Deriving an Appropriate Scientific Perspective for Studying the Mind

[Brief summary of this post]

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, from which I will extrapolate to 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. Pragmatism: to know is to predict
4. How does knowledge become objective and free of preferences, biases, and fallacies?
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 it makes sense to think about what we know about the mind from common knowledge, which is the subset of our shared knowledge with which we are most likely to agree. 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 do we collectively think about how the mind works? This brief summary is just to characterize 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 does not hinge on common knowledge.

First and foremost, having a mind means being conscious, which is a first person (subjective) awareness of our surroundings through our senses and an ability to direct our interactions with the world using our body. We know our minds travel with our bodies and we feel that our minds are in our heads because of our eyes and ears and because feeling well in the head correlates to thinking well. We feel implicit trust in our sensory connection to the world, but we also know that our senses can fool us, so we’re always looking again and reassessing. Our sensations, more 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 triggering stimuli. Beyond direct sensing, we can simulate sensing using imagination and thought, but the feel of the qualia is diminished. We can also think about abstract groupings of things or ideas, which we can direct with or without the aid of language (though language provides our strongest connection to many abstract ideas). Thoughts lake the persistent feel of qualia but instead stimulate other, related thoughts. We can think about all the above ideas as abstractions, and also about our bodies and minds (the self). We have an ability called reasoning which lets us think logically about abstractions. We can preserve our interactions with the world and our thoughts in our memory as experience. We develop preferences and develop strategies to satisfy them by learning from feedback. We have free will to direct our thoughts, but only at the top (conscious) level. Thoughts I will call subconscious (because we are not aware of them unless we turn our attention to them) control learned, habitual behaviors. We perform them on “autopilot,” essentially delegating that they should happen with a little conscious oversight but without constant conscious handholding. We can gain conscious access to some subconscious thought processes, but others, like how we remember or recognize things or words just work like magic to us. We have a subconscious gift for juggling many possibilities or possible worlds given just the slightest conscious effort. Some possibilities correspond well to the physical world and others are more imaginary. We think of those that correspond particularly well as being true, which relates mostly to their ability to successfully predict what will happen.

While we build much of our concept of the outside world on the “blank slate” of our minds, which are innately predisposed to make sense of it in human-oriented ways, culture contributes a huge pool of common knowledge. This includes what I am saying here, ideas embedded in words (though language is mostly an innate capacity), and many schools of thought from customs to explanatory theories to science. We gather much of this cultural knowledge from nonverbal interactions with people and artifacts, but most of it comes through language, either conversationally or from books and other media. Our vocabulary neatly divides into physical words about things and mental words about ideas, and never the twain shall meet (though some words can go both ways, like “up” for upward or happy). Physical words describe things and events, covering everything that unfolds in spacetime. All physical words have a kind of mental component to them because we group things and events in the physical world using generalizations that are mental constructions. So how we divide the parts of an object or the steps of a process has a subjective angle to it, but there is no doubt these words refer to the physical world. Mental words cover sensations (see, hear, hunger, feeling, etc.), emotions (anger, fear, feeling, etc.), and thinking (know, believe, like, design, etc.), none of which have physical corollaries. The mental and physical worlds can follow disjoint paths, with mental events developing independently of physical and physical independently of mental, but they can also bring about changes in each other. Our sensations are directly affected by physical changes, our emotions are indirectly affected by our interpretations of physical changes (with many emotional changes under subconscious and hence involuntary control), and our thoughts are also indirectly affected as we have good reason to focus many of our thoughts on our physical circumstances. Our bodies perform actions that cause change to the physical world, and those actions are directed by our mental states. We join the physical and mental worlds through causality; we speak as if events in one world can simply cause events in the other, even though causation itself is a macroscopic generalization with no hard physical definition.

The current state of common knowledge is constantly evolving, and in a society that embraces objectivity, as ours has been doing increasingly for centuries, that change brings rapid progress in the depth and scope of that knowledge. Put approximately, the average educated person of today knows more about psychology (say) than the experts knew a generation ago. Put more accurately, while none of us is fully up to date in all fields, as our collective knowledge across all disciplines grows, our common knowledge (the most broadly shared parts) grows as well. This has led to a deeper shared familiarity of all fields and also to general truths that connect the fields together, many of which are not articulated formally through science. For example, we have a greater appreciation for the interconnectedness of life and both the resilience and fragility of biological systems. As an example regarding the mind specifically, we think more about what shapes our thought processes than people did a few generations back. So instead of just thinking that decisions have a “trust your gut” indivisibility, today we are aware of the impact of biases, data quality, and how to use decision trees and other algorithmic approaches. Beyond awareness, we have internalized political correctness (a term of dubious provenance) to the point where we automatically apply anti-biasing in our thinking and social interactions. We are less innocent and more mature, starting at an ever-earlier age. We know so much more than our forebears that we can’t easily conceive just how much weaker their world view was. I’m not saying we are smarter or more capable; modern conveniences have cost us many of the skills of self-sufficiency and much of the patience for mastery. I’m just saying we carry the burden of more comprehensive knowledge. For this discussion, what matters is that we know more than we think we know: a clear, scientific view of the world supported by common knowledge is right before us, and our bullshit meters are good enough that we can throw out theories that have overstayed their welcome.

