The Story of the Mind: A New Scientific Perspective into Our Essential Nature

We all have minds. We use them continuously every waking moment of every day. We take what they can do for granted. Why we have them and how they work has no direct bearing on our lives, so we mostly just carry on and don’t worry about it. It’s remarkable that all understanding, scientific or otherwise, depends critically on our ability to use our minds, yet we don’t have to understand them to use them and thus far have failed to do so. Similarly, we can use software without having read its source code, much less having been able to have written it. The “software” of our minds, usually called wetware, was “written” to meet our needs in the ancestral state, not the rapidly changing and increasingly artificial environment we have created. We can’t afford to remain mere users; we have to understand what makes us tick and even to tweak or upgrade our programming if we want to survive in the long run. To get started, we have to find a way to make minds and ideas into objects of study themselves. But how should we go about it? The Greeks started with the psyche, which is analogous to what we would call the soul. Aristotle wrote in Peri psyche that the psyche is that which makes the body alive and able to perform its characteristic functions. He divided them into vegetative powers, concerned with nutrition and growth; sensory powers (that is, vision, hearing, taste, smell, and touch, as well as the internal senses of imagination and memory); and intellectual powers (understanding, assertion, and discursive thinking).1. From my perspective, this is pretty close; closer than anyone has come since. The theory I will develop here will corroborate his view. What Aristotle had that has been in short supply lately is a broad mandate. Science did not yet exist, so he created it, substantially filling in the major branches. As the tree of science has grown, it has become less fashionable and feasible to address the big picture with fresh eyes the way he did. Science has trended toward specialization, not generalization. There are perfectly good reasons for this, which I will address later, but suppose we take it as a challenge. What if our understanding of the mind has been held back by the way science has branched, leading to detailed study in specialized areas while missing the forest for the trees? What if I took on the broad mandate to explain the mind from first principles, rethinking the structure of science and what it means in relation to the mind?

That challenge is a raison d’être quest for me. I’ve always been just a bit obsessed with examining my own thought processes to get to the bottom of it all. We all have thoughts about our thoughts and don’t expect to make a career of it, so I was not surprised to find no obvious path forward in college. I started out focusing on genetics, but it was all lab work in those days and I am more of a theorist than an experimentalist. I turned my attention to computers but saw no promise in the artificial intelligence of the time, which was based entirely on representation and logic. I put my thoughts on the matter aside as something to get back to in the future and settled into a career in systems and application programming, which kept me off the streets. But it kept bothering me, so I started writing a book to explain the mind in 1996. But with a family and a full-time job, I was only able to make sporadic progress until I retired in 2016, at which time I decided to fulfill the quest. I’ve rewritten the first few chapters dozens of times as my ideas have evolved.

Many other people have also been thinking about the problem. Unraveling the mind has become something of an international obsession over the past fifty years. But I don’t think many have looked at it with a broad mandate and fresh eyes. It’s all dividing and no conquering because it is not a problem that can be solved with specialization. We need to step back to a state of maximal generalization and from there start to focus in. I am not here to refute any of the findings of science. I am here to embrace them. But our scientific knowledge that bears on the mind is scattered and does not speak to the nature of mind confidently from the top down. Different schools of thought have evolved to cover different aspects but have only culminated in a lot of conflicting schools of thought. I’m going to try to develop a firm foundation for a comprehensive view that integrates our scientific knowledge into one framework.

My approach is scientific but to achieve that we have to agree on what it means to be scientific. For starters, I will take on the philosophy of science itself, both defining meaning in science and providing an expanded framework of what science should be. Science is founded on educated guesswork, by which I mean proposing hypotheses to explain phenomena. One then tests the hypotheses, which either confirms them or highlights the need for new hypotheses. All practicing scientists are expected to conduct original scientific research, which includes both new hypotheses and new experiments to test them. I am not an experimentalist; I am a synthesist. My goal is not to make new scientific discoveries, but to reorganize existing scientific knowledge into a more explanatory framework. Consequently, I will only be proposing hypotheses that are already supported by abundant evidence. My claims, as I state them initially, may not seem adequately supported, but as the book proceeds I will fill in the gaps. It is not my intention to be contentious or even controversial as I am only seeking to form a larger accord in scientific thought, which is necessary to propose and advance theories of the mind. Keep your eyes open for any claims that contradict settled science and feel free to call me out on them.

I take heart from Joscha Bach’s essay Is Scientific Genius a Thing of the Past? on the current sad state of paradigm shifting in the sciences. Bach argues correctly that many sciences are in dire need of a revolution, but there just isn’t a framework for uprooting the status quo in the sciences. As he puts it in the case of cognitive science, it is “a bunch of incompatible methodologies competing for the same funding bucket” which has rather foolishly put most of its eggs in the brain scanning basket. Once paradigms have taken hold, as Thomas Kuhn taught us, strong sociological forces take hold that make it hard for new paradigms to overtake them. We don’t need to tear science down and build it up again, but we do need to reform from within to include the foundations of science in scientific discussions. Scientists know that science must always iterate and cannot produce absolute knowledge, so they must also admit that the foundations are not absolute either. Instead of just propping up status quo paradigms, every paper should question the paradigm it seeks to support both by describing what that paradigm even is and by offering some alternatives. In this way, we will empower all scientists to work on generalities and not just details. The status quo becomes an immovable block only if we have no mechanism to move it. Instead of simply hoping social forces will be strong enough to overcome the establishment, we need to put the seeds of change into the establishment. So I open the challenge to any scientific discipline: insist that every paper go beyond the details to encompass the full range of assumptions on which it rests, with at least a nod to alternative assumptions. It is not that every paper has to launch a scientific revolution, it is that every paper must be empowered to do so. We need to be given access to levers that can move the earth or the institutions we have constructed to keep civilization running will inadvertently destroy us by failing to respond adequately to change, which is always accelerating.

The Mind Matters: The Scientific Case for Our Existence

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 will agree that there is a physical basis for everything physical, I will not agree that there is a physical explanation for everything physical. Instead, I will argot that there is a functional basis for a few kinds of physical things (namely living things), and that these physical things cannot be explained without considering that functional basis. Conversely, there is an informational basis for everything functional, but a physical basis for a few kinds of functional things (again, living things), and these functional things cannot be explained without considering that physical basis. 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 don’t manage information. Once physical things start to manage information, physical monism breaks down. I will argue that function is a second kind of existence entirely that can be said to characterize a system’s capabilities rather than its substance. Functional systems can be physical or fictional systems, but if they exist physically then their physical existence is independent from their functional existence and only relevant to it in limited ways. A capability is the power to do something, which is different from any underlying physical mechanism that might 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. The brain is an organ for dynamically managing function, and the mind is a subprocess of the brain that manages high-level functionality through a first-person interface. The brain is so abstracted from physical mechanisms that we only marginally understand how it physically works at this point, and the mind is still further abstracted so that we have no idea how it physically works or even what fraction of the brain constitutes the mind. Scientific explanations themselves (even those of materialists) are entirely functional as they speak to capabilities and have no fixed physical form. An eliminative materialist will hold that a scientific theory is ultimately a physical thing, and, like all ideas, has a physical form in each of our brains. I hold that this is silly and that all ideas and theories are functional things built out of a fabric of relationships between other functional things.

Reductionists reject downward causation1 as a nonsensical emergent power of complex systems. 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 will be mirrored to follow the natural hexagonal symmetry of water, so it preferentially grows symmetrically. 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. In fact, any system capable of leveraging feedback can make adjustments to reach or cause an objective, if it also has a means 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 force 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 it is possible using physical systems to manage information and apply feedback. Downward causation doesn’t happen in any system between any two lower and higher “levels” of organization, but living things use at least two and arguably more information management systems at different levels. Reductionists hold that causation is the direct consequence of subatomic forces, which can be demonstrated for nonliving natural systems, and 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.

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 this book before I discovered Bob Doyle’s writings, so I did not know that anyone else had proposed the full-fledged existence of function (which is synonymous with information), independent of physical matter. But I am greatly heartened to see that at least someone doesn’t think I am crazy. From this common starting point I will go quite a bit further than Doyle has to build a detailed explanation of the mind.

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, physical things can configure themselves using mechanisms I call information management systems so as to manage complex feedback behavior relative to other physical things. These feedback behaviors can only be predicted, which is to say understood, by using a paradigm based on information and function. Physical information management systems can leverage information, but the same information can exist in multiple physical systems or on a theoretical basis outside any physical manifestation. So a physical universe such as ours can use information while every part and aspect of it remains physical. However, if you want to explain how the parts that leverage information work, you must invoke information to explain them. This is simply because “explaining how” is a functional operation built out of information that summarizes some things about the underlying noumena. By the same token, I am not saying that Laplace’s demon could not predict the future perfectly. If we suppose the universe is deterministic, i.e. that the future follows entirely from past conditions and hence could be perfectly predicted given complete knowledge of current conditions, then Laplace’s demon is a being with that perfect foreknowledge and the computing power to apply it. Our current understanding of the universe suggests that it is a deterministic multiverse that branches an infinite number of times every second as every wave “collapses” into a particle. Put another way, every particle is a wave that interacts at a distance with every other particle, but at every moment it branches off a universe in which it has resolved into a particle in a particular place. But the actual mechanics of the universe, and even whether it is strictly deterministic, are not relevant to this argument. The point is that this demon could perfectly predict the future and would have need no concept of information to do it: particle would hit particle like billiards and everything would unfold in a fixed way. The demon would not particularly care that many particles had aggregated into atoms and molecules and rocks, and that the fate of all the molecules in a rock were more closely bound than those in the air around the rock. And it would also not particularly care that interactions of other aggregates of matter were closely bound by informational relationships, e.g. that trees spring from seeds. At the subatomic level none of that matters. But if you want to explain how or understand why, then you have crossed over into the realm of information and function and need to consider methods of summarizing and generalizing and where information has been leveraged by the system. There is no one analysis that explains summary behavior; an infinite variety of techniques may be used to provide different degrees of ability to predict the behavior of the system. But all such explanatory methods are informational constructs, not physical. And because our minds provide us with sufficiently general-purpose tools to explain things, we can explain anything any number of ways, though our minds are not powerful enough to explain everything every possible way. The upshot of this is that reductionism is bankrupt, because its goal is to explain, and Laplace’s demon doesn’t have to explain. You can’t explain how rocks behave without grouping them into an aggregate, unless you are happy with an infinitely detailed explanation that tracks every particle, which would no doubt make a note of how each particle was strongly bound to its neighboring particles. And you can’t explain how a tree springs from a seed without explaining how evolution has shaped self-perpetuating organisms by perpetuating functions through genes, unless you are happy with an infinitely detailed explanation that tracks every particle, which would no doubt make a note of indirect feedback relationships that have the effect of perpetuating an overall organizational structure, even though the molecules comprising it are flushed periodically by metabolic processes. But simplicity does matter to explanations, because in physical information management systems the model must be much simpler than that which is being modeled, and the simpler it can be without appreciable loss of predictive power, the better.

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. So far as we know, everything in the universe except life is devoid of function. 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 recurred 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 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 to discuss such functions independently of the underlying physical systems they run on. More importantly, most of the meaning of these functions is quite independent of their underlying physical systems. Also note that minds heavily leverage the information stored in organisms, so one could not replicate a mind comprehensively on a computer without a functional understanding of both. Civilizations leverage minds and so would require an understanding of genes, minds, and culture to be replicated, but software stands alone. A software system employs an independent model to represent and process information. The extent to which the information in a software system can be correlated or applied outside that system is entirely subject to the interpretation of the user, at which point the combined system of software plus user acquires a dependence on the information management system of the user too.

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 is correct if we take organisms to be strictly physical and “we” to be strictly functional, which are the senses in which we usually use these words. 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 that just makes 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”). You can do a number of complex behaviors while sleepwalking, even including going to the fridge or in some cases even making dinner or answering simple questions, but there is “nobody home,” there will be no reflection or goal directiveness. We collect that experience together as a memory of what just happened, and access to that memory gives us our experience of being conscious. Running simulations on that memory and further integrating it with the world makes our conscious experience seem meaningful to us. Bach identifies the dorsolateral prefrontal cortex as the brain region that does this in our brain, and this may well be where the lion’s share of advanced mental simulation happens, but our experience of consciousness draws other features from many other parts of the brain, some found only in higher animals and some found in almost all animals. Bach’s theory proposes that “You are not your brain, you are a story that your brain tells itself,” which is correct except for a small logical error — it puts physicalism ahead of functionalism by implying that it is meaningful to say that a brain has an “itself”; it doesn’t. The sentiment should be, “You are not your brain, you are a top-level story that your brain culls from the information that it manages.” The brain is not the actor here; it is just an instrument.

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 recognizes material, formal and efficient causes as physical substance, how we classify it, and what changed it, but 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. But for functional systems, teleology is both intuitively true and actually true, and the mechanism is information management.

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 19486, which then led into systems theory7, 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 Mind8 in 1949. While 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, Ryle felt it still had tacit if not explicit “official” support. While our lives unfold in two arenas, one of ‘inner’ mental happenings and one of ‘outer’ physical happenings, each with a distinct vocabulary, he felt philosophy presumed more than this: “It is 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, 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, the mistake arises from a failure to understand that forest has a different scope than tree.9 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 talking about telling time. 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, but he overstepped his knowledge by attempting to provide the physical explanation. 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 fundamentally not physical and their existence is not dependent on space or time; they are pure expressions of hypothetical relationships and possibilities.

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 a 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 arise subconsciously but sometimes 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 conceptual thinking, i.e. thinking with concepts, which most notably includes logical reasoning. Logical reasoning starts with premises, which are statements (predicates about subjects) taken to be true, and draws consequences from them. Subjects, predicates, and premises are concepts viewed from a logical perspective. The second approach is subconceptual thinking, which is a kitchen sink of data analysis capabilities. Unlike instincts, whose reactions are fixed, subconceptual thinking does not produce fixed responses. Subconceptual thinking includes common sense, pattern recognition, and intuition, but also includes much of our facility for math, language, music and other largely innate but not fixed, instinctive talents. Much of what we learn from experience is subconceptual in that it is not dependent on conceptualizing or logical reasoning. Conditioning, for example, with or without reinforcement, is subconceptual. Much, or even most, of the data our brains gather about the world is subconceptual and is there to help us despite the lack of a conceptual understanding. When conceptual and subconceptual thinking are done consciously we call it reasoning. Reasoning is the conscious capacity to “make sense” of things, which means to produce useful information. What we are conscious of is organizing, weighing, and otherwise assessing all the factors relevant to a situation. It doesn’t need to involve concepts or logic, and mostly it doesn’t. We lack conscious awareness of many aspects of both conceptual and subconceptual thinking, so these are said to be subconscious. For example, we recognize and recall things without knowing how, we can tell when sentences are properly formed, and we have hunches about the best way to do things that just come to us by intuition.

Subconceptual thinking uses subconceptual data (subconcepts) while conceptual thinking uses concepts. 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 knowing 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. without a 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. Some kinds of reasoning can be done subconceptually by pattern analysis, specifically 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, not diffuse, unstructured data. The other kinds of reasoning can also leverage concepts, but deduction specifically requires them. Also, so far as we know, deduction can’t be done subconsciously. 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. Note that while all reasoning, being the top level or final approval of our decision-making process, is also strictly conscious, many other kinds of conceptual and subconceptual thinking happen subconsciously.

Relationships that bind subconcepts and concepts together form mental models, which constitute our sense for how things work or behave. Mental models have strong subconscious support that lets them appear in our heads with little conscious effort. The subconceptual aspects of these models give them a very “real,” sensory feel to us, while the conceptual aspects that overlay them connect things at a higher level of meaning. Although subconceptual thinking supports much of what we need to do with these models (akin to an autopilot), conceptual thinking organizes things better for higher purposes, and logical reasoning can be much more effective for solving problems than our more limited conceptual and subconceptual thinking processes. While conceptual and subconceptual data analysis is quite powerful, it can’t readily solve novel problems. Logical reasoning, however, gives us an open-ended capacity to chain causes and effects in real time. As we mature we build a vast catalog of mental models to help us navigate the world. We remember the specific times they were applied, but mostly the general sense of how to use them. Note that although logic helps hold mental models together, it doesn’t follow that understanding is a consequence of logic. John von Neumann once said, “Young man, in mathematics you don’t understand things. You just get used to them.”10 This is actually true of all understanding: understanding is news you can use. He didn’t just mean we are used to things, but that we are used to them working for us. The function captured by information needs to be applicable to close the loop and count as understanding.

