Deriving an Appropriate Scientific Perspective for Studying the Mind

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

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

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

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

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

Even more than our senses, we identify our conscious selves with our ability to think. We can tell that our thoughts are happening inside our heads, and not, say, in our hearts. It is common knowledge that our brains are in our heads and brains think1, so this impression is a well-supported fact, but why do we feel it? Let’s call this awareness of our brains “encephaloception,” a subset of proprioception (our sense of where the parts of our body are), but also including other somatosenses like pain, touch, pressure. The main reason our encephaloception pinpoints our thoughts in our heads is that senses work best when they provide consistent and accurate information, and the truth is we are thinking with our brains. Like other internal organs, it helps us to be aware of pain, motion, impact, balance, etc. on the head and brain as this can affect our ability to think, so having sufficient sensory awareness of our brain just makes sense. It is not just a side effect, say, of having vision or hearing in the head that we assume our thoughts originate there; it is the consistent integration of all the sensory information we have available.

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

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

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

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

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

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

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

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

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

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

2. Form & Function Dualism: things and ideas exist

We can’t study anything without a subject to study. What we need first is an ontology, a doctrine about what kinds of things exist. We are all familiar with the notion of physical existence, and so to the extent we are referring to things in time and space that can be seen and measured we share the well-known physicalist ontology. Physicalism is an ontological monism, which means it says just one kind of thing exists, namely physical things. But is physicalism is a sufficient ontology to explain the mind? Die-hard natural scientists insist it is and must be, and that anything else is new-age nonsense. I am sympathetic to that view as mysticism is not explanatory and consequently has no place in discussions about explanations. And we can certainly agree from common knowledge that there is a physical aspect, being the body of each person and the world around us. But knowing that seems to give us little ability to explain our subjective experience, which is so much more complex than the observed physical properties of the brain would seem to suggest. Can we extend science’s reach with another kind of existence that is not supernatural?

We are intimately familiar with the notion of mental existence, as in Descartes’ “I think therefore I am.” Feeling and thinking (as states of mind) seem to us to exist in a distinct way from physical things as they lack extent in space or time. Idealism is the monistic ontology that asserts that only mental things exist, and what we think of as physical things are really just mental representations. In other words, we dream up reality any way we like. But science and our own experience have provided overwhelming evidence of a persistent physical reality that doesn’t fluctuate in accord with our imagination, and this makes idealism rather untenable. But if we join the two together we can imagine a dualism between mind and matter in which both the mental and physical exist without either being reducible to the other. All religions have seized on this idea, stipulating a soul (or equivalent) that is quite distinct from the body. But no scientific evidence has been found supporting the idea that the mind can physically exist independent of the body or is in any way supernatural. But if we can extend science beyond physicalism, we might find a natural basis for the mind that could lift religion out of this metaphysical quicksand. Descartes also promoted dualism, but he got into trouble identifying the mechanism: he supposed the brain had a special mental substance that did the thinking, a substance that could in principle be separated from the body. Descartes imagined the two substances somehow interacted in the pineal gland. But no such substance was ever found and the pineal gland’s primary role is to make melatonin, which helps regulate sleep.

If the brain just operates under the normal rules of spacetime, as the evidence suggests, we need an explanation of the mind bound by that constraint. While Descartes’ substance dualism doesn’t deliver, two other forms of dualism have been proposed. Property dualism tries to separate mind from matter by asserting that mental states are nonphysical properties of physical substances (namely brains). This misses the mark too because it suggests a direct or inherent relationship between mental states and the physical substance that holds the state (the brain), and as we will see it is precisely the point that this relationship is not direct. It is like saying software is a non-physical property of hardware; while software runs on hardware, the hardware reveals nothing about what the software is meant to do.

