, it can learn to favor strategies that work and avoid those that fail. While the mind heavily depends on association-based approaches for memory and learning, they do not explain consciousness or the unique intelligence of humans, which results from decision-based strategies.

Reasoning is powered by a combination of association-based and decision-based strategies, but the association-based parts are subsidiary as the role of decision-based strategies is to override learned behavior when appropriate. Decision-based strategies draw logical conclusions from premises either with certainty (deduction) or probability (induction). Reasoning itself, the application of logic given premises, is the easy part from the perspective of information management. The hard part is establishing the premises. The physical world has no premises; it only has matter and energy moving about in different configurations. Beneath the level of reasoning, the mind looks for patterns and distinguishes the observed environment into a collection of objects (or, more broadly, concepts). The distinguished objects themselves are not natural kinds because the physical world has no natural kinds, just matter and energy, but there are some compelling practical reasons for us to group them this way. Lifeforms, in particular, each have a single body, and some of them (animals) can move about. Since animals prey on lifeforms for food, and also need to recognize mates and confederates, an ability to recognize and reason about lifeforms is indispensable. Physically, lifeforms have debatable stability, as their composition constantly changes through metabolism, but that bears little on our need to categorize them. Similarly, other aspects of the environment prove useful to distinguish as objects and by generalization as kinds of objects. Animals chunk data at levels of granularity that prove useful for accomplishing objectives. Grouping information into concepts this way sets the stage for the SSSS to use them in propositions and do logical reasoning. Concepts become the actors in a chain of events and can be said to have “cause and effect” relationships with each other from the “perspective” of the SSSS. That is, cause and effect are abstractions defined by the way the data is grouped and behaves at the grouped level that the SSSS can then use as a basis for decisions. In this way, the world is “dumbed down” for the SSSS so it can make decisions (i.e. select actions) in real time with great quality and efficiency despite having just one processing stream.

We experience the SSSS as the focal point of reasoning, the center of conscious awareness, where our attention is overseeing or making decisions. Though it sounds a bit surprising that we are nothing more than processes running in our brains, unless magic or improbable laws of physics are involved, this is the only possible way to explain what we are and is also consistent with brain studies to date and computer science theory. The way our conscious mind “feels” to us, more than anything, is information. The world feels amazing to us because consciousness is designed so that important information grabs our attention through all the distractions. Our conscious experience of vision, hearing, body sense, other senses, and memory are all just ways of interpreting gobs of pure information to facilitate a continuous stream of decisions. The human conscious experience is a big step up from that of animals because directed abstract thinking enables us to potentially conceive of any relationship or system, and in particular powers our ability to imagine possible worlds, including self-awareness of ourselves as abstract players in such systems.

The process of mind

[Brief summary of this post]

Let’s say the mind is a kind of computer. As a program, it moves data around and executes instructions. Herein I am going to consider the form of the data and the structure of the program. I have proposed that from the top down the mind is controlled by a process I call the SSSS, for single stream step selector. I have argued that this process uses a single CPU, i.e. one thread or train of thought, but an unlimited number of multitasked processes, though it is only actively pursuing a handful of these at a time. And I have argued that top-level decisions use reason, either inductive of deductive logic, on propositions, which are simplifications or generalizations about the world, guided by desires, which are instinctive preferences understood consciously as preferential propositions. Propositions are represented using concepts framed by models, both of which we keep in our memory.

To decompose this further working from the top down let’s consider how a program works. First, it collects data, aka inputs. Then it does some processing on the data. Third, it produces outputs. And last, it repeats. For a service-oriented program, i.e. one that provides a continuous stream of outputs for a shifting stream of inputs, this endless iteration of the central processing loop, which for minds is heavily driven by outputs feeding back to inputs, forms the outer structure of the program. I call the loop used by the SSSS the RRR loop, for recollection, reasoning, and reaction.

Before I discuss these in some detail, I want to say something about the data and instructions. If I say I’m losing my mind, I’m talking about my memory, not my faculties, which I can take for granted. All of the “interesting” parts are in the data, from our past experiences to our understanding of the present to our future plans. The instructions our brain and body follows are, by comparison, low-level and mostly hard-wired. The detailed plans that let us play the piano or speak a sentence are stored in memory. Built-in instructions support memory retrieval, logical operations, and transmission of instructions to our fingers or mouths, but any higher-level understanding of the mind relates to the contents of memory. Our memory is inconceivably vast. At any one time, we can consciously manage just a handful of data references and an impression of the data to which they refer. But that referenced data itself in turn ultimately refers to all the data in our minds, everything we have ever known, and to some degree everything everyone has ever known. Because “everything” means representations of everything, and since representations are generalizations that lose information, much has been lost. Most, no doubt. But it is still a massive amount of useful information, distilled from our personal experience, our interactions with others, culture, and a genetic heritage of instinctive impressions that develop into memory as we grow. Note that genetically-based “memory” is not yet memory at birth but a predisposition to develop procedural memory (e.g. breastfeeding, walking) or declarative memory (e.g. concepts, language).