The physical world lives in our minds via mental models. Our minds hold an overall model of the current state of the physical world that I call the mind’s real world. Whatever the physical world might actually be, we only know it consciously through the mind’s real world. The mind’s real world leverages countless mental models we have that have helped us understand everything we have ever seen. These models don’t have to be right or mutually exclusive; whatever models help provide us with our most accurate view of physical reality comprise our conception of it. The mind’s real world “feels” real to us, although it is purely a mental construct, because the mind is inclined to interpret its sensory connections to the physical world that way instinctively, subconceptually and conceptually. But we don’t just live in the here and now. Because the mind’s primary task (and the whole role of information and function) is to predict the future, mental models flexibly apply to a range of circumstances. We call the alternative ways things could have been or might yet be possible worlds. In principle, the mind’s real world is a single possible world, but in practice our knowledge of the physical world is imperfect, so our model of it in the past, present and future is always a constellation of possible worlds.

In summary, all behavior results from instinct, subconceptual thinking, and conceptual thinking. Our mental models combine these approaches to leverage the strengths of each. Genetic data is a first-order bearer of information that is collected and refined on an evolutionary timescale. Instincts (senses, drives, and emotions) are second-order bearers of information that process patterns in real time whose utility has been predetermined by evolution. Subconcepts are third-order bearers of information in which the exact utility of the patterns has not been predetermined by evolution, but which do tend to turn out to be valuable in general ways. Finally, concepts are fourth-order bearers of information that are fundamentally symbolic; a concept is a pure abstraction that represents a block of related information distilled from patterns in the feedback. Some subconscious thought processes (e.g. vision and language processing) manipulate concepts in customized ways without applying general-purpose logical reasoning, which can only be done consciously. Logic finds reasons, i.e. rules, that work reliably or even perfectly in mental models. The utility of logical reasoning ultimately depends on correlating models back to the real-world, and for this we depend on mostly subconscious but conceptual reverse recognition mechanisms that fit our models back to reality. Recognition and reverse recognition are complex problems requiring massive parallel computation for which present-day computers are only recently developing some facility, but for us they just happen with no conscious effort. This not only lets us think about more important things, it makes our simplified, almost cartoon-like representation of the world through concepts feasible.

Our four real-time thinking talents — instinct, subconceptual thinking, conceptual thinking, and logical reasoning (this last one being a kind of conceptual thinking) — are distinct but can be very hard to cleanly separate. We know instinct influences much of our behavior, but we are quite unsure where instinct leaves off and tailored information management begins because they integrate very well. And even complex behavior, most notably mating, can be driven by instincts, so we can’t be too sure instinct isn’t behind any given action. While subconceptual and conceptual thinking can be readily separated based on the presence of concepts, it can be difficult to impossible to say at exactly what point a concept has coalesced from subconcepts. In theory, though, I believe there must be a logical and physical point at which a concept comes to exist, the moment that a set of information is referenced as a collective. This suggests that conceptual processes differ from subconceptual ones because they involve objectification of data by reference. Logical reasoning refines conceptual thinking by introducing the logical form, which abstracts logical operations from their content, making it possible to devise internally consistent logical models within which everything is necessarily true. 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 subconsciously, because we consciously decide whether to act. Or do we? Habitual or snap decisions are sometimes made on a “preapproved” basis where we act entirely on subconscious reasoning which we then only observe consciously. We do always have the conscious prerogative to override “automated” behavior, though it may take us some time to decide whether to do so. The truth is, at one level of granularity or another, all our activity is driven subconsciously by muscle memory or procedural memory, which needs some conscious approval to proceed, but that approval can be implied by circumstances, effectively taking it out of the hands of consciousness. I posit that logical reasoning of any complexity only happens consciously as well because only consciousness is equipped to pursue a chain of reasoning. We can still reason logically when we dream and daydream, as this chaining capacity is not otherwise occupied then, with greater free association that can lead to more creativity, though with less rigor.

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.”11 So it is entirely instinctive. We know language acquisition is similarly innate in humans because humans with no language will create one12. But we know that all the artifacts of civilization (except perhaps the hand axe, which may have been innate), including all its formal institutions, are primarily the products of thinking, but subconceptual and conceptual, and the experience they created. Our experience of our own minds is both natural (i.e. instinctive) and artificial (i.e. extended by thinking and experience), but these aspects are so intertwined in our perspective 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 rationalized 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 indicate the conceptual use of cause and effect, which goes beyond what instinct and subconceptual thinking could achieve. Other animals do not, and I suspect all others lack even a rudimentary conceptual capacity. 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? It is our greater capacity for abstract logical reasoning. Abstraction is the ability to decouple information from physical referents, to think in terms of concepts and mental models in logical terms independent of physical reality. We consequently don’t need to constrain our thoughts to the here and now; we can dream in any direction. This greater facility and impetus to abstraction has coevolved with a better ability to think spatially, temporally, logically and especially linguistically than other animals. Loosening this tether back to reality began with small changes in our minds, but these changes opened a floodgate of increased abstraction as it provides greater adaptive power. Though we must ultimately connect generalities back to specifics, most words are generic rather than specific, meaning that language is based more on possible worlds than the mind’s real world specifically. I call our ability to control our thoughts in any direction we choose directed abstract thinking, and I maintain animals can’t do it. Advanced animals can logically reason, focus, imitate, wonder, remember, and dream but their behavior suggests they can’t pursue abstract chains of thoughts very far or at will. Perhaps the ecological niches into which they evolved did not present them with enough situations where directed abstract thinking would benefit them to justify the additional costs such abilities bring. But why is it useful to be able to decouple information from the world to such a degree? The greater facility a mind has for abstraction, the more creatively it can develop causal chains that can outperform instinct 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 hence 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 a portion of cognitive science, 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. With ingrained behaviors, the ends (essentially) justify the means, which makes the means a gestalt indivisible into explanatory parts. Explanation is irrelevant to the feedback loops that create instinct, which produce supporting feedback based on overall benefit to survival. Subconceptual thinking is also a gestalt approach that applies innate algorithms to subconcepts (big data) and uses feedback to collect useful patterns. Conceptual thinking (logical reasoning) creates the criteria it uses for feedback. A criterion is a functional entity, a “standard, ideal, rule or test by which something may be judged.” What this implies is that reasoning depends both on representation (which brings that “something” into functional existence) and entailment (so rules can be applied). Philosophically, reasoning can never work in a gestalt way; 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 these 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 general-purpose skills to find certain kinds of patterns. Conceptual thinking adds more power because self-contained logical models are internally true by design and can build on each other to explain 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”13. 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

Function entities have different forms. Function is ultimately a process of applying information, but we can use nouns to refer to the steps or the whole process, and we can also store packets of information for later use. These units can exist indirectly as subconcepts or be called out directly as concepts. In either case, we they can exist mentally, verbally, or via natural or artificial representations. Each of these has a noumenal physical form which is subject to further mental interpretation of its phenomena. How can we keep them straight? Let’s take an example. The concept APPLE (capitalization is a way of indicating we are referring to apple as a concept) refers to the generic idea of an apple and not to any specific example. It refers specifically to the mental noumenon for apple and not verbal or artificial forms. Whenever we think about apples, we are bringing forth (phenomenal) memories about this noumenal form, and by considering a handful of thoughts about apples we develop a pretty good (phenomenal) idea of what the noumenon means. When we talk about apples, we are using linguistic tools to stimulate the same memories, but talking lets us bring the same concept into other people’s minds as well. An apple itself, or a model or depiction of one, will also bring APPLE to mind. So APPLE is the concept in our mind, not to be confused with thoughts about apples, discussions of apples, apples themselves, or physical models of apples. The same distinctions apply to subconcepts, but it is harder to talk about them because to do so we have to use words which then elevates them to the status of concepts. For example, foods affect our moods, and we have feelings about how all foods do this from our experience, and we consider how foods will affect our moods when choosing what to eat. To the extent we don’t think about this relationship of foods to moods directly, or talk about it, or think about mood-altering attributes of foods, this whole topic is subconceptual. It exists and and we subconsciously leverage information about it. Discussing it as a topic this way carves it out and conceptualizes it, which is not the same thing really because doing this puts all the emphasis on the impact eating a food has on mood, which is only the tip of the iceberg of all the kinds of subconceptual associations we make about the foods we eat.

JUSTICE is a more abstract concept as the underlying noumenon is functional instead of physical. And yet, APPLE is abstract as well since no generic apple physically exists. The “real” entities behind JUSTICE and APPLE are functional: they are defined in terms of characteristics that relate to their purpose. A specific apple has an exact physical noumenon, and a concept we might create to refer to it, e.g. THIS_APPLE, refers to that noumenon and no other. All concepts are generalizations based on features shared by a number of more specific instances. THIS_APPLE does this by generalizing many observations of an apple into a single persistent object. THIS_APPLE with a bite taken out of it is still the same apple conceptually provided our concept allows for a certain range of changes provided it satisfies the APPLE concept before and after. The bite taken out, for example, is not the apple and does not inherit the properties we associate with APPLE. Each slice of an apple sliced into sixteen identical pieces, however, retains the full complement of the traits an APPLE has, albeit only to the extent 1/16th of an object can carry the properties of the whole object. NEW_YORK_CITY does refer to a specific physical noumenon, but the boundaries of that physical object are considerably more diffuse conceptually than those of THIS_APPLE because while the city does have specific city limits, our functional use of the term is not always that exact.

I mention these things to illustrate why descriptions of things must always be sketchy. Concepts and models generalize less detailed versions out of more detailed instances. The resulting models and descriptions presuppose a great deal of context which is presumably understood and agreeable. It must focus on only the most salient aspects in the hopes that omitted details are not material to understanding. For physical things, one can presumably see the object and make other physical observations that provide deeper understanding far beyond what superficial observation can achieve or verbal description can convey. But direct observation is not possible for functional things. Fortunately, most functional things, such as justice or 3D vision, also have instinctive and subconceptual support. So while we can’t see them in the physical world, we share an innate grasp of them. We also have names and descriptions for them which call them to mind, so these kinds of functional things can be nearly as evident to us as physical things. We also invent functional things that go well beyond our built-in capabilities, such as art, fiction, or any system of rules, such as the law. As I attempt to unravel how the mind works, I form concepts and models in my head which I describe with words. While the results will be sketchy, if I pick good concepts and models I will achieve simplicity and broad applicability. I believe most of the context falls in the realm of common knowledge and so will be understood and agreeable. We do have an innate grasp of what our minds are doing noumenally, we just haven’t put much energy into describing it phenomenally. To ensure the greatest simplicity and applicability I will start from the top down with the most salient aspects and expand the model as I go deeper. My explanations will appeal to and depend heavily on our own personal understanding of first-person experience. This aspect is introspective, which poses a challenge to objectivity. I will address that challenge in more detail later, but in short, I will look to introspection to stimulate hypotheses, not to test them. The resulting descriptions of mind I develop will constitute a theory to be tested. Like all theories, it is not intended to have the same function as the mind or to be complete, only to be internally consistent and supported by the evidence. I intend to show that it is consistent with prevailing scientific perspectives once those perspectives are interpreted in the framework of form and function dualism. Just how one can objectively test theories about functional things, which are necessarily about and conceived using subjective mechanisms, is a subject I will discuss later.

All scientific theories are descriptions, and hence are sketchy representations of reality. Boyle’s Law describes the relationship between volume and pressure of a gas at constant temperature. Boyle’s Law is “sketchy” even though it seems to work perfectly because volume and pressure are approximate measures of reality dependent on instruments and not fundamental “properties” of spacetime. Spacetime does not actually have “properties”, which are ways of describing aspects in general ways when actually the component pieces under observation just follow their own paths and are not inherently collective. But we can measure volume and pressure almost perfectly most of the time, and in these cases the law has always worked to our knowledge and so we can feel pretty comfortable that it always will. Though unprovable and sketchy, we can label it “true” and depend on it without fear. Of course, to use it we have to match our logical model which models gases as collections of independently-moving particles with volume and pressure to a real-world circumstance involving gas, which depends on observations and measurements to provide a high probability of a good fit of theory to practice. This modeling, matching, observing, and measuring involves some subjective elements which have some uncertainty and vagueness themselves, so all objectivity has limits and caveats. But we can accurately estimate these uncertainties and establish a probability of success that is very close to one hundred percent in many real-world situations.

Given these caveats about how science can only characterize subjects and can’t reveal their true nature, let’s take a look at which sciences study functional subjects. 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 to 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 are presumed by materialists to be reducible to fundamental physics, at least in principle. 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 the most objectively observable, but, just as abstract concepts have abstract noumena, any abstract function implies an abstract corresponding form. Once an entity exists functionally, one can study that function observationally much the same way one would study more concrete noumena. Formal sciences define premises and rules from which one draws implications. These premises, rules, and implications are themselves the form of the formal sciences and are therefore the only kind of noumena which we can see directly. None of the knowledge so derived is phenomenal, i.e. based on observation; it is all spelled out. These noumena are clearly abstract rather than physical, but does this imply their existence is functional? Yes. One could divide function or information into two camps, deductive and inductive, based on whether it follows from the definition of a model of from observations of the model, but this division is not helpful here, so I will lump them together. We can conclude that the formal sciences usually study form, but it is quite significant that they take on an experimental aspect when theorems are proposed that can’t yet be proven. Induction is used to support these theorems, and some formal sciences shift substantially toward being experimental sciences because experimental results are powerful when formal proofs are just not feasible. The experimental sciences, being physical, life, social and applied science, always use physical observations to validate hypotheses, but some study form while others study function. The physical sciences clearly study form, specifically the forms bound by the theory of everything, which is not yet a concrete theory but a stand-in for phenomena described by either general relativity and quantum field theory. The life sciences study function, specifically functions that result from the <a href=”https://en.wikipedia.org/wiki/Evolution>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 very gradually evolve using very complex, interwoven, and layered feedback responses that gradually cause 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 study function, specifically functions produced by the mind, which is not yet explained by any solid theory. As a subset of both the life and physical sciences, one must also study those to understand the mind, but nearly all understanding of social sciences follows from considerations relevant to minds that are pretty independent of considerations of living and physical forms alone. Finally, applied science studies both form and function, specifically technological forms and functions that help us live better. Applied sciences further develop social, life, and physical sciences in directions particularly beneficial to people. Let me further add that the experimental sciences depend heavily on the formal sciences for mathematical and logical rigor. In summary, the two basic objects of the study of form are form itself and the universe, while the two basic objects of the study of function are life and the mind.

Although the formal sciences study form directly, they do so to achieve function. 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, which is consistent with what I am calling form. But are they completely theoretical, i.e. indifferent to any concern of applicability? Yes, in theory, they are, and so they are often significantly so in practice as well, but it would be naive to think that the fields receiving the most attention don’t do so because they have the greatest applicability. After all, an infinite range of formal sciences are possible, and any arbitrary set of rules may be chosen for any of them, so if we had no criteria for studying one over another then their results would resemble the infinite monkeys trying to produce the works of Shakespeare. In Mathematics Form and Function, Saunders MacLane proposed six possible foundations for mathematics: Logicism, Set Theory, Platonism, Formalism, Intuitionism, and Empiricism. But these foundations are all wet — all the formal sciences really aim to do is maximize functionality. Whether they align with logic, sets, ideals, forms, hunches, evidence, or any arbitrary rules is not the point; applicability is the point. Theoretical math has the unspoken goal of supporting applied math.