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 expert opinion is divided as to whether physicalism is a sufficient ontology to explain the mind. The 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. 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.

Alternatively, we are also familiar with the notion of mentally functional existence, as in Descartes’ “I think therefore I am.” We experience states of mind, feeling and thinking things, and this kind of existence does at least seem to us to be distinct from physical existence because thoughts have no length, width, or mass. Idealism is another monistic ontology, but it asserts that reality is entirely mental, and what we think of as physical things are really just mental representations. In other words, we dream up reality any way we like. Science, and our own experience, have provided overwhelming evidence of the persistent existence of physical reality, which has put idealism in a rather untenable state. But if one were to join the two together, one could 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. And Descartes promoted it, but where he got into trouble was in trying to explain 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 is not the seat of the soul. Natural science gives us no evidence, reason, or inclination to suppose that anything in our brains transcends the rules of spacetime, so we need to try to figure out the mind within that constraint. While Descartes’ substance dualism doesn’t hold up, another form called property dualism has been proposed. Property dualism tries to separate the two by asserting the mental states are non-physical properties of physical substances. This misses the mark as well because it suggests a direct relationship between the mental state and the physical substance, and as we will see it is precisely the point that the relationship is not direct. A third variety of dualism called predicate dualism proposes that intentional predicates like belief, desire, thought, and feeling can’t be reduced to physical explanations. This comes a bit closer to the truth, because these predicates are certainly only indirectly physical, but they are also high-level human skills and not a fundamental kind of existence in their own right. We just need to break them down a bit more to establish a proper basis for dualism.

Let’s consider why any purely physical explanation will come up short. There are aspects of our thoughts that cannot be reduced to the physical because they have no extent in space or time; they are generalizations. “Three” and “red” and “up” are not physical. As Sean Carroll writes,1 “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. All our mental words for sensations, emotions, and thoughts have no physical corollary. Although we can say that any given use of a word or thought at a point in time could be correlated to something about the physical state of the body (and probably the brain if we want to get anatomical), that can never be enough to define them, because their definition is not a function of physical form but of logical relation. It is exactly in the focusing on form and not function that the physical sciences miss the forest for the trees and remain completely unable to see the mind at all. Form and function are differences of perspective that not only explain things in different ways but which cannot be reduced to each other, at least not while retaining a useful explanation. When we start to contemplate function we have actually opened the door to a different brand of existence, that of mental things. They have a kind of physical existence in the brain yet they can’t be understood physically. So what are they then?

The mental world is a product of information. Information, perhaps itself the simplest of mental words, is the patterns hiding in data, the wheat separated from the chaff. The value of these patterns is that they can be used to predict the future. Brains use a wide variety of customized algorithms, some innate and some learned, that find information and use it to turn events to their advantage. Information is not a physical thing because its value is not physical, it is functional. Yes, it is physically encoded as a state in a computer (biological or manmade), or written down, but a physical representation of information isn’t noteworthy in isolation. It is that the information can be put to use that matters, and minds and computers are configured so as to be able to apply the information itself without much regard for the form it takes. What technical trick allows information to behave in this transphysical way? The trick is indirection, known more commonly as representation. The brain can take a stream of data and distill information from it to represent something else, the referent. “Ceci n’est pas une pipe,” as Magritte said. 2
Pipe
A thought about something is not the thing itself. 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? It’s 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 connecting things together, not the things themselves, which are in fact defined only in terms of their connections.

I argue that these two perspectives, physical and mental, are not just different ways of viewing a subject, but define different kinds of existences, different kinds of subjects. Physical things have form, e.g. in spacetime, or in any dimensional state in which they have an extent. Mental things have function. They have no extent, and they can be characterized as relationships between themselves. They include ideas, but they also include mathematics. They are the rules that comprise any formal system. They are also the information comprising a statistical system, which is a degenerate kind of formal system with a lot of data and not much logic. We recognize things subconsciously using statistical matches against lots of stored information (where by statistical I mean finding correlating support from a volume of data as opposed to drawing logical conclusions). Through representation the mental can be used to model the physical, which is to say that it can predict, i.e. “know,” certain things about the physical world. So function is the “substance” of mental things.

On this view, the mind still runs in a brain using entirely physical processes. However, because physical processes can execute indirection, the brain can create abstractions that are not physical themselves which we call concepts, ideas, perspectives, or mental models, and these collectively constitute minds. So an analysis of the physical form won’t explain the function, which has been abstracted away from the physical using physical mechanisms that support indirection. And we do have to keep in mind that scientific theories themselves, and the whole idea of understanding, “exist” in this realm of indirection, where potential physical referents are referred to via generalities with no physical existence themselves. It is an existence worth talking about because mathematical objects and logical concepts, built and manipulated this way through the imagination, support all the mental accomplishments of man.