Finally, predicate dualism proposes that predicates, being any subjects of conversation, are not reducible to physical explanations and so constitute a separate kind of existence. I will demonstrate that this is true and so hold that predicate dualism is the correct ontology science needs, but I am rebranding it as form and function dualism (just why is explained below). Sean Carroll writes,3 “Does baseball exist? It’s nowhere to be found in the Standard Model of particle physics. But any definition of “exist” that can’t find room for baseball seems overly narrow to me.” Me too. Baseball encompasses everything from an abstract set of rules to a national pastime to specific events featuring two baseball teams. Some parts have a physical corollary and some don’t, but the physical part isn’t the point. A game is an abstraction about possible outcomes when two sides compete under a set of rules. “Apple” and “water” are (seemingly) physical predicates while “three”, “red” and “happy” are not. Three is an abstraction of quantity, red of color, happy of emotion. Quantity is an abstraction of groups, color of light frequency, brightness and context, and emotion of experienced mental states. Apple and water are also abstractions; apples are fruits from certain varieties of trees and water is the liquid state of H2O, but is usually used generically and not to refer to a specific portion of water.4 Any physical example of apple or water will fall short of any ideal definition in some ways, but this doesn’t matter because function is never the same as form; it is intentionally an abstract characterization.

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

This diagram shows how form and function dualism compares to substance dualism and several monisms. These two perspectives, form and function, are not just different ways of viewing a subject, but define different kinds of existences. Physical things have form, e.g. in spacetime, or potentially in any dimensional state in which they can have an extent. Physical systems that leverage information have both form and function, but to the extent we are discussing the function we can ignore or set aside considerations of the form because it just provides a means to an end. Function has no extent but is instead measured in terms of its predictive power. Pattern-matching techniques and algorithms implement functionality passively through brute force, while reasoning creates information actively by laying out concepts and rules that connect them. In a physical world, form makes function possible, so they coexist, but form and function can’t be reduced to each other. This is why I show them in the diagram as independent dimensions that intersect but generally do their own thing. Technically, function emerges from form, meaning that interactions of forms cause function to “spring” into existence with new properties not present in forms. But it has nothing to do with magic; it is just a consequence of abstraction decoupling information from what it refers to. The information systems are still physical, but the function they manage is not. Function can be said to exist in an abstract, timeless, nonphysical sense independent of whether it is ever implemented. This is true because an idea is not made possible because we think it; it is “out there” waiting to be thought whether we think it or not. However, as physical creatures, our access to function and the ideal realm is limited by the physical mechanisms our brains use to implement abstraction. We could, in principle, build a better mind, or perhaps a computer, that can do more, but any physical system will always be physically constrained and so limit our access to the infinite domain of possible ideas. Idealism is the reigning ontology across this hypothetical space of ideas, but it can’t stand alone in our physical space. And though we can’t think all ideas, we can potentially steer our thoughts in any direction, so given enough time we can potentially conceive anything.

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

Pipe

A thought about something is not the thing itself. “Ceci n’est pas une pipe,” as Magritte said5. The phenomenon is not the noumenon, as Heidegger would have put it: the thing-as-sensed is not the thing-in-itself. If it is not the thing itself, what is it? Its whole existence is wrapped up in its potential to predict the future; that is it. However, to us, as mental beings, it is very hard to distinguish phenomena from noumena, because we can’t know the noumena directly. Knowledge is only about representations, and isn’t and can’t be the physical things themselves. The only physical world the mind knows is actually a mental model of the physical world. So while Magritte’s picture of a pipe is not a pipe, the image in our minds of an actual pipe is not a pipe either: both are representations. And what they represent is a pipe you can smoke. What this critically tells us is that we don’t care about the pipe, we only care about what the pipe can do for us, i.e. what we can predict about it. Our knowledge was never about the noumenon of the pipe; it was only about the phenomena that the pipe could enter into. In other words, knowledge is about function and only cares about form to the extent it affects function. We know the physical things have a provable physical existence — that the noumena are real — it is just that our knowledge of them is always mediated through phenomena. Our minds experience phenomena as a combination of passive and active information, where the passive work is done for us subconsciously finding patterns in everything and the active work is our conscious train of thought applying abstracted concepts to whatever situations seem to be good matches for them.