For that matter, the other science that studies form directly, physical science, also does so to achieve function. We may think we want to know the structure of the universe independent of what applications we might use that knowledge for, but we literally can’t because knowledge itself exists to support the application of knowledge. Knowledge is entirely structured around its predictive power; information differs from noise only because observed patterns may occur again. The theories of general relativity and quantum field theory only characterize the underlying universal structure in terms of ways one can predict the future. Our model of space and matter is really a model of time and causality and reveals no attributes of space or matter outside their functional roles.

Now that I’ve characterized distinctions between form and function in the sciences, it is time to tackle objectivity. Objectivity is what separates science from pseudoscience. Objectivity is hard to define, but I will look at it from several perspectives in an attempt to nail it down. According to the dictionary, it means based on facts and not opinions, but this begs the question of how we can distinguish facts from opinions. But facts that are simply true by definition are just tautologies. Fact usually refers to a truth about the natural world. Literally it refers to a third-person or objective perspective as opposed to a first-person or subjective perspective. In principle, the former is entirely factual and the latter is entirely idiosyncratic and consequently dubious. In an ideal objective perspective everything that is true is provably true. The formal sciences create the forms they study, so truth is indeed what they define it to be. The formal sciences thus achieve perfect objectivity, at least to the extent we are satisfied with which formal systems get studied. The applied formal sciences, like computer science, become a bit more of an art because their direction is constrained by physical limitations of computers and by human needs for kinds of functionality. But in the physical world, our perspective is entirely first-person, and we can only imagine what the third-person perspective might be like by collecting information we believe to be factual. So the question boils down to whether we can obtain absolute facts about the physical world, and, if not, how close we can come. Let’s go back to square one: what is our most certain knowledge? What we are most sure about, because it is independent of any external theory, is that we are, in each waking moment, thinking about things (Descartes’ “I think therefore I am”). Sureness itself requires a thinking entity capable of being sure about things. Beyond that, we know our senses feed us information, which puts information and function at the center of our knowledge of the world. We think, and what we think about is information. But thinking and information are inherently subjective, dependent on a first-person perspective that constantly shifts and which is completely inaccessible to anyone but us. We are certain we have these things, but we don’t really know quite what they are. Our next largest certainty is object permanence, the idea that the physical world exists independent of our conception of it. Not only does our sensory feedback strongly provide evidence of a permanent physical world, our whole sensory apparatus is designed from the ground up to endorse this perspective. Thinking and information are subjective, but object permanence goes to the literal definition of objective: “being based on objects under observation.” Objectivity defines a third-person perspective that arguably doesn’t “shift” because subsequent observations, by us or others, corroborate its existence. This corroboration is not proof, it is only an accumulation of information which, taken together, is consistent with the idea that objects can persist relatively unchanged over periods of time. Objectivity is useful because things generally persist, but when they do change it seems to happen according to hidden rules of cause and effect. Experimental science follows the scientific method, which makes observations that minimize subjective bias and maximize reliability and broad applicability and then proposes the simplest rules of cause and effect that would account for those observations. No actual truths emerge from this approach, but rules that can predict many things almost perfectly give science tremendous explanatory power. The original method consisted of systematically observing nature for patterns, formulating hypotheses to explain them, proposing experiments to test the hypothesis, conducting the experiments using instruments where possible, and adjusting hypotheses and iterating hypotheses that did not hold up. The extent to which subjectivity can bias results is much greater than originally anticipated, and the scientific method now protects against some kinds of bias.1Peer review was added to improve quality and standards but has the effect of spotting subjectivity by objectifying the studies themselves; more eyes minimizes subjectivity in many ways. Preregistering research for publication before the results are known helps protect against the bias of publishing only desirable results. Research since 1971 into the many ways bias can creep into scientific studies are now well known and are generally avoided. 2 Bias in the institutional and societal structure of science remains a big problem. For example, funding is biased and often comes with strings attached, papers hide behind paywalls even though the studies were paid for with public money, and status-quo prevailing paradigms are hard to unseat because scientific revolutions are disruptive and so are discouraged. 3 Bias is hardly the only problem plaguing science right now; it has other big systemic problems, e.g. difficulty in getting funding, bad incentives (e.g. pressure to publish) leading to bad science, inadequate attention to replicating results, not doing peer review right, so much focus on specialization that general summarization for public consumption doesn’t happen, and a miserable, poor, stressful life for young scientists.4
The imperfections of the scientific method aside, experimental science has produced many theories which may not be absolute truth but which can effectively be taken as true with little risk for many intents and purposes.

Experimental science has had its greatest success in the physical sciences, which, as was discussed above, study physical forms. Great objectivity is possible about the study of physical forms because we have developed so many instruments which can gather information about them with almost complete impartiality. The physical sciences heavily leverage the formal sciences, because accurate physical data support very precise mathematical models. It doesn’t mean physics is solved; general relativity improved on Newton’s law of universal gravitation, and MOG (MOdified Gravity) may improve on general relativity. And it doesn’t free formal and physical sciences from elements of subjectivity; our formalizations and theories on these subjects invariably involve judgment and bias because any system can be modeled (i.e. simplified) in an infinite number of ways. But we have had little trouble agreeing on models that work well, and keeping secondary models as backups. The main thing is that we know the assumptions we are building on.

But life is a far less tractable subject. Billions of years of adaptations have piled on complexities orders of magnitude harder to decipher than those of nonliving physical systems. That complexity is driven by feedback to provide general-purpose functionality instead of the specific causes and effects studied in physics and chemistry. Functionality can’t be studied directly with instruments, but requires analysis — information can only be understood by doing more processing on the information. Consequently, theories about life can never achieve the same level of formality and closure enjoyed by the physical and formal sciences. But we do have some objective sources of information about function in living things. Chiefly, we know life evolved, and we know a number of the mechanisms that made that possible. More significantly than the mechanisms, we know that it was driven by the value of function to survival. Function was selected for, and the mechanisms that made it possible were only along for the ride. It was not the genes that were selfish, but the functions of the genes. In other words, their physical form mutated along the way to preserve an unending chain of function, making their functional existence paramount and their physical existence incidental. Their functions are informational constructs whose true depth hides in the full history that led up to each gene surviving to the present day, and can’t be fully grasped just by discovering, say, the apparent primary role of the protein the gene encodes. The whole context of its function across a wide variety of circumstances contributes to why the gene is exactly the way it is. But one can still try to guess the functions and run tests to verify them, so we can still adapt the scientific method to minimize subjectivity and maximize reliability and broad applicability. I said above that the two basic information management systems these sciences study are life (overall) and the mind. In the following quick review I will further subdivide the mind into instincts, subconcepts, and concepts:

Genes. The bodies of living organisms are their physical manifestations, but the bodies exist to fulfill evolutionary functions, and in particular the function of each gene. Genes either encode proteins or regulate when genes turn on and off. Proteins engage in pretty specific chemical reactions which usually reveal at least the primary purpose of the gene. We can conclude that their chemistry ties pretty closely to their function. This doesn’t resolve finer details of their function because it is impossible to predict all the side effects the production of a chemical might have. By studying variants of the gene and situations where the gene is not expressed normally, under normal or stressed situations, we can come to appreciate its net value to the organism better. But we have found in many cases that chemical knowledge translates pretty well to functional knowledge in the case of genes. A common-sense mantra of science says that “form determines function,” which means that one can guess at the function by considering the form. In Darwinian evolution, the form (variation) precedes the function (as determined by selection), but the net result is that the function causes the form to be selected and then to persist, so function determines form. While it is not fashionable (yet) for life scientists to declare that function is their primary object of study because physical monism is still the dominant paradigm and physical evidence is still seen to trump functional evidence, they still must think about function first and physical mechanisms second. Having demonstrable physical mechanisms behind the functions makes it easier for the life sciences to claim objectivity. In this way, they can still primarily study function and teach biological functions even though function has no metaphysical basis in the physicalist philosophy of science. It has widely been fashionable over the past century to wear the absence of philosophical support as a badge of honor because science gets things done while philosophy seems to spin its wheels beaming increasingly irrelevant, but that willful ignorance is wearing thin because it is starting to impede further scientific progress. Philosophy does not stand apart from science but is implicit in and essential to all rational thought. It is true that philosophy is a massive accumulation of ideas, many of which are now outmoded. This makes it harder for newer philosophies to push through, but we desperately need a firmer foundation for science. I’ll discuss that further in the next chapter.

Instincts. Instincts are behaviors that respond to triggering stimuli in a way fixed by genes rather than experience, i.e. they are innate behaviors. An instinct strongly nudges the mind to react in a given way rather than being a hardwired reflex. We can distinguish instinctive from learned behaviors by seeing if the behavior happens without experience. Beavers that have never seen a dam can still build one, so we know it is instinctive. Humans who have never heard a language can quickly create one, so we know that language acquisition is instinctive. We don’t yet know which genes create instincts, and many instincts depend on many genes, but we know instincts are genetic because they are not learned. Once we have demonstrated that a behavior is instinctive, we could, in principle, discover the underlying genetic mechanisms that support it. In practice, this is somewhat beyond our current capabilities since we have found genes for few if any instinctive behaviors, but it still helps in our quest for objectivity to know that a behavior is instinctive because it means we objectively know it functions independent of learning.

Subconcepts. Before we get to subconcepts, let’s take a look at percepts. Percepts are the sensory feelings that flow into our minds continuously from our senses. The five classic senses are sight, hearing, taste, smell, and touch. Sight combines senses for color, brightness, and depth to create percepts about 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. Other somatosenses include balance, vibration sense, proprioception (limb awareness), hunger, erogenous sensation, and chemoreception (e.g. salt, carbon dioxide or oxygen levels in blood). Awareness and attention themselves have a feeling of time and space. We feel all of these things without reflection; they are immediate and hard-wired subconscious mechanisms that bring external information into conscious awareness.

We know that we can consciously reflect on our perceptions to find patterns, and when we find a pattern we will remember it and keep an eye out for it in the future. What is less clear to us because we have no conscious awareness of it is the degree to which our subconscious mind can find patterns and leverage them. However, we can use process of elimination to spot cognitive tasks we know we must be performing for which we can’t take conscious credit and attribute them to the subconscious. What I want to focus on here is the kind of stored experience the subconscious uses to do that, which can be divided into two categories, procedural and informational. Procedural memory, called muscle memory when used to help us move, focuses on helping us repeat useful behaviors. We consciously preapprove procedural memory to start or continue so long as situations warrant, so can appear to be driven entirely subconsciously, but conscious approval is required. Informational memory, which I call subconcepts, help us understand the world. Consciously, we just look at something and recognize it, but we know from an informational standpoint that it must require millions or billions of comparisons to work. We can conclude that recognition is a massively parallel subconscious process that accesses this large store of information. Recognition is so continuous and pervasive that it gives us an ongoing feeling of familiarity. Anything unfamiliar will stand out to our attention process, which is itself a subconscious process designed to make us focus conscious attention on anything novel or unexpected, i.e. not recognized by our subconceptual database. While procedural memory can be overridden, recognition can’t be suppressed; once we see (recognize) something, we can’t unsee it. We usually associate recognition with visual objects, but we can recognize many aspects of situations. If the recognition is faint, we get a sense of déjà vu, but usually we get comfortable familiarity. Recognition in this broader sense is more generally called intuition and includes everything we know without consciously reasoning about it. Intuition roughly divides into the confident knowledge we call common sense and the more speculative knowledge we call hunches. Let me go over concepts before considering objectivity again.

Concepts. As I have noted before, the hallmark function of conceptual thinking is problem-solving, which is much more powerful than recognition and intuition because logical reasoning chains causes and effects together, making multi-step procedures possible. Recognition and intuition can essentially only go one step deep, albeit very powerfully. Though it may sometimes seem to us multi-step solutions appear to us by intuition, I think it is more likely that we subconceptually store many things about steps and multi-step solutions we have used before, and intuition recognizes links we couldn’t see from conscious effort. With logical reasoning, we build simplified models of the world in which concepts form the subjects, predicates, and premises that we link together logically to reckon conclusions. Since formal models can achieve perfect objectivity because they define what is true, logical reasoning seems at least superficially to be the pinnacle of objectivity. This breaks down a bit because models must be applied to the real world, and they don’t always model it perfectly, and in fact can never do so perfectly. While a model itself can be spelled out in great detail and agreed to by any number of people quite objectively, how we apply it is always somewhat more circumstantial and a function of more intuitive, recognition-based matching, for which it is hard to be completely objective. Also, we rarely do spell models out in so much detail that the logic is perfect, so many of the steps in our thinking fall far short of being mathematical proofs and may, in fact, depend on unknown subjective processes. But the quest for objectivity should look first to the clear, widely accepted models, especially scientific models, and then to more subjective models and mechanisms which can vary from person to person.

Our direct knowledge of the management of information in the mind, via instinctive, subconceptual, and conceptual thinking, comes entirely from the use of our own minds and is consequently entirely subjective. How then can we develop objectivity about it? First, consider that all knowledge outside the formal sciences is subjective and that objectivity is just a tool to make some of it more reliable and broadly applicable. Evidence from instruments can be very precise and has been very helpful in the physical sciences, but the study of function doesn’t necessarily need that kind of precision. Working with the information sources available, we should still be able to develop reliable and broadly applicable theories. Next, we can still make hypotheses and test them even if it is harder to isolate the specific mental phenomena we want to test. But to get started I think we can find many things about mental function we could agree on without having to conduct tests at all. Finally, we should consider the ways the social sciences try to achieve objectivity considering their sources are purely subjective.

So yes, our thoughts are superficially subjective. While instruments can’t measure thoughts (although MRI and other brain imaging tools have done wonders to localize where different kinds of thoughts in the brain occur), most of the objective power of science comes from the use of the formal sciences, mainly math and logic, which can be applied to the mind as well. This is especially appropriate for understanding the mind, because the mind is what we use to create formal models. Introspective descriptions of our thoughts and feelings may be entirely subjective, but models supported by the available scientific information that are consistent with our subjective thoughts are objective. Theories that start on objective ground, even about subjective things, remain objective until proven otherwise. Anyway, our minds are designed to pull objectivity out of subjectivity: we all discern external objects the same way and believe it is generally possible to distinguish truth from falsity. Language itself depends on developing a shared understanding of concepts, which are abstracted objects. We mostly see the world as a place of facts, not opinions.

Our only evidence of our minds beyond our personal experience of them is our behavior. However, if we only had behavior to go on, we would be very hard-pressed to guess anything about the mechanisms of our minds. In fact, without our own first-hand experience of consciousness, we would have no reason to suspect that minds even existed. We would just see robots moving about getting things done, not unlike ants. To the extent ants can be said to have minds at all, which is pretty debatable, they are certainly not remotely as functionally complex as ours. Arguably the robots would claim they had minds, but, like us, they would still need to prove they existed and were not just charming affectations built into their programming but incidental to their operation. We can’t argue that minds are fundamentally necessary, either, since it is clearly possible to design a mindless brain to perform any given task we wish to give it using brute-force machine learning approaches that consume vast quantities of data and experience. While it is tempting to suppose that such a zombie-like robot would not be as adaptable to new circumstances as us, it is at least theoretically possible to program it to have a range of adaptability sufficient to handle any situation humans have yet faced. While such robot humans may not need art or entertainment (unless these turn out to play an important role in developing general-purpose adaptability), they would procreate and advance civilization as well or better than we would. The problem with these zombie scenarios is that while they are theoretically possible, they are most likely not feasible, and in any case are not the route life took. While I can’t prove that they are less feasible, my guess is that it takes a lot more low-level compute power to match what much less general-purpose high-level compute power can achieve, and I think the general-purpose solution converges on consciousness. In any case, it is not relevant as life chose the consciousness route. All earthly animals with centralized brains have features of consciousness strong enough to suggest that evolution strongly selects for minds. So the real question is what capabilities of consciousness have made it so successful.