One more thing before I go into the phases. We consciously control the SSSS process; making decisions is the part of our existence we identify with most strongly. But the SSSS process is supported by an incalculably large (from a conscious perspective) amount of subconscious thinking. Our subconscious does so much for us we are already very smart before we consciously “lift a finger”. This predigested layer is what makes explaining the way the mind works so impenetrable: how can you explain what just appears by magic? Yes, subjectively it is magic — conscious awareness and attention is a subprocess of the mind that is constrained to see just the outer layers of thought that support the SSSS, without all the distraction of the underlying computations that support it. But objectively we can deduce much about what the subconscious layers must be doing and how they must be doing it, and we now have machine learning algorithms that approximate some of what the subconscious does for the SSSS in a very rudimentary way. So from a computational standpoint, all three phases of the SSSS are almost entirely subconscious. All the conscious layer is doing is providing direction — recall this, reason that, react like so — and the subconscious makes it happen with a vast amount of hidden machinery.

Recollections can be either externally or internally stimulated, which I call recognition-based or association-based recall. Recognition means identifying things in the environment similar to what has been seen before, a process known in psychology as apperception. Sensory perception provides a flood of raw information that can only be put to use by the SSSS to aid in control if it can be distilled into a propositional form, which is done by generalizing the information into concepts. The mind first builds simplified generic object representations that require no understanding about what is being sensed. For example, vision processing converts the visual field into a set of colored 3-D objects adjusted for lighting conditions, without trying to recognize them. These objects must have a discrete internal representation headed by an object pointer and containing the attributes assigned to the object. For example, if we identify a red sphere, then a red sphere object pointer contains the attributes red, sphere, and other salient details we noticed. Such a pointer lets us distinguish a red sphere from a blue cube, i.e. that red goes with the sphere and blue goes with the cube, which is called the segregation problem in cognitive science, or sometimes the binding problem (technically subproblem BP1 of the binding problem). Being able to create distinct mental objects at will for anything we see that we wish to think about discretely is critical to making use of the information. Note that in this simplified example I have called out two idealized attributes, red and sphere, but this processing happens subconsciously, so it would be presumptuous (and wrong) to infer that it identifies the red sphere simply by using those two attributes. More on that below.

The next step of recognition is matching perceived objects to things we have seen before. This presupposes we have memories, so let’s just assume that for now. Memory acts like a dictionary of known objects. The way we associate perceived objects to memories, technically called pattern recognition, is solved by brute force: the object is simultaneously compared to every memory we have, trying to match the attributes of that object against the attributes of every object in memory. Technically, to do this comparison concurrently means doing many comparisons in parallel, which probably means many neural copies of the perceived object are broadcast across the brain looking for a match. Nearly all these subconscious attempts to match will fail, but if a match is found then consciously it will just seem to pop out. We know pattern recognition works this way in principle because it is the only way we could recognize things so quickly. Search engines and voice recognition algorithms use machine learning algorithms that function in a similar way, which is sometimes called associative memory. While we don’t know much yet about brain function, this explanation is consistent with brain studies and what we know about nerve activation.

After a match, our associative memory returns the meaning of the object, which is analogous to a dictionary definition, but while any given dictionary definition uses a fixed set of words, a memory returns a pointer connected to other memories. So the meaning consists of other objects and relationships from the given object to them. So when we recognize our wallet, the pointer for our wallet connects it to many other objects, e.g. to a generic wallet object, to all the items in it, and to its composition. Each of these relationships has a type, like “is a”, “is a part of”, “is a feature of”, “is the composition of”, “contains”, etc. This is the tip of the iceberg because we also have long experience with our wallet, more than we can remember, much of which is stored and can potentially be recalled with the right trigger.