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

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

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

Pragmatism takes a hard rap because it carries a negative connotation of compromise. The pragmatist has given up on theory and has “settled” for the “merely” practical. But the whole point of theory is to explain what will really happen and not simply to be elegant. It is not the burden of life to live up to theory, but of theory to live up to life. When an accepted scientific theory doesn’t exactly match experimental evidence, it is because the experimental conditions are more complex than the theory’s ideal model. After all, the real world is full of imperfections that the simple equations of ideal models don’t take into account. However, we can usually model secondary and tertiary effects pretty well 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 approach and a pattern analysis approach. The axioms of theoretical models are built using a pattern analysis approach and its rules by a causative approach, while our subconscious thinking is mostly done using the pattern analysis approach, though also leveraging concepts established using both pattern analysis and causative approaches. So we generate a lot of working knowledge that we draw on constantly that is not the product of rigorous theory. While we think of this conscious merging of subconscious intuition/experience and conscious reason/theory as being fair and balanced, it is not. 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 have to spot and overcome biases or we will overlook more effective rational solutions. All of our top-level decisions have to strike a balance between experience (conservative) and reason (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 principle role of consciousness to provide the progressive element, to make the call between a tried-and-true or a novel approach to any situation. Those 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, it is not a component of the theories themselves and only comes into play in their application. So as I develop a theory to explain the mind, that theory will not itself be pragmatic but will instead be reasoned, so we can say that the kind of knowledge represented by theories has 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 uses. 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 just speak to a conception of effects, which includes our experience of things, but it also provides a set of rules governing causes and effects in idealized systems. Viewed this way, it isn’t a subset at all, but an alternate perspective that only has a pragmatic side if and when the theory is applied. The knowledge embedded in theories thus has two components: (a) the idealization, including objects and rules about them, and (b) the application, including evidence supporting the quality of the correlation between theory and some reality. The latter aspect is entirely pragmatic, but the former is an entirely different kind of knowledge called rationalism, which 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. In science, empiricism is used to refer to a+b, 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 and consequently 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 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. How does knowledge become objective and free of preferences, biases, and fallacies?

Knowledge carries the expectation of a measure of certainty. Objectivity is a way of elevating knowledge to a higher standard, a way of increasing the certainty if you will, by removing any dependence on opinion or personal feelings, i.e. on subjectivity. Science tries to achieve objectivity by measuring with instruments, independently checking results, and using peer review. Statistical knowledge is never certain and is only as good as an observed correlation. But in principle, logical knowledge is always certain because a model’s workings are fully known. But the rub is that we don’t know what models run the physical world, or even if the world actually follows rules. All we know is that it seems to; all evidence points to an exact and consistent mechanism. More importantly, the models we have developed to explain it are very reliable. For example, the laws of motion, thermodynamics, gravity, and conservation of mass, energy, and momentum always seems to work for the systems they describe. But that doesn’t mean they are right; any number of other laws, perhaps more complex, would also work, and the probabilistic nature of quantum mechanics has made it pretty clear that the true mechanics are neither simple or obvious. So logical knowledge can be certain, but how well it corresponds to nature will always be a source of doubt that can be summarized by statistical knowledge.

Logical models also get fuzzy at the edges because if you zoom in you find that the pieces that comprise them are not physically identical. No two apples are totally 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 close enough that models can treat them as interchangeable. Models sometimes fail because the boundaries between pieces become unclear as imperfections mount. Is a blemished or rotten apple still an apple? Is a gun part still a gun part if it doesn’t fit every time? At a small enough scale, our Standard Model of particle physics proposes that all subatomic particles slot into specific particle types (e.g. quarks, leptons, bosons), and that any two particles of the same type are completely identical except for occupying a different location in spacetime6. And maybe they are identical. But location in spacetime is a big wrinkle; the insolubility of the three body problem suggests that a predictive model of how a large group of particles will behave is probably too complex to run even if we could devise one. So both at large scales and small, all models approximate and in so doing they always pick up a degree of uncertainty. But 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.