The answer is that consciousness is function made animate through agency because agency comes with survival benefits that are useful in earthly evolution. The brain is capable of doing many things without consciousness. We can do many very familiar tasks while hardly thinking about them, and sleepwalkers can even raid the fridge with no conscious awareness. The value that consciousness brings to the table is the ability to weigh all the options available at the top level and to select one for the body to do next. It doesn’t simply employ a mindless prioritization algorithm as one might expect. Instead, the brain runs consciousness as a subprocess in the brain and this process “believes” that it is an autonomous agent in the world. This fiction, that the prioritization decisions can be “felt” by that agent through sensory feedback, effectively focuses all the body’s priorities into functional space: every input and output is no longer just data, but is interpreted from the perspective of this fictitious first-person actor. The concept of an actor or agent is purely a functional interpretation and has no meaning in the physical world. That we observe others acting purposefully in no way implies that they experience agency; my example above with zombie-like robots shows that they don’t need to perceive themselves as agents. So how can one objectively explain the experience of agency; what does it feel like? In other words, is it possible to objectively describe subjective experience? It is possible, provided one keeps in mind that explanations only characterize phenomena about something and can’t capture the noumenal quality of what it feels like to be a human (i.e. the map is not the terrain). That quality, analogous to a book or movie, is an ambiance of its own that is entirely the product of all the production qualities that went into creating it, so, like the book or movie, we can dissect it into many pieces to see how the “magic” is made. I will present the full explanation in a few chapters when I discuss consciousness in detail, but for now, I’d just like to make the point that the objective description will strictly talk about the function of each kind of subjective experience, not the special quality (e.g. redness) it seems to have to us personally. That special quality is not imaginary, in the sense that our subconscious tells us the quality is there, but it is imaginary in the sense that it only exists as information in the brain. The result is that things feel like their function, i.e. what they make possible. The net result is that everything feels very customized and special in its own right, even though that specialness actually derives from the function and not the stimulus or its quality (e.g. redness). Many of these functional distinctions are learned, “acquired tastes” which we come to appreciate, but most are innate, the product of millions of years of evolutionary pressures mapping function to feeling.

To give an example, as we survey an ordinary scene in front of us, we are calm and nothing stands out to our attention, even though we can distinguish any number of discrete objects in the scene. But if anything in that scene becomes bright, or flashing, or red, or fast-moving, or loud, etc., our pulse will quicken and our attention will immediately be drawn to it. Those stimuli have the function of warning; they are different from each other, but any of them can trigger the warning reaction and so in many ways feel the same to us. Red and yellow stand out more in any context than other colors because in our ancestral environment objects of these colors were more likely to warrant attention than green, blue, brown or gray objects. This doesn’t mean color alone alarms us, but it is a factor, and importantly, it affects how these colors feel to us. Blues and greens are calming, while reds, oranges, and yellows are a bit unnerving. It is not unpleasant; it is just part of the quality about them that we feel. If we could devise a set of glasses that could invert greens to reds and blues to yellows5, I believe that after a week or two we would come to invert them back, feeling red things like leaves as if they were green and green things like blood as if they were red. While this experiment has not yet been done, this result has been found with similar experiments that flip the image to the brain horizontally or vertically. I am not saying flipped or inverted qualia revert to being indistinguishable from before. No, superficially the quality remains inverted — people who view inverted scenes know they are inverted but can interact with them on that basis as if it were perfectly normal, and it doesn’t seem strange to them. Similarly, people would know that red things like leaves are being perceived as red, but they would trigger calmness and other feelings the same way that green things used to feel. Much of the feeling we get from colors and other qualia isn’t about their superficial distinctness but about our beliefs about their function. Our memory of how colors used to make us feel would matter more to us than the way they appeared now, and we would remap our feelings about the colors back to what they were before. The reason I believe the brain could do this color feeling inversion is that it does this sort of thing all the time; changes in lighting can make the same color appear quite different, yet our feeling about it remains the same. The brain is constantly trying to interpret inputs into functional buckets, correcting for variations in the signal. Ultimately, redness, brightness, loudness, etc., are about how information is hooked up in our minds, not about what is happening outside them, and the way it is hooked up is all about what how that information can help us, i.e. what its function is. In other words, the brain is functional and not literal, so it has many mechanisms to look past literal changes to the underlying function.

The perception that physics and chemistry are fundamentally more objective, provable, and definitive has led to them being called hard science, while social science, which is seen as more subjective and less provable and definitive is called soft science. Biology has both hard and soft aspects. The distinction really derives from our intuition that hard science is a fixed or closed system while soft science is not. A closed system can be modeled as perfectly as you like with a logical model that explains all its fixed components. A variable or open system includes feedback loops which continually impact and adjust the design and capabilities of the system itself. When an open system is implemented using a closed system, as the mind uses the body, there will be an underlying fixed physical explanation for what is happening particle-wise at any given instant, but the physical explanation will reveal nothing about the functional capacities of the system. Physically, information doesn’t exist; fluctuating signals traveling on wires or nerves exist, but divorced from any concept or purpose they have no relevance to anything. It only acquires relevance when an information management system gathers and uses information about something else, summarized and analyzed at practical levels of detail. Cells manage inherited information through genes, which summarize metabolic information, mostly about proteins, in a practical way. Minds manage information summarized from sensory inputs using both inherited (natural) and learned (artificial) mechanisms. Because cells and organs have a very fixed structure and behavior for any given species at any given point in its evolutionary history, the study of these structures from a physical standpoint can often be done with clean, logical models that explain all the fixed components. These models can often be experimentally verified to a high degree of confidence. Although we know such models of biological systems are inherently less fixed than those of nonbiological systems, they are quite comparable for most intents and purposes. They do posit a function for each kind of tissue, which is necessarily a subjective or soft determination, but the primary purpose of most tissues seems very clear, so while some appreciation for multifunctional tissues is lost in this kind of summation, it still works pretty well. But this approach mostly breaks down when studying the brain because its functionality is so highly integrated across many levels. We have identified primary functions for many parts of the brain, but we also have to accept that almost every function of the brain includes substantial integration across many areas. Not only do different brain areas and functions work together to achieve overall function, but they also incorporate feedback across multiple timelines. Instinct gathers feedback over millennia, long-term memory gathers it over a lifetime, and short-term memory gathers it for the scope of a problem at hand. And then there is the matter of the processing, or thinking, that we do with the collected information. While this bears considerably more discussion, for now it is sufficient to say thinking is quite open-ended and impossible to predict. So the distinction between the hard and soft sciences, or more accurately between the physical experimental sciences and the functional experimental sciences, is quite significant. However, it is misleading to characterize it as hard vs. soft; the distinction is really between fixed, closed systems and variable, open systems.

The History of the Philosophy of Science Viewed Functionally

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.

Minds not Brains: Introducing Theoretical Cognitive Science

I’m going to make a big deal about the difference between the mind and the brain. We know what minds are from long experience and take the concept for granted, despite an almost complete absence of a scientific explanation. Conventionally, the mind is “our ability to feel and reason through a first-person awareness of the world”. This definition begs the question of what “feel”, “reason” and “first-person awareness” might be, since we can’t just define the mind by using terms that are only meaningful to the owner of one. While we can safely say they are techniques that help the brain perform its primary function, which is to control the body, we will have to dig deeper to figure out how they work. Our experience of mind links it strongly to our bodies, and scientists have long said it resides in the nervous system and the brain in particular. Steven Pinker says that “The mind is what the brain does.”1 This is only superficially right, because it is not what, but why. It is not the mechanism or form of the mind that matters as much as its purpose or function. But how can we embark on the scientific study of the mind from the perspective of its function? As currently practiced, the natural sciences don’t see function as a thing itself, but more as a side effect of mechanical processes. The social sciences start with the assumption that the mind exists but take no steps to connect it back to the brain. Finally, the formal sciences study theoretical, abstract systems, including logic, mathematics, statistics, theoretical computer science, information theory, game theory, systems theory, decision theory, and theoretical linguistics, but leave it to natural and social scientists to apply them to natural phenomena like brains and minds. What is the best scientific standpoint to study the mind? Cognitive science was created in 1971 to fill this gap, which it does by encouraging collaboration between the sciences. I think we need to go beyond collaboration and admit that the existing three branches have practical and metaphysical constraints that limit their reach into the study of the mind. We need to lift these constraints and develop a unified and expanded scientific framework that can cleanly address both mental and physical phenomena.

Viewed most abstractly, science divides into two branches, the formal and experimental sciences, with the formal being entirely theoretical, and the experimental being a collaboration between theory and testing. Experimental science further divides into fundamental physics, which studies irreducible fields and/or particles, and special sciences (all other natural and social sciences), which are presumed to be reducible to fundamental physics, at least in principle. 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. Hypotheses are purely functional while testing is purely physical. That is, hypotheses are ideas with no physical existence, though we think about and discuss them through physical means, while testing tries to evaluate the physical world as directly as possible. Of course, we use theory to perform and interpret the tests, so it can’t escape some dependence on function. The scientific method tacitly acknowledges and leverages both functional and physical existence, even though it does not overtly explain what functional existence might be or attempt to explain how the mind works. That’s fine — science works — but we can no longer take functional existence and its implications for granted as we start to study the mind. It’s remarkable, really, that all scientific understanding, and everything we do for that matter, depend critically on our ability to use our minds, yet don’t need an understanding of how it works or what it is doing. But we have to find a way to make minds and ideas into objects of study themselves to understand what they are.

The special sciences are broken down further into the natural and social sciences. The natural sciences include everything in nature except minds, and the social sciences study minds and their implications. The social sciences start with the assumption that people, and hence their minds, exist. They draw on our perspectives about ourselves, our behavior patterns, and what we think we are doing to explain what we are and help us manage our lives better. Natural scientists (aka hard scientists) call the social sciences “soft sciences” because they are not based on physical processes bound by mathematical laws of nature; nothing about minds has so far yielded that kind of precision. Our only direct knowledge of the mind is our subjective viewpoint, and our only indirect knowledge comes from behavioral studies, evolutionary psychology and outright speculation into the functions our minds appear to perform. The study of behavior finds patterns in the ways brains make bodies behave and may support the idea of mental states but doesn’t prove they exist. Evolutionary psychology also suggests how mental states could explain behavior, but can’t prove they exist. Studying the functions the mind does by just guessing about them sounds crazy at first, but is actually the way all scientific hypotheses are formed: take a guess and see if it holds up. It too can’t prove mental states exist, but we need to remember that science isn’t about proving, it is about developing useful explanations.

The differences in approach between hard and soft sciences have opened up a gap that currently can’t be bridged, but we have to bridge it to develop a complete explanation of the mind. This schism between our subjective and objective viewpoints is sometimes called the explanatory gap. The gap is that we don’t know how physical properties alone could cause a subjective perspective (and its associated feelings) to arise. I closed this gap in The Mind Matters, but not rigorously. In brief, I said that the mind is a process in the brain that experiences things the way it does because creating a process that behaves like an agent and sees itself as an agent is the most effective way to get the job done. More to the point, it feels like an agent because it has to have some way of thinking about its senses and that way needs to keep them all distinct from each other. So perceptions are just the way our brains process information and “present” it to the process of mind. It is not a side effect; much of the wiring of the brain was designed to make this illusion happen exactly the way it does.

Natural science currently operates on the assumption that natural phenomena can be readily modeled by hypotheses which can be tested in a reproducible way. This works well enough for simple systems, i.e. those which can be modeled using a handful of components and rules. The mind, however, is not a simple system for three reasons: complexity, function, and control. Living tissues are complex systems with many interacting components, so while muscle tissue can be modeled as a set of fibers working together as a simple machine, like any complex system its behavior will become chaotic outside normal operating parameters. Next, the mind (and muscles) have a different metaphysical nature than nonliving things. Unlike rocks and streams, muscles and nerves are organized to perform a function rather than employ a specific physical form. And most significantly, the mind is not organized to perform functions itself but to control how the body will perform functions, and so could be called metafunctional. These three complicating factors make developing and testing hypotheses about the mind vastly more complicated than doing it for rocks and streams, so paradigms based only on natural laws won’t work. Yet the attitude among natural scientists is that the mind is just an elaborate cuckoo clock and so understanding it reduces to knowing its brain chemistry. That will indeed reveal the physical mechanisms, but it won’t reveal the reasons for the design, any more than understanding the clock explains why we want to know what time it is. When we study complex systems, like the weather, we have to accept that chaos and unpredictability are around every corner. When we study functional systems, like living things, we have to accept that functional explanations — and all explanations are functional — need to acknowledge the existence of function. And when we study control systems, like brains and minds, we have to accept that direct cause and effect is supplanted by indirect cause and effect through information processing. Natural sciences study complexity and function in living systems, but not the control aspect of minds. Control is addressed by a number of the formal sciences, but since the formal sciences are not concerned with natural phenomena like minds, the study of control by minds has been left high and dry. It falls under the purview of cognitive science, but we need to completely revamp our concept of what scientific method is appropriate to study function and control. We will need theories that seek to explain how control is managed from a functional perspective, that is, using information processing, and we will need ways to test them that are less direct than tests of natural laws.

Nearly all our knowledge of our mind comes from using it, not understanding it. We are experts at using our minds. Our facility develops naturally and is helped along by nurture. Then we spend decades at schools to further develop our ability to use our mind. But despite all this attention on using it, we think little about what it is and how it works. Just as we don’t need to understand how any machine works to use it, we don’t need to know how our mind works to use it. And we can no more intuit how it works that we can intuit how a car or TV works. We consequently take it for granted and even develop a blindness about the subject because of its irrelevance. But it is the relevant subject here, so we have to overcome this innate bias. We can’t paint a picture of a scene we won’t look at. While we have no natural understanding of it, we do know it is a construct of information managed by the brain. Understanding the physical mechanisms of the brain won’t explain the mind any more than taking a TV apart would explain TV shows, because for both the mind and TV shows the hardware is just a starting point from which information management constructs highly complex products. So the mind is less what the brain does than why it does it. It is about how it physically accomplishes things so much as what it is trying to accomplish. This is the non-physical, functional existence I have argued for. In fact, for us, functional existence is primary to physical existence, because knowledge itself is information or function, so we only know of physical existence as mediated through functional existence, i.e. from observations we make with our minds (i.e. “I think therefore I am”).

Knowing that functional existence is real and being able to talk about it still doesn’t explain how it works. We take understanding to be axiomatic. We use words to explain it, but they are words defined in terms of each other without any underlying explanation. For example, to understand is to know the meaning of something, to know is to have information about, information is facts or what a representation conveys, facts are things that are known, convey is to make something known to someone, meaning is a worthwhile quality or purpose, purpose is a reason for doing something, reason is a cause for an event, and cause is to induce, give rise, bring about, or make happen. If anything, causality seems like it should reduce to something physical and not mental, yet it doesn’t. But the language of the mind is not intended to explain how understanding or the mind works, just to let us use understanding and our minds. If we are to explain how understanding and other mental processes work we will need to develop an objective frame of reference that can break mental states down into causes and effects or we will remain trapped in a relativistic bubble.

Let’s consider which sciences study the mind directly. Neuroscience studies the brain and nervous system, but this is not direct for the same reason studying computer hardware says little or nothing about what computer software does. On the other hand, psychology and cognitive science are dedicated to the study of the mind. Psychology studies the mind as we perceive it, our experience of mind, while cognitive science studies how it works. One could say psychology studies the subjective side and cognitive science studies the objective side. Psychology divides into a variety of subdisciplines, including neuropsychology, behavioral psychology, evolutionary psychology, cognitive psychology, psychoanalysis, and humanistic psychology. They each draw on a different objective source of information. Neuropsychology studies the brain for effects on behavior and cognition. Behavioral psychology studies behavior. Evolutionary psychology studies the impact of evolution. Cognitive psychology studies mental processes like perception, attention, reasoning, thinking, problem-solving, memory, learning, language, and emotion. Psychoanalysis studies experience (but with a medical goal). Humanistic psychology studies uniquely human issues, such as free will, personal growth, self-actualization, self-identity, death, aloneness, freedom, and meaning. Cognitive science focuses on the processes that support and create the mind. Most cognitive scientists, including me, are functionalists, maintaining that the mind should be explained in terms of what it does. But science continues to be almost completely dominated by a physicalist tradition, which suggests and even claims that studying the brain will ultimately explain the mind. I have adamantly argued that function does not reduce to form, even though it needs form. And it is true that knowing the form provides many clues to the function, and it is also true that form is our only hard evidence. But we are still a long way from unraveling all the mechanics of neurochemistry, though rapid progress is being made. In the meantime, without any more information than we already have at hand there is much that we can say about the brain’s function, that is, about the mind, by taking a functional perspective on what it is doing. So cognitive science should not be an interdisciplinary collaboration, but should reboot science from scratch by establishing a scientific approach to studying function that can meet a comparable level of objectivity as our paradigm for studying form. I have, so far, proposed that all of science be refounded on the ontology of form and function dualism. The prevailing paradigm, which derives as I have noted from the Deductive Nomological Model, uses function to study form, while I propose to use function to study both form and function.