A single recognition event, the moment an object is compared against everything we know to find a match, is itself a simple hit or miss: our subconscious either finds relevant match(es) or it doesn’t. However, what we sense at the conscious level is a complex assembly of many such matches. There are many reasons for this, and I will list a few, but they stem from the fact that consciousness needs more than an isolated recognition event can deliver:
1. The attributes one which we base recognition are themselves often products of recognition. Our experience with substances leads us to evaluate the composition of the object based on texture, color, and pattern. Our experience with letters leads us to evaluate them based on lines, curves, and enclosed areas. Our experience with shapes leads us to evaluate them based on flatness or curviness, protuberances, and proportions. This kind of low-level recognition is based on a very large internal database of attributes comprehensible only to our internal subconscious matching process (beyond just “red” or “sphere”) that is built from a lifetime of experience and not from rational idealizations we concoct consciously. So size, luminosity, depth, tone, context and more trigger many subconscious recognition events from our whole life experience. These subconscious attributes derive from what is called unsupervised learning in machine learning circles, meaning that they result from patterns in the data and not from a qualitative assessment of what “should” be an attribute.
2. Each subset of the object’s attributes represents a potentially matchable object. So red spheres can also match anything red or any sphere. Every added attribute doubles the number of combinations and adds a new subset with all the attributes, so five attributes have 31 combinations and six have 63. A small shiny red sphere with a small white circle having a black “3” in it has six (named) attributes, and we will immediately recognize it as a pool ball, specifically the 3-ball, which is always red. Our subconscious does the 63 combinations for us and finds a match on the combination of all six attributes. Without the white circle with the “3”, the sphere could be a red snooker ball, a Christmas ornament, or a bouncy ball, so these possibilities will occur to us as we study the red sphere. As noted from my comments on machine learning above, the subconscious is not really using these six attributes per se but draws on a much broader and more subtle set of attributes generalized from experience. But it still faces a subset matching problem that requires more recognition events.
3. Reconsideration. We’re never satisfied with our first recognition; we keep doing it and refining it and verifying it, quickly building up a fairly complex network of associations and likelihoods, which our subconsciously distills down for us to the most likely recognized assembly. So a red sphere among numbered pool balls will be seen as the 3-ball even if the “3” is hidden because the larger context is taken into consideration. A red ball on a Christmas tree will be seen as an ornament. So long as objects fit into well-recognized contexts, the subconscious takes care of all the details, though this leaves us somewhat vulnerable to optical illusions.
Although the possible attribute combinations from approach (2) grow exponentially to infinity, our experience-based memory of encountered attributes using approach (1) constrain that growth. So familiar objects like phones and cars, composed of many identifiable sub-objects and attributes seen in countless related variations over the years, are instantly identified and confirmed using approach (3) even if they look slightly different from any seen before.

Our subconscious recognition algorithms are largely innate, e.g. they know how to identify 3-D objects and assemble memories. But some are learned. Linguistic abilities, which enable us to not only remember things but words that go with them and ways to compose them into sentences, are chief among these for humans. Generalization, mechanics (knowledge of motion), math (knowledge of quantity), psychology (knowledge of behavior), and focusing attention on what is important are other examples where innate talents make things easy for us. We can also train our subconscious procedural memory by learning new behaviors. In this case, we consciously work out what to do, practice it, and acquire the ability to perform them subconsciously with little conscious oversight. I allot both innate and learned algorithms to the recollection phase.

Beyond recognition, we recollect using what I call association-based recall. This happens when thoughts about one thing trigger recollection of related things. This is pretty obvious — our memory is stirred either by seeing something and recognizing it or because thinking about one thing leads to another. I already discussed how our subconscious does this to draw memories together through reconsideration, but here I am referring to when we consciously use it to elaborate on a train of thought. We can also conjure up seemingly random memories about topics unrelated to anything we have been thinking about. While subconscious and conscious free association are vital to maintaining our overall broad perspective, it is the conscious recognitions and associations that drive the reasoning process to make decisions. And in humans, our added ability to consciously direct abstract thinking lets us pursue any train of thought as far as we like.

The second phase, reasoning, is the conscious use of deductive and inductive logic. This means applying logical operations like and, or, not, and if…then on the propositions under attention. Deduction produces conclusions that necessarily follow from premises while induction produces conclusions that likely follow from premises based on prior experience. Intuition (which I consider part of the recollection phase) is very much like a subconscious capacity for induction, as it reviews our prior experience to find good inferences. But that review uses subconscious logic hidden to us which we can generally trust because it has been reliable before, but not trust too much because it is localized data analysis that doesn’t take everything into account the way reasoning can. Recollection and reasoning form an inner RR loop that cycles many times before generating a reaction, though if we need a very quick response we may jump straight from intuition to reaction. Although there is only one RRR loop, the mind multitasks, swapping between many trains of thought at once. This comes in handy when planning what to do next as the mind pursues many possible futures simultaneously to find the most beneficial one. Those that seem most likely draw most of our attention while the least likely hover at the periphery of our awareness.