Our mastery over some areas of science does not grant us full objectivity. Subjectivity still puts the quality of knowledge at risk. This is due to preferences, biases, and fallacies, which are both tools and traps of the mind. Preferences are innate prioritization schemes that make us aim for some objectives over others. Without preferences, we would have no reason to do anything, so they are indispensable, but can lead us to value wishes ahead of a realistic path to attain them (e.g. Gore’s “An Inconvenient Truth”). Biases are rules of thumb that often help but sometimes hurt. Examples include favoring existing beliefs (confirmation bias), favoring first impressions (anchoring), and reputational favoritism (halo effect). They are subconscious consequences of generalization whose risks can be damped through conscious awareness of them. Fallacies are mistakes of thinking that always compromise information quality, either due to irrelevance, ambiguity or false presumption. Biases and fallacies give us excuses to promote our preferences over those of others or over our lesser preferences.

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 Revolutions7, 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!”8. 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.”910. 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 talents11. 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). 12

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.

  1. First formally theorized by Alcmaeon of Croton in the 5th century BC
  2. As a child I always wondered just what, specifically, people were referring to when they said to use your common sense. If nobody had taught it to me, and nobody could describe it, then where did it come from, what was its scope, and could it be trusted? It is no doubt common for children to rebel against exhortations to use their common sense because children question everything, and common sense is all about giving up questioning and just following along like sheep.
  3. Sean Carroll, “Free Will Is as Real as Baseball“, Discover Magazine, 2011
  4. Water doesn’t just mean liquid H2O. Because we now understand that H2O is the principal component of most liquids we would label as water, we have multiple definitions for water, including liquid H2O, any form of H2O, and some liquids containing or resembling water (e.g. certain bodily fluids). Even words with pretty specific physical definitions are often used in more general ways to refer to things sharing many of same properties. It is understood that words, being generalizations, can have a rather broad scope and so context often matters. Diet soda has much less aspartame (0.05%) than seawater has salt (3.5%), but we would never call it water, though we would call seawater “water”. This highlights the importance of function to definitions. Also, words are often used as metaphors for other things entirely, and this kind of usage is not intended to expand the scope of the word but only to bring other thoughts to mind by analogy. For example, water can be used as a metaphor for blandness, dilution or life.
  5. Magritte’s “La Trahison des Images” (“The Treachery of Images”) (1928-9) or “Ceci n’est pas une pipe” (“This is not a pipe”). Sometimes translated as “The Betrayal of Images” by René Magritte, 1898-1967. The work is now owned by and exhibited at LACMA.
  6. 37 particles (counting antiparticles) are considered confirmed (12 quarks, 12 leptons, 13 bosons) and dozens more are hypothesized. See List of particles, Wikipedia
  7. Thomas Kuhn, The Structure of Scientific Revolutions, The University of Chicago Press, 1962
  8. Aaron Souppouris, “Richard Dawkins on science: ‘it works, bitches’“, The Verge, at Oxford’s Sheldonian Theater, 2013
  9. Amos Tversky and Daniel Kahneman, “Belief in the law of small numbers.“, Psychological Bulletin, 1971
  10. Michael Lewis, The Undoing Project: A Friendship That Changed Our Minds, W. W. Norton & Company, 2016
  11. Chomsky, Noam; Skinner, B.F. (1959). “A Review of B.F. Skinner’s Verbal Behavior“. Language. 35 (1): 26–58. doi:10.2307/411334. JSTOR 411334.
  12. Margaret Boden, Mind as Machine, vol 2, Clarendon Press, 2006

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