One other discipline formally studies the mind: philosophy. Practiced as an independent field, general philosophy studies fundamental questions, such as the nature of knowledge, reality, and existence. But because they don’t establish an objective basis for their claims, philosophers ultimately depend on the subjective, intuitive appeal of their perspectives. For example, universality is the notion that universal facts can be discovered and is therefore understood as being in opposition to relativism. Universality and relativism assume the concepts of facts, discovery, understanding, and perception, but these assumptions are at best loosely defined and really depend on a common knowledge of what they are. Philosophy builds on common knowledge ideas without attempting to establish an objective basis. What principally distinguishes science is the effort to establish objectivity, and the way it does this it itself studied unscientifically as the philosophy of science. It is an ironic situation that the solid foundation upon which science has presumably been built is itself unclear and ultimately pretty subjective. George Bernard Shaw said, “Those who can do, those who can’t teach,” and this is a theme I have been repeating. We are designed to do things but not to understand how we do them or, much less, to teach how they are done. But understanding and teaching are important to get us to the next level so we can leverage what we know in new ways. We have long been perfectly capable of practicing science without dwelling too much on its philosophical basis, but that was before we started to study the mind. We desperately need an objective basis of objectivity itself and how to apply it to the study of both form and function in order to proceed. Philosophers have asked the questions and laid out the issues, but scientists now have to step up and answer them.

Philosophy of science and philosophy of mind have detailed the issues at hand from a number of directions, and characteristically of philosophy have failed to indicate an objective path forward. I believe we can derive the objective philosophy we need by reasoning it out from scratch using the common and scientific knowledge of which we are most confident, which I will do in the next chapter. But a brief summary of the fields is a good starting point to provide some orientation. Science was a well-established practice long before efforts were made to describe its philosophy. August Comte proposed in 1848 that science proceeds through three stages, the theological, the metaphysical, and the positive. The theological stage is prescientific and cites supernatural causes. In the metaphysical 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 and embrace an ever-progressing refinement of facts based on empirical observations. So every theory must be guided by observed facts, which in turn can only be observed under the guidance of some theory. Thus arises the hypothesis-testing loop of the scientific method and the widely accepted view that science continually refines our knowledge of nature. Comte’s third stage developed further in the 1920’s into logical positivism, the theory that only knowledge verified empirically (by observation) was meaningful. More specifically, logical positivism says that the meaning of logically defined symbols could mirror or capture the lawful relationship between an effect and its cause2. Every term or symbol in a theory must correspond to an observed phenomena, which then provides a rigorous way to describe nature mathematically. It was a bold assertion because it says that science derives the actual laws of nature, even though we know any given evidence can be used to support any number of theories, even if the simplest theory (i.e. by Occam’s razor) seems more compelling. In the middle of the 20th century, cracks began to appear in logical positivism (and its apotheosis in the DN Model, see above) as the sense of certainty promised by modernism began to be replaced by a postmodern feeling of uncertainty and continuous change. In the sciences, Thomas Kuhn published The Structure of Scientific Revolutions in 1962, which is remembered popularly for introducing the idea of paradigm shifts (though Kuhn did not coin that phrase specifically). Though Kuhn’s goal was to help science by unmasking the forces behind scientific revolutions, he inadvertently opened a door he couldn’t shut, forever ending dreams of absolutism and a complete understanding of nature and replacing it with a relativism in which potentially all truth is socially constructed. In the 1990s, postmodernists claimed all of science was a social construction in the so-called science wars. Because this seems to be true in many ways, science formally lost this battle against relativism and has continued full steam without clarifying its philosophical foundations. Again, while this is good enough to do science that studies form, it is not enough to do science that studies function. Arguably, we could and very well might develop a scientific tradition for studying function that lets us get the job done without a firm philosophical foundation either. After all, we need news you can use regardless of why it works. Maybe it will happen that way, but I personally consider the why to be the more interesting question, and because function is so much more self-referential than form that I think studying it will turn out to require understanding what it means to study it.

The philosophy of mind is studied as a survey of topics including existence (the mind-body problem), theories of mental phenomena, consciousness/qualia/self/will, and thoughts/concepts/meaning. My goal, as noted, is to establish an objectively supportable stance on these topics and on objectivity itself, which I will then use to launch an investigation into the workings of the mind. It will take some time to do all this, but as a preview I will lay out where I will land on some fundamental questions:

I endorse physicalism (i.e. minimal or supervenience physicalism), which says the mind has a physical basis, or, as philosophers sometimes say, the mental supervenes on the physical. This means that a physical duplicate of the world would also duplicate our minds. While true duplication is impossible, my point here is just that the mind draws its power entirely from physical materials. Physicalism rejects the idea of an immortal soul and Descartes’ substance dualism in which mind and body are distinct substances. Physicalism is often taken to simultaneously reject any other kind of existence, making it a physical monism, but that rejection is unnecessary. At its core physicalism just says that physical things are physical. That one might also interpret something physical from another perspective is irrelevant to physicalism.

I endorse non-reductive physicalism, which is just a fancy way of saying that things that are not physical are not physical, and in particular, that function is not form or reducible to it. More accurately, mental explanations cannot be reduced solely to physical explanations. That doesn’t mean that physical things like brains, that can carry out functions, are not physical, because they are entirely physical from a physical perspective. But if you look at brains from the perspective of what they are doing you create an auxiliary kind of explanation, a functional one. And because explanatory perspectives are abstract, there are an unlimited number of functional perspectives (or existences) about everything. The brain is still physical, the explanations of it are not. To the extent the word “mind” is taken to be a functional perspective of what the brain is doing, it is really the union of all the explanatory perspectives the brain uses when going about its business. These functional perspectives are not mystical, they are relational, tying information to other information using math, logic or correlation. A given thought has a form as an absolute, physical particular in a brain, but its meaning is relative, being a generalization or idealization that might refer to any number of things. Thus, “three” and “above” are not physical particulars. A thought is a functional tool that may be employed in a specific physical example but exists as an abstraction independent of the physical.

I endorse functionalism, which is the theory that mental states are more profitably viewed from the perspective of what they do rather than what they are made of, that is, in terms of their function, not their form. In my ontology of form & function dualism mental states have both kinds of existence, with many possible takes as to what their function is, but they evolved to satisfy the control function for the body, and so our efforts to understand them should take this perspective first.

I endorse the idea that consciousness is a subprocess of the brain that is designed to create a subjective theater from which centralized control of the body by the brain can be performed efficiently. All the familiar aspects of consciousness such as qualia, self, and the will are just states managed by this subprocess. As a special spoiler, I will reveal that I endorse free will, even if the universe is deterministic, which to the best of our knowledge it is not.

Finally, I endorse the idea that thoughts, concepts, and meaning are information management techniques that have both conscious and subconscious aspects, where subconscious refers to subprocesses of the brain that are supportive of consciousness, which is the most supervisory subprocess.

While this says much about where I am going, it doesn’t say how how I will get there or how a properly unified philosophy of science and mind imply these things.

Deriving an Appropriate Scientific Perspective for Studying the Mind

I have made the case for developing a unified and expanded scientific framework that can cleanly address both mental and physical phenomena. I am going to focus first on deriving an appropriate scientific perspective for studying the mind, which also bears on science at large. I will follow these five steps:

1. The common knowledge perspective of how the mind works
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. The common-knowledge perspective of how the mind works

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. While our sensory qualia inform us of physical properties (form), our emotional qualia inform us of mental properties (function). Fear, desire, love, revulsion, etc., feel as real to us as sight and sound, though mature humans also 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 call this awareness of our brains “encephaloception,” a subset of proprioception (our sense of where the parts of our body are), but also including other somatosenses like pain, touch, pressure. The main reason our encephaloception pinpoints our thoughts in our heads is that senses work best when they provide consistent and accurate information, and the truth is we are thinking with our brains. Like other internal organs, it helps us to be aware of pain, motion, impact, balance, etc. on the head and brain as this can affect our ability to think, so having sufficient sensory awareness of our brain just makes sense. It is not just a side effect, say, of having vision or hearing in the head that we assume our thoughts originate there; it is the consistent integration of all the sensory information we have available.

But what is thinking? Loosely speaking it is the union of everything we feel happening in our heads, but more specifically we think of it as a continuous train of thought which connects what is happening in our minds from moment to moment in a purposeful way. This can happen through a variety of modalities, but the primary one is the simulation of current events. As our bodies participate in events, the mind simultaneously simulates those events to create an internal “movie” that represents them as well as we understand them. We accept that our understanding is limited to our experience and so tends to focus on levels of detail and salient features that have been relevant to us in the past. The other modalities arise from emphasizing the use of specific qualia and/or learned skills. Painting and sculpting emphasize vision and pattern/object recognition, music emphasizes hearing and musical pattern recognition, and communication usually emphasizes language. Trains of thought using these modalities feel different from our default “movie” modality but have in common that our mind is stepping through time trying connecting the dots so things “make sense.” Making sense is all about achieving pleasing patterns and our conscious role in spotting them.

And even above our ability to think, we consciously identify with our ability to control our bodies and, indirectly through them, the world. Though much of our talent for thought is innate, we believe the most important part is learned, the result of years of experience in the school of hard knocks. We believe in our free will to take what our senses, emotions, and memory can offer us to select the actions that will serve us best. At every waking moment, we are consciously considering and choosing our upcoming actions. Sometimes those actions are moments away, sometimes years. Once we have selected a course of action, we will, as much as possible, execute it on “autopilot,” which is to say we leverage conditioned behavior to reduce the burden on our conscious mind by letting our subconscious handle it. So we recognize that we have a conscious mind that is just that part that is actively considering our qualia and memories to select next actions and a subconscious mind that is processing our qualia and memories and performing a variety of control functions that don’t require conscious control. All of this is common knowledge from common sense, and it is also well-established scientifically.

But what is thinking? What does it mean to consider and decide? Thinking seems like such an ineffable process, but we know a lot about it from common knowledge. We know that concepts are critical building blocks of thought, and we know that concepts are generalizations gleaned from grouping similar experiences together into a unit. Language itself functions by using words to invoke concepts. We each make strong associations between each word we know and a variety of concepts that word has been used to represent. Our ability to use language to communicate hinges on the idea that the same word will trigger very similar concepts in other people. Our concepts are all connected to each other through a web of relationships which reveal how the concepts will affect each other under different circumstances. This web thus reveals the function of the concept and constitutes its meaning, so its meaning and hence its existence is entirely functional and not physical. Its neural physical manifestation is only indirectly related and hence incidental, as the meaning could in principle be realized in different people or by another intelligent being or even just written down. Although every physical brain contemplating any given concept will have some subtle and deep differences in their understanding of it, because the concept is fundamentally a generalization, subtle and deep characteristics are necessarily of less significance than the overall thrust.

The crux of thinking, though, is what we do with concepts: we reason with them. Basically, reasoning means carefully laying out a set of related concepts and the relevant relationships that bind them and drawing logical implications. To be useful, the concepts and implications have to be correlated to a situation for which one wants to develop a purposeful strategy. In other words, when we face a situation we don’t know how to handle it creates a problem we have to solve. We try to identify the most relevant factors of the problem by correlating the situation to all the solutions we have reasoned out in the past, which lets us narrow it down to a few key concepts and relationships. To reason, we consider just these concepts and our rules about them in a kind of cartoon of reality, and then we hope that conclusions we drew about these generalized concepts will apply to the real situation we are addressing. In practice, it usually works so well that we think of our concepts as being identical to the things they represent, even though they are really just loose descriptive generalizations that are nothing like what they represent and, in fact, only capture a small slice of abstract functional properties about those things. But they tend to be exactly what we need to know. “Thinking outside the box” refers to the idea of contemplating uses for concepts beyond the ones most familiar to us. An infinite variety of possible alternate uses for any thing or concept always exists, and it is a good idea to consider some of them when a problem arises, but most of the time we can solve most problems well enough by just recombining our familiar concepts in familiar ways.

This much has arguably been common knowledge for thousands of years, even if not articulated as such, and so can arguably even be subsumed under the more heading common sense, which includes everything intuitively obvious to normal people 2. But can civilization and culture be said to have generated trustworthy common knowledge that goes beyond what we can intuit for ourselves using common sense just by growing up? I am not referring to the common knowledge of details, e.g. historical facts, but to the common knowledge of generalities, i.e. the way things work. Here I would divide such generalities into two camps, those that have scientific support and hence can be clearly explained and demonstrated and those that don’t, but which still have broad enough acceptance to be considered common knowledge. I will consider these two camps in turn.

Our scientific common knowledge expands dramatically with each generation. We take much for granted today from physics, chemistry, and biology that were unknown a few hundred years ago. Even if we are weak in the details, we are all familiar with the scope of physical and chemical discoveries from artifacts we use every day. We know evolution is the prime mover in evolution, causally linking biological traits to the benefits they provide. Relative to the mind specifically, we have familiarity with discoveries from neuroscience, computer science, psychology, sociology and more that expand our insight into what the brain is up to. Although we recognize there is still much more unknown than known, we are pretty confident about a number of things. We know the mind is produced by the brain and not an ethereal force independent of the brain or body. This is scientific knowledge, as thoroughly proven from innumerable scientific experiments as gravity or evolution, and is accepted as common knowledge by those who recognize science’s capacity to increase our predictive power over of the world. Those who reject science or who employ unscientific methods should read no further as I believe the alternatives are smoke and mirrors and should not be trusted as the basis for guiding decisions.

Beyond being powered by the brain, we also now know from common knowledge that the mind traffics solely in information. We don’t need to have any idea how it manages it to see that everything that is happening in our subjective sphere is relational, just a big description of things in terms of other things. It is a large pool of information that we gather in real time and integrate both with information we have stored from a lifetime of experience and collected as instinctive intuitions from millions of years of evolution. The advent of computers has given us a more general conception of information than our parents and grandparents had. We know it can all be encoded as 0’s and 1’s, and we have now seen so many kinds of information encoded digitally that we have a common-knowledge intuition about information that didn’t exist 30 to 60 years ago.

It is also common knowledge that there is something about understanding the brain and/or mind that makes it a hard problem. While everything else in the known universe can be explained with well-defined (if not perfectly fleshed-out) laws of physics and chemistry, biology has introduced incredible complexity. How has it accomplished that and how can we understand it? The ability of living things to use feedback from natural selection, i.e. evolution, is the first piece of the puzzle. Complexity can be managed over countless generations to develop traits that exploit almost any energy source to support life better. But although this can create some very complex and interdependent systems, we have been pretty successful in breaking them down into genetic traits with pros and cons. We basically understand plants, for example, which don’t have brains per se. The control systems of plants are less complex than animal brains, but there is much we still don’t understand, including how they communicate with each other through mycorrhizal networks to manage the health of whole forests. But while we know the role brains serve and how they are wired to do it with neurons, we have only a vague idea how the neurons do it. We know that even a complete understanding of how the one hundred or so neurotransmitters activate isn’t going to explain it.