Just as recollection is mostly subconscious but consciously steered, so too does reasoning leverage a lot of subconscious support, much of which itself leverages memory to hold the propositions and models behind all the work it is multitasking. For example, most of our more common deductions don’t need to be explicitly spelled out because habitual use of plans used many times before lets us blend learned behavior with step by step reasoning to spell out only the details that differ from past experience. So intuition basically tells us, “I think you’ve done this kind of thing before, I’ve got this,” and we give it a bit more rope. But the top level, where reasoning occurs, is entirely conscious and the central reason consciousness exists. A subprocess of the brain that pulls all the pieces together and considers the logical implications of all the parts is extremely helpful for handling novel situations. It turns out that nearly every situation has at least some novel aspects, so we are constantly reasoning.

The third phase of the RRR loop is reaction. Reaction has two components, deciding on the reaction and implementing it. The decision itself is the culminating purpose of the mind and especially the conscious mind, which only exists to make such top-level decisions. The mind considers many possible futures before settling on an action that it believes will hopefully precipitate one of them. The decision is simply the selection of the possible future (or, more specifically, one step toward that future) that the SSSS algorithm has ranked as the optimal one to aim for. That ranking process considers all the beliefs and desires the SSSS is monitoring, both from rational inputs and irrational feelings and intuitions. Selecting the right moment to act is one of the factors managed by that consideration process, so it follows logically from the reasoning process. While there is some pressure to reconsider indefinitely to refine the reaction, there is also pressure to seize the opportunity before it slips away or hampers one’s ability to move on to other decisions. Most decisions are routine, so we are fairly comfortable using tried and true methods, but we spend more time with novel circumstances.

While the SSSS decides on, or at least finalizes, the reaction, it delegates the implementation or physical reaction to the subconscious to carry out as this part doesn’t require further decision support. Even the simplest actions require a lot of parallel processing to control the muscles to perform the action, and the conscious mind is just not up to that or even wired for it. So all of our reactions, in the last few milliseconds at least, leverage innate or habituated behavior. As we execute a related chain of reactions, we will continue to provide conscious oversight to some degree, but will largely expect learned behavior to manage the details. This is why studies show that the brain often commits to decisions before we consciously become aware of them, an argument that has been used to suggest we don’t have free will since the body acts “on its own”. All this demonstrates is that we delegated our subconscious minds to execute plans we previously blessed. Of course, if we don’t like the way things are turning out we just consciously override them. In this way, walking, for instance, becomes second nature and doesn’t require continual conscious focus. But while not in focus, all actions within conscious awareness remain under the control of the RRR loop of the SSSS process, as is necessary for overall coordinated action. Some actions not normally within the range of conscious control, like pulse rate and blood pressure, can be consciously managed to a degree using biofeedback. It is reasonable for us to lack conscious control over housekeeping tasks that don’t benefit from reason. This is why the enteric nervous system, or “gut brain”, can function pretty well even if the vagus nerve connecting it to the central nervous system is severed1.

Recollection, essential for all three phases of the RRR process, assumes we have the right kind of knowledge stored in our memory, but I did not say how it got there. Considering that our memory is empty when we begin life, we must be able to add to our store of memory very frequently early in life to develop an understanding of what we are doing. Once mature, the ability to add to our memory lets us keep a record of everything we do and to expand our knowledge to adapt to changes, which have become frequent in our fast-paced world. From a logical perspective, then, we can conclude that the brain would be well served by committing to memory every experience that passes through the RRR loop. However, one can readily calculate that the amount of information passing through our senses would fill any storage mechanism the brain might use in a few hours or days at most. So we can amend the strategy to this: attempt to remember everything, but prioritize remembering the most important things.

This is a pretty broad mandate. Without some knowledge of the brain’s memory storage mechanisms, it will be hard to deduce more details about the process of mind with much confidence. It is certainly not impossible, and I am prepared to go deeper, but now is a good time to introduce what we do know about how the memory works because brain research has produced some important breakthroughs in this area. While the history of this subject is fascinating and mostly concerns a few patients with short and long-term memory loss, I will jump to the most broadly-supported conclusions, which are mostly well-known enough now to be considered common knowledge. In particular, we have short-term and long-term memory, which differ principally in that short-term memory lasts from moments to minutes, while long-term memory lasts indefinitely. We don’t consciously differentiate the two because the smooth operation of the mind benefits from maintaining the illusion of remembering everything. We know gaps can develop in our memory quickly, but we come to accept them because they have a limited impact on our decisions going forward, which is the role of the conscious mind.

