Deriving an Appropriate Scientific Perspective for Studying the Mind

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

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

1. Our Common Knowledge Understanding of the Mind

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

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

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

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

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

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

2. Form & Function Dualism: things and ideas exist

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

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

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

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

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

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

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

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

Pipe

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

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

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

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

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

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

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

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

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

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

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

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

4. What Makes Knowledge Objective?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

An Overview of What We Are

[Brief summary of this post]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Certainty Engine

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

An overview of computation and the mind

[Brief summary of this post]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

An overview of evolution and the mind

[Brief summary of this post]

The human mind arose from three somewhat miraculous breakthroughs:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The process of mind

[Brief summary of this post]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Concepts and Models

[Brief summary of this post]

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

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

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

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

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

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

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

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

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

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

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

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