We know now from common knowledge that we have to confront head-on the question of what brains are doing with information to tackle the problem. And the elephant in the room is that science doesn’t recognize the existence of information. There are protons and photons, but no informatons or cogitons. What the brain is up to is still viewed strictly through a physical lens as a process reducible to particles and waves. This has always run counter to our intuitions about the mind, and now that we understand information it runs counter to our common-knowledge understanding of what the mind is really doing. So we have a gap between the tools and methods science brings to the table and the problem that needs to be solved. The solution is not to introduce informatons and cogitons to the physical bestiary, but to see information and thought in a way that makes them explainable as phenomena.

So when we think to ourselves that we “know what we know” and that it is not just reducible to neural impulses, we are on to something. That knowledge can be related verbally and so “jump” between people is proof that it is fundamentally nonphysical, although we need a physical brain to reflect on it. All ideas are abstractions that indirectly characterize real or imagined things. Our minds themselves, using the physical mechanisms of the brain, are organized and oriented so as to leverage the power this abstraction brings. We know all this — better today than ever before — but we find ourselves stymied to address the matter scientifically because abstraction has no scientific pedigree. But I am not going to ignore common sense and common knowledge, as science is wont to do, as I unravel this problem.

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-hard natural scientists insist it is and must be, and that anything else is new-age nonsense. I am sympathetic to that view as mysticism is not explanatory and consequently has no place in discussions about explanations. And we can certainly agree from common knowledge that there is a physical aspect, being the body of each person and the world around us. But knowing that seems to give us little ability to explain our subjective experience, which is so much more complex than the observed physical properties of the brain would seem to suggest. Can we extend science’s reach with another kind of existence that is not 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 have provided overwhelming evidence of a persistent physical reality that doesn’t fluctuate in accord with our imagination, and this makes idealism rather untenable. But if we join the two together we can imagine a dualism between mind and matter in which both the mental and physical exist without either being reducible to the other. All religions have seized on this idea, stipulating a soul (or equivalent) that is quite distinct from the body. But no scientific evidence has been found supporting the idea that the mind can physically exist independent of the body or is in any way supernatural. But if we can extend science beyond physicalism, we might find a natural basis for the mind that could lift religion out of this metaphysical quicksand. Descartes also promoted dualism, but he got into trouble identifying the mechanism: he supposed 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.

If the brain just operates under the normal rules of spacetime, as the evidence suggests, 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 it is precisely the point that this relationship is not direct. It is like saying software is a non-physical property of hardware; while software runs on hardware, the hardware reveals nothing about what the software is meant to do.

Finally, 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,3 “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 events featuring two baseball teams. Some parts 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. “Apple” and “water” are (seemingly) physical predicates while “three”, “red” and “happy” are not. 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. Apple and water are also abstractions; apples are fruits from certain varieties of trees and water is the liquid state of H2O, but is usually used generically and not to refer to a specific portion of water.4 Any physical example of apple or water will fall short of any ideal definition in some ways, but this doesn’t matter because function is never the same as form; it is intentionally an abstract characterization.

I prefer form and function dualism to predicate dualism because it is both clearer and more technically correct. It is clearer because it names both kinds of things that exist. It is more correct because function is bigger than predicates. I divide function into active and passive forms. Active function uses reference, logical reasoning, and intelligence. The word “predicate” emphasizes a subject, being something that refers to something else, either specifically (definite “the”) or generally (indefinite “a”) through the ascription of certain qualities. Predicates are the subjects (and objects) of logical reasoning. Passive function, which is employed by evolution, instinct, and conditioned responses, uses mechanisms and behaviors that were previously established to be effective in similar situations. Evolution established that fins, legs, and wings could be useful for locomotion. Animals don’t need to know the details so long as they work, but the selection pressures are on the function, not the form. 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, are executed passively (on autopilot) without active use of predicates or reasoning. Function can only be achieved in physical systems by identifying and applying information, which as I have previously noted is the basic unit of function. Life is the only kind of physical system that has developed positive feedback mechanisms capable of capturing and using information. These mechanisms evolved because they enable life to do things more competitively than it could otherwise do because predicting the future beats blind guessing. Evolution captures information using genes, which apply it either directly through gene expression (to regulate or code proteins) or indirectly through instinct (to influence the mind). Minds capture information using memory, which is a partially understood neural process, and then applies it through recall or recognition, which subconsciously identify appropriate memories through triggering features. But if information is captured using physical genes or neurons, what trick makes it nonphysical? That is the power of abstraction: it allows stored patterns to be as indefinite generalities to be correlated later to new situations to provide a predictive edge. Information is created actively by using concepts to represent general situations and passively via pattern matching. Genes create proteins that do chemical pattern matching, while instinct and conditioned response leverage subconscious neural pattern matching.

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 have both form and function, but to the extent we are discussing the function we can ignore or set aside considerations of the form because it just provides a means to an end. Function has no extent but is instead measured in terms of its predictive power. Pattern-matching techniques and algorithms implement functionality passively through brute force, while reasoning creates information actively by laying out concepts and rules that connect them. In a physical world, form makes function possible, so they coexist, but form and function can’t be reduced to each other. This is why I show them in the diagram as independent dimensions that intersect but generally do their own thing. Technically, function emerges from form, meaning that interactions of forms cause function to “spring” into existence with new properties not present in forms. But it has nothing to do with magic; it is just a consequence of abstraction decoupling information from what it refers to. The information systems 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. However, as physical creatures, our access to function and the ideal realm is limited by the physical mechanisms our brains use to implement abstraction. We could, in principle, build a better mind, or perhaps a computer, that can do more, but any physical system will always be physically constrained and so limit our access to the infinite domain of possible ideas. Idealism is the reigning ontology across this hypothetical space of ideas, but it can’t stand alone in our physical space. And though we can’t think all ideas, we can potentially steer our thoughts in any direction, so given enough time we can potentially conceive anything.

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 said5. 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 reliability6. 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.7. 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.”89. 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 10.

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.11. 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 Revolutions12, 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!”13. 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.”1415. 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 talents16. 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). 17

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.

Key insights of my theory

The key insights as I see them:

1. Descartes was right about dualism
2. We underappreciate the impact of evolution on the mind
3. We underappreciate the computational nature of the mind.
4. Consciousness exists to facilitate reasoning.
5. Consciousness is a simplified, “cartoon”-like version of reality with its own feel
6. Minds reason using models that represent possibilities
7. Reasoning is fundamentally an objective process that manages truth and knowledge
8. We really have free will

Insight 1. Descartes was right about dualism – mind and body are separate kinds of substances. He made the false assumption that the mind is a physical substance, but then, he had no scientific basis for distinguishing mental from physical. We do have a basis now, but no one, so far as I can tell, has pointed it out as such. I will do so now. Mind and body, or, as I will refer to them, the mental (or ideal) and physical, are not separate in the sense of being different physical substances, but in the sense of being different independent kinds of existence that don’t preclude each other, but can affect each other. The brain and everything it does has a physical aspect, but some of the things it does, e.g. relationships and ideas, have an ideal, or mental, aspect as well. The mechanics of how a brain thinks is physical, but the ideas it thinks, viewed abstractly, are mental. You could say the idea that 1+1=2 exists regardless of whether any brain thinks about it. So our experience of mind is physical, but to the degree that our minds use relationships and ideas as part of that experience (using a physical representation), those relationships and ideas are also mental (in that they have an abstract meaning). The brain leverages mental relationships analogously to the way life leverages chemicals that have different physical properties, except that mental relationships have no physical properties like chemicals but instead impact the physical world through feedback and information processing as a series of physical events. As with chemicals, the net effect is that the complexity of the physical world increases.

Only abstract relationships count as mental, where “abstract” refers to the idea of indirect reference, which is a technique of using one thing to represent or refer to another. A physical system, like a brain or a computer, that implements such techniques has all sorts of physical limitations on the scope and power of those representations, but, like a Turing machine, any implementation capable of performing logical operations on arbitrary abstract relationships can in principle compute anything in the ideal world. In other words, there are no “mysterious” ideas beyond our comprehension, though some will exceed our practical capacity. The confusion between physical and mental that has dogged philosophy and science for centuries only continues because we have not been clearly differentiating the brain from what it does. The brain implements a biological computer physically, but what it does is represent relationships as ideas. Ideas are not dependent on the implementation so that an idea can be represented with words and shared by author and reader. The three forms are very different, but we know that they share important aspects.

All abstract relationships exist (ideally) whether any brain (or computer) thinks about them or not. So the imaginary world is a much broader space than the physical, if you will, as it essentially parameterizes possibility – thoughts are not locked down in all their specifics but generalize to a range of possibilities. Consider a computer program, which is a simple system that manipulates abstract relations. A program executing on a computer will go through a very real set of instructions and process specific data from inputs to outputs. But a program’s capability can be nearly infinite if it is capable of handling many kinds of inputs across a whole range of situations. The program “itself” doesn’t know this, but the programmer does. Our minds work like the programmer; they manage an immense range of possibilities. We see these possibilities in general terms, then add specifics (provide inputs) to make them more concrete, and ultimately a few of them are realized in the real world (i.e. match up to things there). In a very real sense, we live our lives principally in this world of possibilities and only secondarily in the physical world. I’m not speaking fancifully about daydreaming about dragons and unicorns, though we can do that, but about whether the mail has come yet or rain is likely. Whatever actually happens to us immediately becomes the past and doesn’t matter anymore, except in regards to how it will help us in the future. Of course, knowing that we have gotten the mail or that it is rained matters a lot to how we plan for the future, so we have to track the past to manage the future. But nostalgia for its own sake matters little, and so it is no big surprise that our memory of past events dissipates rather quickly (on the theory that our memory has evolved to intentionally “forget” information that could do more to distract effective decision making that help it). My point, though, is that we continually imagine possibilities.

Insight 2. We underappreciate the impact of evolution on the mind. Darwin certainly tried to address this. “How does consciousness commence?” Darwin wondered. It was, and is, a hard question to answer because we still lack any objective means of studying what the mind does (as the mind is only visible from within, subjectively). 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 talents. And Piaget extended the list of innate cognitive skills by developing his staged theory of intellectual development. And we now know that thinking is much more than a conditioned behavior but employs reasoning and subconscious know-how. But evolution tells us more still. The mind is the control center of the body, so the direct feedback from evolution is more on how the mind handles a situation than the parts of the body it used to do it. The body is a multipurpose tool to help the mind satisfy its objectives. Mental evolution, therefore, leads somatic evolution. However, since we don’t understand the mechanics of the mind we have done less to study the mind than the body, which is just more tractable. Although understanding the full mechanics of mind is still a long way off, by looking at what selection pressures created demand for what kinds of cognitive skills evolutionary psychologists can explain them. Those explanations involve the evolution of both specialized and general purpose software and hardware in the brain, with consciousness itself being the ultimate general purpose coordinator of action.

Insight 3. We underappreciate the computational nature of the mind. As I noted in The Certainty Engine, what the mind does is computational, if computation is taken to be any information management process. But 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 broadly 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 perceive many kinds of things at the same time without difficulty.

Insight 4. Consciousness exists to facilitate reasoning. Consciousness exists because we continually encounter situations beyond the range of our learned responses, and being able to reason out effective strategies works much better than not being able to. We can do a lot on “autopilot” through habit and learned behavior, but it is too limited to get us through the day. Most significantly, our overall top-level plan has to involve prioritizing many activities over short and long time frames, which learned behavior alone can’t do. Logic, inductive or deductive, can do it, but only if we come up with a way to interpret the world in terms of propositions composed of symbols. This is where a simplified, cartoon-like version of reality comes into play. To reason, we must separate relevant from irrelevant information, and then focus on the relevant to draw logical conclusions. So we reduce the flood of sensory inputs continually entering our brains into a set of discrete objects we can represent as symbols we can use in logical formulas (here I don’t mean shaped symbols but referential concepts we can keep in mind). The idea that hypothetical internal cognitive symbols represent external reality is called the Representational Theory of Mind (RTM), and in my view is the critical simplification employed by reasoning, but it is not critical to much of subconscious processing, which does not have this need to simplify. Although we can generalize to kinds using logical buckets like bird or robin, we can also track all experience and draw statistical inferences without any attempt at representation at all, yielding bird-like or robin-like without any actual categories.

Do we reason using language? Are these symbols words and are the formulas sentences? There is a debate about whether the mind reasons directly in our natural language (e.g. English) or an internal language, sometimes called “mentalese”. Both are partially right but mostly wrong; the confusion comes from failing to appreciate the difference between the conscious and subconscious minds. Language is part of the simplified world of consciousness that tries to turn a gray world into something more black and white that reason can attack (while not incidentally aiding communication). From the conscious side, language-assisted reasoning is done entirely in natural language. We are also capable of reasoning without language, and much of the time we do, but language is not just an add-on capability, it is what pushes human reasoning power into high gear. Animals have had solid reasoning skills (and consequently consciousness) for hundreds of millions of years, so that they could apply cause and effect in ways that matter to them, creating a subjective version of the laws of nature and the jungle. But without language animals can only follow simple chains of reasoning. Language, which evolved in just a few million years, lengthens those chains and adds nesting of concepts. It gives us the ability to reason in a directed way over an arbitrarily abstract terrain. Without inner speech, the familiar internal monolog of our native tongue, we can’t ponder or scheme, we can only manage simple tasks. Sure, we can keep schemes in our heads without further use of language, but language so greatly facilitates rearranging ideas that we can’t develop abstract ideas very far without it. Helen Keller could remember her languageless existence and claimed to be a non-thinking entity during that time. By “thinking” I believe she only meant directed abstract reasoning and all the higher categories of thoughts that brings to mind.

I cannot hope to describe adequately that unconscious, yet conscious time of nothingness. I did not know that I knew aught, or that I lived or acted or desired. I had neither will nor intellect.1

She admits to consciousness, which I argue exists because animals need to reason, yet also felt unconsciousness, in that large parts of her mind were absent, notably intellect (directed abstract reasoning) and will (abstract awareness of desire). So our ability to string ideas together with language vastly extends and generalizes our cognitive reach, accounting for what we think of as human intelligence.

While we could call the part of the subconscious that supports language mentalese, I wouldn’t recommend it, because this support system is not language itself. This massively parallel process can’t be thought of as just a string of symbols; each word and phrase branches out into a massive web of interconnections into a deeply multidimensional space that joins back to the rest of the mind. It follows Universal Grammar (UG), which as laid out by Noam Chomsky is a top-down set of language rules that the subconscious language module supports, but not because it is an internal language but because it has to simplify the output into a form consciousness can use. So natural language is the outer layer of the onion, but it is the only layer we can consciously access, so it is fair to say we consciously reason with the help of natural language, even though we can also do simple reasoning without language. While the part of language-assisted reasoning of which we are consciously aware is entirely conducted in natural language, it is only partially right to say we think in our native tongue because most of the actual work behind that reasoning happens subconsciously. And the subconscious part doing most of the work is not using an internal language at all, though it does use innate mechanisms that support features common to all languages.

So what about linguistic determinism, aka the Sapir-Whorf hypothesis, which states that the structure of a natural language determines or greatly influences the modes of thought and behavior characteristic of the culture in which it is spoken? As with all nature/nurture debates, it is some of each, but with the lion’s share being nature. Natural language is just a veneer on our deep subconscious language processing capacity, but both develop through practice and what we are practicing is the native language our society developed over time. The important point, though, is that thinking is only superficially conscious and consequently only superficially linguistic, and hence only marginally linguistically determined. Words do matter, as do the kinds of verbal constructions we use, so to the extent we guide our thinking process linguistically with inner speech they have an influence. But language is only a high-level organizer of ideas, not the source of meaning, so it does not ultimately constrain us, even though it can sometimes steer us. We can coin new words and idioms, and phase out those that no longer serve as well. So again, just to clarify: while a digital computer can parse language and store words and phrases, this doesn’t even scratch the surface of the deep language processing our subconscious does for us. It is only a false impression of consciousness that the flow of words through our minds reveals anything about the information processing steps that we perform when we understand things or reason with language.