We understand long-term memory better. If you picture the brain you see the wrinkled neocortex, most of which is folded up beneath the surface. But long-term memories are not formed in the neocortex. After all, every vertebrate can form long-term memories, but only mammals have a neocortex. Long-term memory comes in two forms stored very differently in the brain. Procedural memory (learned motor skills) are stored outside the cortex in the cerebellum and other structures, and is inaccessible to conscious thought, though we can, of course, employ it. Declarative memory (events, facts, and concepts) is created in the hippocampus, part of the archicortex (called the limbic system in mammals), which is the earliest evolved portion of the cortex. This kind of long-term memory is rehearsed by looping it via the Papez circuit from the hippocampus through to the medial temporal lobe and back again. After some iterations, the memory is consolidated into a form that joins the parts together (solving the binding problem mentioned above) and is stored in the medial temporal lobe using permanent and stable changes in neural connections. Over the course of years the memory is gradually distributed to other locations in the neocortex so that recent memories are mostly in the medial temporal lobe and memories within twelve years have been maximally distributed elsewhere2. For the most part, I will be focusing on declarative memory (aka explicit memory, as opposed to implicit procedural memory) as it is the cornerstone of reasoning, but we can’t forget that the rest of the brain and nervous system contribute useful impressions. For example, the enteric nervous system or “gut brain” (noted above) generates gut feelings. The knowledge conveyed from the gut is now believed to arise from its microbiome. This show of “no digestion without representation” is our gut bacteria chipping in their two cents toward our best interests.

What about short-term memory? It is sometimes called working memory because long-term memory needs to be put into short-term memory to be consciously available for reasoning. In humans, we know it is mostly managed in the prefrontal lobe of the neocortex. Short-term memory persists for about 10 to 20 seconds but can be extended indefinitely by rehearsal, that is, repeating the memory to reinforce it. In this way, it seems short-term memories can be kept for minutes without actually forming long-term memories. The amount of active short-term memory is thought to be about 4 to 5 items, but can be enlarged by chunking, which is grouping larger sets into subsets of three to four. Short-term memory being kept available by rehearsal can extend this, even though only 4 to 5 items are consciously available at once.

While reasoning probably only considers propositions encoded in prefrontal short-term memory, the other data channels flowing into conscious awareness provide other forms of short-term memory. Sensory memory registers provide brief persistence of sensory data. Visible persistence (iconic memory) lasts a fraction of a second, one second at most, aural persistence (echoic memory) up to about four seconds, and touch persistence (haptic memory) for about two seconds. Senses are processed into information such as objects, sounds, or textures, and a short-term memory of this sensory information independent of prefrontal memory seems to exist but has not been extensively studied. Sensory and emotional data channels that provide a fairly constant message (like body sense or hunger) can also be thought of as a form of short-term memory because the information they carry is always available to be moved into prefrontal short-term memory.

Short-term and long-term memory were first proposed in 1968 by Atkinson’s and Shiffrin’s (1968) multi-store model. Baddeley and Hitch introduced a more complex model they called working memory to explain how auditory and visual tasks could be done simultaneously with nearly the same efficiency as if done separately. From a top-down perspective, the brain has great potential to process tasks in parallel but ultimately must reconcile any parallel processing into a single stream of actions. Processing sensory signals, however, are not reactions to those signals, so it makes sense we can process them in parallel and that some short-term memory capacity in each would facilitate that. If the mechanisms the brain uses to maintain short-term memories of sensory signals and pre-frontal working memory involve close loops that rehearse or cycle the memories to give them enough longevity that the mind has time to manipulate them in various ways, then it makes sense that the brain would have just a handful of such closed loops which work closely with pre-frontal working memory to manage all short-term memory needs. Alan Baddeley proposed a central executive process that coordinates the different kinds of working memory, to which he added episodic buffer in 2000. He based the central executive on the idea of the Supervisory Attentional System (SAS) of Norman and Shallice (1980).

Interestingly, we appear to be unable to form new long-term memories during REM sleep, nor do our dreaming thoughts pursue prioritized objectives. However, if we are awakened or disturbed from REM sleep we can recover our long-term storage capacity quickly enough to commit some of our dreams to memory. This suggests some mechanisms of the SSSS are disabled during dreaming while others still operate3.

Having established the basic outer process of the conscious mind as an RRR loop within an SSSS process supported by algorithms and memory that largely operate subconsciously, the next question is how this framework is used to generate the content of the conscious mind, concepts and models.

Concepts and Models

[Brief summary of this post]

In The process of mind I discussed the reasoning process as
the second phase of the RRR loop (recollection, reasoning, and reaction). That discussion addressed procedural elements of reasoning, while this discussion will address the nature of the informational content. Information undergoes a critical shift in order to be used by the reasoning process, a shift from an informal set of associations to explicit relationships in formal systems, in which thoughts are slotted into buckets which can be processed logically into outcomes which are certain instead of just likely. Certainty is dramatically more powerful than guesswork. The buckets are propositions about concepts and the formal systems are an aspect of mental models (which I will hereafter call models).

I have previously described this formal cataloging as a cartoon, which you can review here. So is that it then, consciousness is a cartoon and life is a joke? No, the logic of reasoning is a cartoon but the concepts and models that comprise them bridge the gap — they have an informal side that carries the real meaning and a formal side that is abstracted away from the underlying meaning. So there is consequently a great schism in the mind between the formal or rational side and the informal or subrational side. Both participate in conscious awareness, but the reason for consciousness is to support the rational side. Reasoning requires that the world be broken down, as it were, into black and white choices, but to be relevant and helpful it needs to remain tightly integrated to both external and internal worlds, so the connections between the cartoon world and the real world must be as strong as possible.

So let’s define some terms in a bit more detail and then work out the implications. I call anything that floats through our conscious mind a thought. That includes anything from a sensory perception to a memory to a feeling to a concept. A concept is a thought about something, i.e. an indirect reference to it, and this indirect reference is the formal aspect that supports reasoning, a thought process that uses concepts to form propositions to do logical analysis. (A concept may also be about nothing; see below.) What concepts refer to doesn’t actually matter to logical analysis; logic is indifferent to content. Of course, content ultimately matters to the value of an analysis, so reasoning goes beyond logic to incorporate meaning, context, and relevance. So I distinguish reasoning from rational thought in that it leverages both rational and subrational thinking. And concepts as well leverage both: though they may be developed or enhanced by rational thinking, they are first and foremost subrational. They are a way of grouping thoughts, e.g. sensory impressions or thoughts about other thoughts, into categories for easy reference.

We pragmatically subdivide our whole world into concepts. The divisions are arbitrary in the sense that the physical world has no such lines — it is just a collection of waves and/or particles in ten or so dimensions. But it is not arbitrary in the sense that patterns emerge that carry practical implications: wavelets clump into subatomic particles, which clump into atoms, which clump into molecules, which clump into earth, water, and air or self-organize into living things. These larger clumps behave as if causes produce effects at a given macro level, which can explain how lakes collect water or squirrels collect nuts. The power that organizes things into concepts is generalization, which starts from recognizing commonalities between two or more experiences. Fixed reactions to sensory information, e.g. to keep eating while hungry, are not a sufficiently nuanced response to ensure survival. No one reaction to any sight, sound or smell is helpful in all cases, and in any case, one never sees exactly the same thing twice. Generalization is the differentiator that provides the raw materials that go into creating concepts. Our visual system contains custom algorithms to differentiate objects based on hardwired expectations about the kinds of boundaries between objects that we encountered in our ancestral environment that we benefited most from being able to discriminate. Humans are adapted to specialize in binocular, high-resolution, 3-D color vision of slowly moving objects under good lighting, even to the point of being particularly good at recognizing specific threats, like snakes1. Most other animals do better than us with fast objects, poor lighting, and peripheral vision. My point here is just that there are many options for collecting visual information and for generalizing from it, and we are designed to do much of that automatically. But being able to recognize a range of objects doesn’t tell us how best to interact with them. Animals also need concepts about those objects that relate their value to make useful decisions.

Internally, a concept has two parts, its datum and a reference to the datum, which we can call a handle after the computer science term for an abstract, relocatable way of referring to a data item. A handle does two things for us. First, it says I am here, I am a concept, you can move me about as a unit. Second, it points to its datum, which is a single piece of information insofar as it has one handle, but connecting to much more information, the generalizations, which together comprises the meaning of the concept. A datum uniquely collects the meaning of a given concept at a given time in a given mind, but other thoughts or concepts may also use that connected information for other purposes. This highly generalized representation is very flexible because a concept can hold any idea — a sensation, a word, a sentence, a book, a library — without restricting alternative formulations of similar concepts. And a handle with no datum at all is still useful in a discussion about generic concepts, such as the unspecified concept in this clause, which doesn’t point to anything!