Insight 5. Consciousness is a simplified, “cartoon”-like version of reality with its own feel. We are not zombies or robots, pursuing our tasks with no inner life. Consciousness feels the way it does because the overall mind, which also includes considerable subconscious processing of which we are not consciously aware, cordons off conscious access to subconscious processing not deemed relevant to the role of consciousness. The fact that logic only works in a serial way, with propositions implying conclusions, and the fact that bodies can only do one thing at a time, put information management constraints on the mind that consciousness solves. To develop propositions on which one can apply logic that can be useful in making a decision, one has to generalize commonly encountered phenomena into categories about which one can track logical implications. So we simplify the flood of sensory data into a handful of concrete objects about which we reason. These items of reason, generically called concepts, are internal representations of the mind that can be thought of as pointers to the information comprising them. Our concept of a rock bestows a fixed physical shape, while a quantity of water has a fluid shape. The concept sharp refers to a capacity to cut, which is associated with certain physical traits. Freedom and French are abstract concepts only indirectly connected to the physical world about which we each have acquired a very detailed, personal internal representations. Consciousness is a special-purpose “application” (or subroutine) within the mind that focuses on the concepts most relevant to current circumstances and applies reason along with habit, learning and intuition to direct the body to take actions one at a time. The only real role of consciousness is to manage this top-level single-stream logic processing, so it doesn’t need to be aware of, and would only be distracted by, the details that the subconscious takes care of, including sensory processing, memory lookup/recognition, language processing and more. Consciousness needs access to all incoming information upon which it can be useful to apply reason. To do this in real time, the mind preprocesses concepts subconsciously where possible, which is often little more than a memory lookup service, but also includes converting 2-D images into known 3-D objects or converting concepts into linguistic form. We bypass concepts and reason entirely whenever habit, experience and intuition can manage alone, but do so with conscious oversight. Consciousness needs to act continuously and “enthusiastically”, so it is pre-configured to pursue innate desires, and can develop custom desires as well.

I call the consciousness subroutine the SSSS for single-stream step selection, because objectively that is what it is for, selecting the one coordinated action at a time for the body to perform next. Our whole subjective world of experience is just the way the SSSS works, and its first person aspect is just a consequence of the simplification of the world necessary to support reason, combined with all the data sources (senses, memory, emotion) that can help in making decisions. Our subjective perspective is only figuratively a projection or a cartoon; it is actually comprised of a combination of nonrepresentational data that statistically correlates information and representational data that represents both the real and imagined symbolically through concepts. This perspective evolved over millions of years, since the dawn of animal minds. Though reasoning ultimately leads to a single stream of digital decisions (ones that go one way or another), nothing constrains it from using analog or digital inputs or parallel processing along the way, and it does all these things and more to optimize performance. Conscious experience is consequently a combination of many things happening at once, which only feel like a seamless integrated experience because it would be very nonadaptive if it didn’t. For instance, we perceive a steady, complete field of vision as if it were a photograph because it would be distracting if we didn’t, but actually our eyes are only focused on a narrow circle of central vision, the periphery is a blur, and our eyes dart around a lot filling in holes and double checking. The blind spots in our peripheral vision (that form where the optic nerve passes through the retina) appear to have the same color and even pattern of the area around them because it would be distracting if they disturbed the approximation to a photograph. So the software of consciousness tries very hard to create a smooth and seamless experience out of something much more chaotic. It is an intentional illusion. It seems like we see a photo, but as we recognize objects we note the fact and start tracking them separately from the background. We can automatically track their shading and lighting from different perspectives without even being aware we are doing it. Colors have distinct appearances both to provide more information we can use and to alert us to associations we have for each color.

While the only purpose of consciousness is to support reasoning, it carries this very rich subjective feel with it because that helps us make the best decisions very quickly. That it seems pleasurable or painful to us is in a way just a side effect of our internal controls that lead us to seek pleasure and avoid pain. This is because consciousness simplifies decision making by reducing complex situations into an emotional response or a preference. Such responses have no apparent rational basis, but presumably serve an adaptive purpose since we have them and evolved traits are always adaptive, at least originally (in the ancestral environment). We just respond emotionally or prefer things a certain way and then can reason starting with those feelings as propositions. Objectively, we can figure out why such responses could be adaptive. For example, hunger makes us want to eat, and, not coincidentally, eating fends off starvation. Libido makes us want sex, and reproduction perpetuates the species. Providing hunger and libido as axiomatic desires to the reasoning process eliminates the need to justify them on rational grounds. Is there a good reason why we should survive or produce offspring? Not really, but if we just happen to want to do things that have that outcome, the mandate of evolution is satisfied. Basically, if we don’t do it someone else will, and more than that, if we don’t do it better they will, in the long run, squeeze us out, so we had better want it pretty bad. So feelings and desires are critical to support reasoning, even though these premises are not based on reason themselves.

This perhaps explains why we feel emotions and desires, but it doesn’t explain why they feel just the way they do to us. This is both a matter of efficiency and logical necessity. From an efficiency standpoint, for an emotion or innate desire to serve its purpose we need to be able to process immediately and appropriately, but also simultaneously with all other emotions, desires, sensory inputs, memory, and intuition that apply in each moment. To accomplish this, all of these inputs have independent input channels into the conscious mind, and to help us tell them all apart, they all have distinct quale (kwol-ee, the way it feels, the plural is qualia). From a logical necessity standpoint, for reasoning to work appropriately the quale should influence us directly and proportionally to the goal, independent of any internally processed factors. Our bodies require foods with appropriate levels of fat, carbohydrates, and protein, but subjectively we only know smell, taste and hunger (food labels notwithstanding). These senses and our innate preferences directly support reasoning where a detailed analysis (e.g. a list of calories, fat and protein) would not. Successful reproduction requires choosing a fit mate, ensuring that mates will stay together for a long time, and procreating. This gets simplified down to feelings of sex appeal, love, and libido. Based on any kind of subsidiary reasoning couples would never stay together; they need an irrational subconscious mandate, i.e. love.

Nihilists reject or disregard innate feelings and preferences, presumably on philosophical or rational grounds. While this is undoubtedly reasonable and consequently philosophical, we can’t change the way we are wired just by willing it so. We will want to heed our desires, i.e. to pursue happiness, although unlike other animals our facility with directed abstract thought gives us the freedom to reason our way out of it or, potentially, to reach any conclusion we can imagine. Evolution has done its best to keep our reasoning facility in thrall to our desires so that we focus more on surviving and procreating and less so on contemplating our navels, but humans have developed a host of vices which can lead us astray, with technology creating new ones all the time. If vices represent a failure of our desires to keep us focused on activities beneficial to our survival, virtues oppose them by emphasizing desires or values that are a benefit, not just to ourselves but our communities. We can consequently conclude that the meaning of life is to reject nihilism because it is a pointless and vain attempt to supersede our programming, and to embrace virtuous hedonism as its opposite, to exemplify what reason and intelligence can add to life on earth.

Michael Graziano explains well how attention works within consciousness, but he says the motivation to simplify the world down to a single stream is that: “Too much information constantly flows in to be fully processed. The brain evolved increasingly sophisticated mechanisms for deeply processing a few select signals at the expense of others.”2 His sentiment is right, his reason is wrong; the brain has more than enough computational capacity to fully process all the parallel streams hitting the senses and all the streams of memory generated in response, but it does all this subconsciously. Massive parallel processing is the strength of the subconscious. The conscious subroutine is intentionally designed to produce a single stream of actions since there is only one body, and so this is the motivation to simplify and focus attention and create a conscious “theater” of experience. A corollary key insight to my points on consciousness is that most of our minds are subconscious and contain specialized and generalized functions that do all the computationally-intensive stuff.

Insight 6. Minds reason using models that represent possibilities. Its job is to control the body by analyzing current circumstances to compute an optimal response. No ordinary machine can do this; it requires a system that can collect information about the environment, compare it to stored information, and based on matches, statistics, and rules of logical entailment select an appropriate reaction. Matches and statistics are the primary drivers of associative memory, which not only helps us recognize objects on sight and remember information learned in the past given a few reminders, but also supports more general intuition about what is important or relevant to the present situation. While this information is useful, it is not predictive. Since the physical world follows pretty rigid laws of nature, it is possible to predict with near certainty what will happen under controlled circumstances, so an animal that had a way to do this would have a huge advantage over one that could not. Beyond that, once animals developed predictive powers, a mental arms race ensued to do it better. Nearly all animals can do it, and we do it best, but how?

The answer lies entirely in the words “controlled circumstances”. We create a mental model, which is an imaginary set of premises and rules. A physical model, in particular, contains physical objects and rules of cause and effect. Cause and effect is a way of describing laws of nature as they apply at the object level within a physical model. So gravity pulls objects down, object integrity holds objects together differently for each substance, and simple machines like ramps, levers and wheels can impact the effort required. And we recognize other animals as agents employing their own predictive strategies. Within a model, we can achieve certainty: rules that always apply and causes that always produce expected effects. The rules don’t have to be completely certain (deductive); they can be highly likely (inductive). But either way, they work, so once we decide to use a given model in a real-world situation, we can act quickly and effectively. And causal reasoning can be chained to solve complex puzzles. While we can control circumstances with models, the real world will never align precisely with an idealized model, so how we choose the models is as important as how we reason with them. Doubt will creep in if our results fall short of expectations, which can happen if we choose an inappropriate model, or if a model is appropriate but inadequately developed. For every situation, we select one or more models from a constellation of models, and we apply the rules and act with an appropriate degree of certainty based on our confidence in picking the model, its accuracy, and our ability to keep it aligned with reality.

Mental models are mostly subrational. A full definition of subrational will be presented later in Concepts and Models, but for now think of it as a superset of everything subconscious plus everything in our conscious awareness that is not a direct object of reasoning. Models themselves need not be based in reason and need not enter into the focus of conscious reasoning. We can reason with models supported entirely by hunches, but we can also if desired take a step back mentally and use reason to list the premises, rules, and scope of a model we have been using implicitly up to that point. However, as we will see in the next insight, doing this only rationalizes the subrational, which is to say it provides another way of looking at them that is not necessarily better or even right (to the extent an interpretation of something can be said to be right or wrong).

Insight 7. Reasoning is fundamentally an objective process that manages truth and knowledge. Objective principally means without bias and agreeable to anyone based on a preponderance of the evidence, and subjective is everything else, namely that which is biased or not agreeable to everyone. Science tries to achieve objectivity by using instruments for measurements, checking results independently, and using peer review to establish a level of agreement. While these are great ways to eliminate bias and foster agreement, we have no instruments for seeing thoughts or checking them: all our thoughts are inherently subjective. This is an obstacle to an objective understanding of the mind. Conventionally science deals with this by giving up: all evidence of our own thought processes are considered inadmissible, and science consequently has nothing to say. Consider this standard view of introspection:

“Cognitive neuroscientists generally believe that objective data is the only reliable kind of evidence, and they will tend to consider subjective reports as secondary or to disregard them completely. For conscious mental events, however, this approach seems futile: Subjective consciousness cannot be observed ‘from the outside’ with traditional objective means. Accordingly, some will argue, we are left with the challenge to make use of subjective reports within the framework of experimental psychology.” 3

This wasn’t always so. The father of modern psychology, Wilhelm Wundt, was a staunch supporter of introspection, which is the subjective observation of one’s own experience. But its dubious objectivity caught up with it, and in 1912 Knight Dunlap published an article called “The Case Against Introspection” that pointed out that no evidence supports the idea that we can observe the mechanisms of the mind with the mind. I agree, we can’t. In fact, I propose the SSSS process supporting consciousness filters our awareness to include only the elements useful to us in making decisions and consequently blocks our conscious access to the underlying mechanisms. So we don’t realize from introspection that we are machines, albeit fancy ones. But we have figured it out scientifically, using evolutionary psychology, the computational theory of mind, and other approaches within cognitive science.

The limitations of introspection don’t make it useless from an objective standpoint; they only mean we need to interpret it in light of objective knowledge. So we can, for example, postulate an objective basis for desires and then test them for consistency using introspection. We should eventually be able to eliminate introspection from the picture, but most of our understanding of consciousness and the SSSS at this point comes from our use of it, so we can’t ignore what we can glean from that.

While our whole experience is subjective, because we are the subject, that doesn’t mean a subset of what we know isn’t objective. We do know some things objectively, and we know we know them because we are using proven models. And we know the degree of doubt we should have in correlating these models to reality because we have used them many times and seen the results. It is usually more important for consciousness to commit to actions without doubt than to suffer from analysis paralysis, though of course for complex decisions we apply more conscious reasoning as appropriate.

We have many general-purpose models we trust, and we generally know how our models match up with those other people use and how much other people trust them. Since objectivity is a property of what other people think, i.e. agreeable to all and not subjective, we need to have a good idea of what models we are using and the degree to which other people use the same models (i.e. similar models; we each instantiate models differently). If our models are subrational, how can we ever achieve this? For the most part, it is done through an innate talent called theory of mind:

Theory of mind (often abbreviated ToM) is the ability to attribute mental states — beliefs, intents, desires, pretending, knowledge, etc. — to oneself and others and to understand that others have beliefs, desires, intentions, and perspectives that are different from one’s own.

So we subrationally form models and can intuit much about the models of others using this subrational and largely subconscious skill. Our subconscious mind automatically does things for us that would be too laborious to work out consciously. Consciousness evolved more to support quick reactions than to think things through, though humans have developed quite a knack for that. But whether these skills are subconscious, subrational, or rational, the rational conscious mind directs the subrational and subconscious and thus takes credit for the capacities of the whole mind. This gives us the ability to distinguish objective knowledge from subjective opinion. Consider this: when a witness is asked to recount events under oath, the jury is expecting to hear objective truth. They will assess the evidence they hear using theory of mind on the witness to understand his models and to rule out compromising factors such as mental capacity, impartiality, memory or motives. People read each other very well, and it is hard to lie convincingly because we have subconscious tells that others can read, which is a mechanism that evolved to allow trust to develop despite our ability to lie. The jury expects the witness can record objective knowledge similarly to a video camera and that they can acquire this knowledge from him. Beyond juries, we know our capacities, partiality, memory, and motives well enough to know whether our own knowledge is objective (that is, agreeable to anyone based on a preponderance of the evidence). So we’ve been objective long before scientific instruments, independent confirmation or peer review came along. Of course, we know that science can provide more precision and certainty using its methods than we can with ours, but science is more limited in the phenomena to which it applies. People have always understood the world around them to a high degree of objectivity that would stand up to considerable scrutiny, despite also having many personal opinions that would not. We tend not to give ourselves much credit for that because we are not often asked to separate the objective and subjective parts.

Objectivity does not mean certainty. Certainty only applies to tautologies, which are concepts of the ideal world, not the physical world. A tautology is a proposition that is necessarily true, or, put another way, is true by definition. If one sets up a model, sometimes called a possible world, in which one defines what is true, then within that possible world, everything that is true is necessarily true, by definition. So “a tiger is a cat” is certain if our model defines tiger as a kind of cat. Often to verify whether or not one has a tautology one has to clarify definitions, i.e. clarify the model. This process will identify rhetorical tautologies. If we say that the rules of logic apply in our models, and we generally will, then anything logically implied is also necessarily true and a logical tautology. The law of the excluded middle, for example, demonstrates a logical necessity by saying that “A or not A” is necessarily true. Or, more famously, a syllogism says that “if A implies B and B implies C, then A implies C”. While certainty is therefore ideally possible, we can’t see into the future in the physical world, so physical certainty is impossible. We also can never be completely certain about the present or past because we depend on observation, which is both indirect and unprovable. So physical objectivity is not about true (i.e. ideal) certainty, but it does relate to it. By reasoning out the connection, we can learn the definition and value of physical objectivity.