To decompose concepts we need to consider what form the datum takes. This is where things start to get interesting, and is also the point where conventional theories of concepts start to run off the rails. We have to remember that concepts are fundamentally subrational. This means that any attempt to decompose them into logical pieces will fail, or at best produce a rationalization2, which is an after-the-fact reverse-engineered explanation that may contain some elements of the truth but is likely to oversimplify something not easily reducible to logic. For a rational explanation of subrational processes, we should instead think about the value of information more abstractly, e.g. statistically. The datum for the concept APPLE (discussions of concepts typically capitalize examples) might reasonably include a detailed memory of every apple we have ever encountered or thought about. If we were to analyze all that data we might find that most of the apples were red, but some were yellow or green or a mixture. Many of our encounters will have been with products made from apples, so we have a catalog of flavors as well. We also have concepts for prototypical apples for different circumstances, and we are aware of prototypical apples used by the media, as well as many representations of apples or idiomatic usages. All of this information and more, ultimately linking through to everything we know, is embedded in our concept for APPLE. And, of course, everyone has their own distinct APPLE concept.

Given this very broad and even all-encompassing subrational structure for APPLE, it is not hard to see why theories of concepts that seek to provide a logical structure for concepts might go awry. The classical theory of concepts3, widely held until the 1970’s, holds that necessary and sufficient conditions defining the concept exist. It further says that concepts are either primitive or complex. A primitive concept, like a sensation, cannot be decomposed into other concepts. A complex concept either contains (is superordinate to) constituent concepts or implies (is subordinate to) less specific concepts, as red implies color. But actually, concepts are not comprised of other concepts at all. Their handles are all unique, but their data is all shared. Concepts are not primitive or complex; they are handles plus data. Concepts don’t have discrete definitions; their datum comprises a large amount of direct experience which then links ultimately to everything else we know. Rationalizations of this complex reality may have some illustrative value but won’t help explain concepts.

The early refinements to the classical theory, through about the year 2000, fell into two camps, revamp or rejection. Revamps included the prototype, neoclassical and theory-theory, and rejection included the atomistic theory. I’m not going to review these theories in detail here; I am just going to point out that their approach limited their potential. Attempts to revamp still held out hope that some form of definitive logical rules ultimately supported concepts, while atomism covered the alternative by declaring that all concepts are indivisible and consequently innate. But we don’t have to do down either of those routes; we just have to recognize that there are two, or at least two, great strategies for information management: mental association and logic. Rationality and reasoning depend on logic, but there are an unlimited number of potentially powerful algorithmic approaches for applying mental associations. For example, our minds subconsciously apply such algorithms for memory (storage, recall and recognition), sensory processing (especially visual processing in humans), language processing, and theory of mind (ToM, the ability to attribute mental states — beliefs, intents, desires, pretending, knowledge, etc. — to oneself and others). Logic itself critically depends on the power of association to create concepts and so is at least partially subordinate to it. So an explanation of reasoning doesn’t result in turtles (logic) all the way down. One comes first to logic, which can be completely abstracted from mental associations. One then gets to concepts, which may be formed purely by association but usually includes parts (that are necessarily embedded in concepts) built using logic as well. And finally one reaches associations, which are completely untouchable by direct logical analysis and can only be rationally explained indirectly via concepts, which in turn simplify and rationalize them, consequently limiting their explanatory scope to specific circumstances or contexts.

I have established that concepts leverage both informal information (via mental association) and formal information (via logic), but I have not said yet what it means to formalize information. To formalize means to dissociate form from function. Informal information is thoroughly linked or correlated to the physical world. While no knowledge can be literally “direct” since direct implies physical and knowledge is mental (i.e. relational, being about something else), our sensory perceptions are the most direct knowledge we have. And our informal ability to recognize objects, say an APPLE, is also superficially pretty direct — we have clear memories of apples. Formalization means to select properties from our experiences of APPLES that idealize in a simple and generalized way how they interact with other formalized concepts. On the one hand, this sounds like throwing the baby out with the bathwater, as it means ignoring the bulk of our apple-related experiences. But on the other hand, it represents a powerful way to learn from those experiences as it gives us a way to gather usable information about them into one place. I call that place a model; it goes beyond a single generalization to create a simplified or idealized world in our imagination that follows its own brand of logic. A model must be internally consistent but does not necessarily correspond to reality. It is, of course, usually our goal to align our models to reality, but we cognitively distinguish models from reality. We recognize, intuitively if not consciously, that we need to give our models some “breathing room” to follow the rules we set for them rather than any “actual” rules of nature because we don’t have access to the actual rules. We only have our models (including models we learn from others), along with our associative knowledge (because we don’t throw our associative knowledge out with the bathwater; it is the backbone beneath our models). Formally, models are called formal systems, or, in the context of human minds, mental models. Formal systems are dissociated from their content; they are just rules about symbols. But their fixed rules make them highly predictable, which can be very helpful if those predictions could be applied back to the real world. The good news is that many steps can be taken to ensure that they do correlate well with reality, converting their form back into function.

But why do we formalize knowledge into models? Might not the highly detailed, associative knowledge remembered from countless experiences be better? No, we instead simplify reality down to bare-bones cartoon descriptions in models to create useful information. The detailed view misses the forest for the trees. Generalization eliminates irrelevant detail to identify commonality. The mind isolates repetitive patterns over space and time, which inherently simplifies and streamlines. This initially creates a capacity for identification, but the real goal is a capacity for application. Not just news, but news you can use. So from patterns of behavior, the mind starts to generalize rules. It turns out that the laws of nature, whatever they may ultimately be, have enough regularity that patterns pop up everywhere. We start to find predictable consequences from actions at any scale. We call these cause and effect if the effect follows only if the cause precedes, presumably due to some underlying laws of nature. It doesn’t matter if the underlying laws of nature are ever fully understood, or even if they are known at all, which is good because we have no way of learning what the real laws of nature are. All that matters is the predictability of the outcome. And predictability does approach certainty for many things, which is when we nominate the hypothesized cause as a law. But we need to remember that what we are really doing is describing the rules of a model, and both the underlying concepts in the model and their rules can never perfectly correspond to the physical world, even though they appear to do so for all practical purposes. Where there is one model, there can always be another with slightly different rules and concepts that explains all the same phenomena. Both models are effectively correct until a test can be found to challenge them. This is how science vets hypotheses and the paradigms (larger scale models) that hold them.

Having established that we have models and why, we can move on to how. As I noted above, while logic can be abstracted from mental associations, it is not turtles (i.e. logical) all the way down. Models are a variety of concept, and concepts are mostly subrational, the informal products of association: we divine rules and concepts about the world using pattern recognition without formal reasoning. We can and often do greatly enrich models (and all concepts) via reasoning, which ultimately makes it difficult to impossible to say where subrational leaves off and rational begins.4 As noted above, we can’t use reason to separate subrational from rational, because that is rationalizing, whose output is rational. Rational output has plenty of uses, but can’t help but stomp on subrational distinctions. But although we can’t identify where the subrational parts of the model end and the rational parts begin, it does happen, which means we can talk about an informal model that consists of both subrational and rational parts, and a formal model consisting of only rational parts. When we reason, we are using only formal models which implicitly derive their meaning from the informal model that contains them. This is a requirement of formal systems: the rules of logic operate on propositions, which are statements that are true or false affirmations or predicates about a subject, which itself must be a concept. So “apples are edible” and “I am hungry” are propositions about the concepts APPLE, EDIBLE, and HUNGRY (at least). Our informal model in this scenario consists of the aspects of the data (plural of datum) of these concepts and all related interactions we recall or have generalized about in the past. To create a formal model with which we can reason we add propositions such as: “hunger can be cured by eating” and “one must only eat edible items”. From here, logical consequences (entailments) follow. So with this model, I can conclude as a matter of logical necessity that eating an apple could cure my hunger. So while our experience may remind us (by association) of many occasions on which apples cured hunger, reasoning provides a causal connection. Furthermore, anyone would reach that conclusion with that model even though the data behind their concepts varies substantially. The conclusion holds even if we have never eaten an apple and even if we don’t know what an apple is. So chains of reasoning can provide answers where we lack first-hand experience.

So we form idealized worlds in our heads called models so we can reason and manage our actions better. But how much better, exactly, can we manage them than with mental association alone? At the core of formal systems lies logic, which is what makes it possible for everything that is true in the system to be necessarily true, which in principle can confer the power of total certainty. Of course, reasoning is not completely certain, as it involves more than just logic. As Douglas Hofstadter put it, “Logic is done inside a system while reason is done outside the system by such methods as skipping steps, working backward, drawing diagrams, looking at examples, or seeing what happens if you change the rules of the system.”5 I would go a step beyond that. Hofstadter’s methods “outside the system” are themselves inside systems of rules of thumb or common sense we develop that are themselves highly rational. We might not have formally written down when it is a good idea to skip steps or draw diagrams, but we could, so these are still what I call formal models. But that still only scratches the surface of the domain of reason. Reasoning more significantly includes accessing conscious capacities for subrational thought across informal models, and so is a vastly larger playing field than rational thought within formal models. In fact it must be played in this larger arena because logic alone is is an ivory tower — it must be contextualized and correlated to the physical world to be useful. Put simply, we constantly rebalance our formal models using data and skills (e.g. memory, senses, language, theory of mind (ToM), emotion) from informal models, which is where all the meaning behind the models lies. I do still maintain that consciousness overall exists as a consequence of the simplified, logical view of rationality, but our experience of it also includes many subjective (i.e. irrational) elements that, not incidentally, also provide us with the will to live and thrive.