To be physically objective means to establish one or more ideally objective models and to correlate physical circumstance to these models. Physically objectivity is limited by the quality of that correlation, both in how well the inputs line up and how closely the output behavior of the ideal model(s) ends up matching physical circumstances. The innate strategy of minds is to presume perfect correlation as a basis for action and to mop up mistakes afterward. Also, importantly, minds will try to maximally leverage learned behavior first and improvised behavior second. That is, intuitive/automatic first and reasoned/manual second. So, for example, I know how to open the window next to me as I have done it before. I feel certain I can apply that knowledge now to open the window. But my certainty is not really about whether the window will open, it is about the model in my mind that shows it opening when I flip the latch and apply pressure to raise it. In that model, it is a certainty because these actions necessarily cause the window to open. If the window is stuck or even permanently epoxied, it doesn’t invalidate the model, it just means I used the wrong model. So if the window is stuck how do I mop up from this mistake? The models we apply sit within a constellation of models, some of which we hold in reserve from past experience, some of which we have consciously available from premeditation, and some of which we won’t consciously work out until the need arises. For every model we have a confidence level, the degree to which we think it correlates to the situation at hand. This confidence level is mostly a function of associative memory, as we subrationally evaluate how well the model premises line up with the physical circumstances. In the case of habitual or learned behavior, we do this automatically. So if this window doesn’t open as I thought it would, from learned behavior I will push harder and use quick thrusts if needed to try to unjam it. Whether it works or not I will update my internal model of the behavior of stuck windows accordingly, but in this case, I didn’t directly employ reasoning, I just leveraged learned skills. But the mind will rather seamlessly maintain both learned and reasoned approaches for handling situations. This means it will maintain models about everything because it will frequently encounter situations where learned behavior is inadequate but reasoning with models that include causation works.

Just as we don’t generally need to separate the objective and subjective parts of our knowledge, we don’t generally need to separate learned behavior from reasoned behavior. It is important to the unity of our subjective experience that we perceive these very different things as fitting seamlessly together. But this introduces another factor that makes it hard to identify our internal models: learned behavior doesn’t need models or a representational approach, at least not of the simplified form used for logical analysis. We can, potentially, remember every detail of every experience and pull up relevant information exactly when needed in an appropriate way without any recourse to logic or reason at all. So what kind of existence do our ideal models have? While I think we do persist many aspects of many models in our memories as premises and rules, we tend not to be too hard and fast about them, and they blend into each other and our overall memory in ways that let us leverage their strengths without dwelling on their weaknesses. Even as we start to reason with them, we only require a general sense that their premises and rules are sound and well defined, and if pressed we may learn they are not, at which point we will fill them out until they are as certain as we like. We can, therefore, conclude that while we use objectivity all the time, in is usually in close conjunction with subjectivity and inseparable from it. To be convincing, we need to develop ways to isolate objective and subjective components.


Insight 8. Insight 8. We really have free will. We already know (intuitively) that we have free will, so I shouldn’t take any credit for this one. But I will because a preponderance of the experts believe we don’t, which is a consequence of their physicalist perspective. Yes, the universe is deterministic and everything happens according to fixed laws of nature, so we are not somehow changing those laws in our heads to make nature unfold differently. What happens in our heads is in fact part of its orderly operation; we are machines that have been tuned to change the world in the ways that we do. So far, that suggests we don’t have free will but are just robots following genetic programming. But several things happen to create genuine freedom. Freedom can’t mean altering the future from a preordained course to a new one because the universe is deterministic and each moment follows the preceding according to fixed laws of nature. But since the universe has always been this way and we nevertheless feel like we have free will, freedom must mean something else.

Freedom really has to do with our conception of possible futures. We imagine the different outcomes from different possible courses of action. These are just imaginary constructions (models with projected consequences) with no physical correlate, other than the fact that they exist in our brains. But we think of them as being possible futures even though there is really only one future for us. So our sense of free will is rooted in the idea that what we do changes the universe from its predetermined course. We don’t, but two factors explain why our perspective is indistinguishable from a universe in which we could change the future: unpredictability and optimized selection. Regarding unpredictability, neither we nor anyone could ever know for sure what we are going to do; only an approximate prediction is possible. Although thinking follows the laws of nature, the process is both complex and chaotic, meaning that any factor, even the smallest, could change the outcome. So every decision, even the simplest, could never be predicted with certainty. The second factor is optimized selection, which is a mental or computational process that uses an optimization algorithm to choose a strategy that has the best chance of producing a physical effect. First, the algorithm collects information, which is data that has more value for some purpose than white noise has. For example, sensory information is very valuable for assessing the current environment. And our innate preferences, experience, state of mind, and whim (which is a possibly unexplainable preference) are fed to the algorithm as well. This mishmash of inputs is weighed and an optimal outcome results. If the optimal action seems insufficiently justified, we will pause or reconsider as long as it takes until the moment of sufficient justification arrives, and then we invariably perform that action. At that moment the time for free will has passed; the universe is just proceeding deterministically. We exercised our free will just before that moment, but before I explain why I have a few more comments on unpredictability and optimization algorithms.

The weather is unpredictable but lacks optimized selection because it is undirected. A robot trained to use learned behavior alone to choose strategies that produce desired effects has an optimized selection algorithm, but might be entirely predictable. If its behavior dynamically evolves based on live input data, then it may become unpredictable. Viewed externally, the robot might appear to have “free will” in that its behavior would be unpredictable and goal-oriented like that of a human. However, internally the human is thinking in terms of selecting from possible futures, while the robot is just looking up learned behaviors. People don’t depend solely on learned behavior; we also use reason to contemplate general implications of object interactions. To do this, we set up mental models and project how different object interactions might play out if different events transpired.

The real and deep upshot of this is that our concept of reality is not the same thing as physical reality. It is a much vaster realm that includes all the possible futures and all the models we have used in our lives. Our concept of reality is really the ideal world, in which the physical world is just a special case. The exercise of free will, being the decisions we take in the physical world, does represent a free selection from myriad possibilities. Become our models correlate so well to the real world we come to think of them as being the same, but they aren’t. Free will exists in our minds, but not in our hypothetical robot minds, because our minds project possible futures. A robot programmed to do this would then have all the elements of free will we know, and further would be capable of intelligent reasoning and not just learned behavior. It could pass a Turing test where the questioner moved outside the range of the learned behavior of the robot. Once we build such a robot, we will need to start thinking about robot rights. Could an equally intelligent optimization algorithm be designed that did not use models (and consequently had no consciousness or free will)? Perhaps, but I can’t think of a way to do it.

So our brain’s algorithm made an unpredictable decision and acted on it. The real question is this: Why do “we” take credit for the free will of our optimization algorithms? Aren’t we just passively observing the algorithms execute? This is simply a matter of perspective. We “are” our modeling algorithms. Ultimately, we have to mean something when we talk about the real “us”. Broadly, we mean our bodies, inclusive of our minds, but more narrowly, when we are referring just to the part of us that makes the decisions, we mean those modeling algorithms. In the final analysis, we are just some nice algorithms. But that’s ok. Those algorithms harbor all the richness and complexity that we, as humans, can really handle anyway. They are enough for us, and we are very much evolved to feel and believe that they are enough for us. Objectively, they are a patchwork of different strategies held together with scotch tape and baling wire, but we don’t see them that way subjectively. Subjectively the world is a clean, clear picture where everything has its place and makes sense in one organic whole that seems fashioned by a divine creator in a state of sheer perfection. But subjectively we’re wearing rose-colored glasses, and darkly-tinted ones at that, because objectively things are very far from clean or perfect.

So that explains free will, the power to act in a way we can fairly call our own. To summarize, our brains behave deterministically, but we perceive the methods they use to do it as selections from a realm of possibilities, and we quite reasonably identify with those methods so that we take both credit and responsibility for the decisions. More significantly, while we were dealt a body and mind with certain strengths and an upbringing with certain cultural benefits, this still leaves a vast array of possible futures for our algorithms to choose from. Since nobody can exercise duress on us inside our own minds, this means that no other entity but the one we see as ourself can take credit or blame for any decision we make. Do we have to take responsibility for our actions or can we absolve ourselves as merely inheriting our bodies and minds? We do have to take credit and blame because running the optimization algorithms is an active role; abstaining would mean doing nothing, which is just a different choice. Note that this physical responsibility is not the same as moral responsibility. How our thoughts, choices, and actions stand up from a societal standpoint is another question outside the scope of this discussion. But physically, if we perform an action then it is a safe bet that we exercised free will to do it. The only alternate explanations are mind control or some kind of misinterpretation, e.g. that it looked like we pressed the button but actually we were asleep and our hand fell from the force of gravity.

Sometimes free will is defined as “the ability to choose between different possible courses of action”. This definition is actually tautological because the word “choose” is only meaningful if you understand and accept free will. To choose implies unpredictability, an optimization algorithm, consciousness, and ownership of consciousness. Our whole subjective vocabulary (subjective words include feel, see, imagine, hope) implies all sorts of internal mechanisms we can’t readily explain objectively. And we are so prone to intermingling subjective vocabulary with objective vocabulary that we are usually unaware we are doing it.

One more point about free will: my position is a compatibilist position, meaning that determinism and free will are compatible concepts. Free will doesn’t undermine determinism, it just combines unpredictability, optimization algorithms, and the conscious modeling of future possibilities to produce an effect that is indistinguishable from the actual future changing based on our actions.

 

A very brief overview of TDTM

TDTM, for Top-Down Theory of Mind, principally combines a new philosophical stance with two scientific theories:

1. Physicoidealism
2. The theory of evolution
3. The computational theory of mind (CTM)

Any scientific discussion must first define what exists, which is called an ontology or theory of being. I am proposing a new ontology for TDTM that I call physicoidealism. Physicalism is an ontological monism, which means it says just one kind of thing exists. Specifically, it asserts that only the physical world exists, consisting of space, matter, and energy. Idealism asserts that only the mental world exists, consisting of immaterial ideas. Physicoidealism is just the union of these two monisms, eliminating the “only” from each. As the brain is now known to reduce to purely physical phenomena, science has concluded that the ideal does not exist, but this is a bit preposterous considering science is built out of hypotheses, which are ideal. Math, ideas, models, and theories are all nonphysical constructions of the ideal world. Nothing about them precludes the physical in any way, but they are not physical. Yes, of course, our access to them is entirely mediated through physical mechanisms like the brain, computers, and books, but any given mathematical law exists (in an ideal sense) independently of any physical system that uses or refers to it. Scientists do try to divine the “actual” laws of nature, but we can never know if there are any as such. All we can do is create idealized, non-physical models that correlate pretty well with nature. So although we have some confidence “actual” laws of nature do exist since the universe behaves so consistently, we have no way to find them or prove that the laws we come up with are right.

The more adamant physicalists among you will by now be thinking that since reductionism implies that everything is physical, this means anything I am calling ideal is just a convenient fiction or illusion with no real substance. All ideas are fictions and illusions with no physical substance, but that doesn’t mean they can’t impact the physical world. Physical systems like minds and computers can use math and programs and ideas to affect the physical world. How these systems affect the world can only be understood through the ideal concepts of information and algorithm. No amount of study of the mechanics of the brain will ever reveal these important aspects of its programming. Programming is the key that unlocks the ideal world, where logic, mathematics, representations and ideas live. Programs represent possibilities; they use one kind of simplified representation or another to describe bounded or unbounded sets of possibilities, and they describe logical operations that can be used to generate a limited or unlimited set of outputs given any inputs. We can discuss an abstract idea, like a pencil or a cat, independent of any physical implementation and inclusive of possibilities both bounded or unbounded. As Sean Carroll writes1 “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. Also, to be useful programs must ultimately correlate back to reality, tie references back to referents, and allow us to change reality. The mind (and other programs) can do that. So minds add a new capability to the physical world it lacked before, a capability that could only seem like magic to inanimate matter or even plants, the power to predict the future by dividing nature into causes and effects, and thus develop strategies and then act on them. It seems a bit ironic that we consider fortune tellers to be charlatans considering the purpose of minds is to “see” the future so as to better control it. Of course, the limitation is that minds never have certain knowledge of the future, but they are chock full of very good guesses.

Another question that tends to come up about now is “what about determinism?” Physicalism says the world is running on fixed laws and that the outcome is preordained. Now that we have quantum uncertainty, perhaps it is not preordained, but it is still not alterable by free will. I explained in Key Insights why free will exists despite determinism. Although the decisions we made could not have been decided otherwise, they seem like they could to us because we imagine how they could have turned out otherwise, and since the physical world is too complex to predict, no one can tell us we didn’t pick one future out from among many. What makes us free is that our minds dwell in the ideal world of possibilities, and only secondarily in the physical world. In our world baseball and other generalizations exist, but they are not strict physical objects or events. When we are about to do things, and after we have done them, we don’t think only in terms of that specific instance, we generalize to all similar situations. So while actual decisions could not have been decided otherwise, we don’t see decisions in the context of a specific case, we generalize to all similar situations. To function effectively, we have to view the world through this much larger lens of possibility than as a mundane physical world that ultimately lacks any possibility since only one path will unfold. Put another way, at the moment we take an action, we have no choice, it is done. The moment before that, the universe and our minds are simply too complex for it to be possible to predict what will happen, even though we know it must unfold deterministically. So looking forward minds manage all possibilities as possible and interpret their action-optimizing algorithms as choosing from those possibilities, even though they are just making the generalization that situations similar to those in the past will play out in similar ways given similar reactions.

So are human choices actually shaping the world? Yes, because free will actually does exist as we think it does. The only illusion here is the idea that determinism implies the future is simple to predict. It doesn’t. Because it is possible for minds to exist and to gather information, model it, and compute and take actions, the physical world actually includes this slice of the ideal world, and so outcomes that leverage the world of possibility are entirely within the laws of determinism. In other words, determinism is not limited to the “direct” interactions of particle A hitting particle B; information processing and feedback vastly expand the range of complexity of what might happen “indirectly” (I use quotes because everything physical is necessarily direct, it is just that direct can become very convoluted). The physical universe does seem simple enough to predict if you leave out minds, but minds are part of the physical universe. When we change the world around us, it is the physical world changing itself. We are just the most complex cogs in the machine.

So brains create minds, and minds open a window into the ideal world of possibility that actually turns out to be an infinitely richer world than the physical world that spawned it. What do we know so far, scientifically, about how the mind came to be and how it works? Darwin discovered how it came to be with his theory of evolution in 1859 and the computational theory of mind (CTM), proposed in its modern form by Hilary Putnam in 1961, provides the basis of how it works. While Darwin wondered, “How does consciousness commence?”, he didn’t solve it, but he opened the door to evolutionary psychology in The Origin of Species with this comment: “Psychology will be based on a new foundation, that of the necessary acquirement of each mental power and capacity by gradation.” We now have a good overall sense of the evolutionary basis of all major mental capacities. The computational theory of mind (CTM) proposes mechanisms to support mental processes. The idea that thought is an exercise in information management and not a standalone substance is a major breakthrough, so far fully supported by the evidence. We now have reasonably good digital algorithms that approximate some mental functions, though we are still far short of artificial intelligence itself.

So far, the implications of these theories for understanding the mind have been best organized into one place in Steven Pinker’s 1996 book How the Mind Works. Pinker covers many implications of evolution and CTM on how the mind works in a very objective way, and I highly recommend it and will build on it. But we have a ways to go. Pinker doesn’t wade into the treacherous waters of metaphysics I’m in. I’ve introduced the idea of a computational idealism that forms an independent monism that has to be combined with physicalism to cover all that exists. From there I have developed the subjective perspective as a referential reality that funnels a cartoon of the world into a stream that can be analyzed logically to make decisions. And I explain free will as a consequence of the future being unknowable combined with action-optimizing algorithms that model possible worlds and pick from them. From where I sit, we need to expand the scope of objective science to include the ideal world, which is not a discovered world but a created one, an engineering project. Logic and math and models and ideas are built, not discovered, and the mind is a software engineering project. So much of how it works is not the simple outcome of scientific laws but the complex result of engineering decisions. The biological and social sciences, of course, accept that life is engineered and that understanding it better requires some reverse engineering, but I think they have historically undervalued the need to apply reverse engineering to psychology. Steven Pinker does an excellent job covering evolutionary psychology, and I will take that thinking further still.

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.

© 2016 Steve Wagar Send mail to Steve