The two fundamental kinds of existence are therefore form and function. As the distinction of form is implied in the terms property dualism and predicate dualism, I will just call this new ontology form & function dualism. It is a recast of predicate dualism that changes the emphasis from the data itself (predicate) to what the data can do (function), i.e. from intentional states to information, whose existence derives from its value in prediction and not in the data itself. While the brain and all the processes within it are entirely physical, the function of the brain is something else, an alternative kind of existence (i.e. the mind) that in no way undermines the physical nature of the brain, but which does empower the brain to do things it could not otherwise do. Form & function dualism merges physicalism with idealism, eliminating the “only” requirement from each: form and function don’t reduce to each other, they coexist. Function exists regardless of whether it is ever implemented because function is abstract and its existence doesn’t depend on whether a physical brain or computer thinks of it. However, as physical creatures, our access to function and the ideal realm is strictly limited by the physical mechanisms used to implement abstraction in our brains. We can, in principle, build better computers and minds that can do more, but they will always be physically constrained, and so we will always have limited access to the infinite ideal domain.

Physicalism is not exactly wrong on its own: a purely physical explanation does exist for every physical phenomenon. However, even the concept of a phenomenon or its explanation comes from the ideal world, as is our own existence as mental beings, so physicalism without idealism won’t get us very far. And besides that, one can’t physically explain animal behavior or human civilization; one must resort to functional explanations. Idealism, too, is not exactly wrong on its own: idealizations exist regardless of whether the physical world does. But for people, at least, the connection back to the physical world matters. We can’t conceive of a purpose of a pure existentialism independent of our physical existence. Even if we someday live simulated lives in virtual reality, and leave it to others to run the computers and pay the electric bill, those lives will simulate physical lives. I guess it comes down to having something to do. For people this means using our minds to control things, which is to say using our predictive abilities in an environment that challenges them. It is the purpose of our minds, and all potential minds, to manage information for functional purposes, even imaginary mathematical minds with no concept of physical existence.

The rise of physicalism has made us blind to idealism, even though science is built on ideas. The most physical of sciences — physics and chemistry — aim to be purely physical, and yet they invoke laws that are pure function with no form. Laws are predictive of natural phenomena but are not physical themselves, yet we seem unconcerned that physicalism has no place for them. We aren’t concerned about this gap so long as the subject doesn’t come up. The phenomena described by physics and chemistry have no functional aspect themselves, so our laws don’t overlap with what they describe. But everything beyond physics and chemistry is biological, and biological systems manage information and hence have an ideal or functional aspect which physicalism alone can’t explain. So we have to embrace form & function dualism to discuss biological systems; we need to consider what purpose genetic traits serve. And it is not just genetic; life encodes information in three reservoirs: genes (DNA), memory (neurons), and culture (artifacts, both physical and institutional). Memory is impossible without genes, and culture is impossible without genes and memory. Through feedback our genes and memory are also shaped over time by memory and culture, so the three influence each other in some ways. Of course, science has addressed these things, hence the biological and social sciences. But under what ontology? I propose the denigration of dualism has left them ontologically limited: they speak to biological or social systems without asking too many questions about what kinds of existence they are addressing. But this reticence becomes crippling as soon as we try to understand the mind itself. At every turn we face questions of both form and function, and so long as we overlook these ontological shortcomings we won’t be able to build an overall theory.

So the love-hate relationship that philosophy and science have had with dualism was just a misunderstanding. Physicalism and idealism have never been at odds with each other. Information processing is possible in the physical world, but the information processed is not physical and can’t be understood in physical terms. The brain is physical but the mind is mental. Unless and until one looks to the functional side of the mind one can’t explain it, which is to say one can’t predict how it will behave. So to move into biology or social science one must expand one’s ontology to include information, the functions it performs, and the ways it is processed. Form & function dualism gives cognitive science the clear ontological basis it needs to unify the sciences. Form is noumenal but known to us through observation (phenomena), and function is purely the generalization of behavior and properties of phenomena with the goal of separating predictive information from noise.

3. Pragmatism: to know is to predict

Given that we agree to break entities down into 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 or knowledge differs from noise to the extent that it can be used, i.e. for prediction. The ability to assist in prediction is nothing like the certainty of mathematics. It can be helpful but can never prove anything to be true.

Pragmatism takes a hard rap because it carries the heavy connotation of compromise. The pragmatist has given up on theory and has “settled” for the “merely” practical. But the goal of theory all along was to explain what really happens. 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 fail to live up to the ideal model. After all, the real world is full of impurities and imperfections that throw off simple equations. Presumably, if one took all the actual circumstances into account and had an appropriate way to model them theoretically then the theory would accurately predict the future. But it is often not practical to take all circumstances into account, and in these situations one can either settle for the inexact or inappropriate results of theory or employ other methods. The pragmatist, whose goal is to achieve the best prediction possible, will combine all available approaches to do it. This doesn’t mean giving up on theory; on the contrary, a pragmatist will prefer well-proven theory to the limit of practicality, but will then supplement using the pre-theoretic analysis of the relevant data using innate algorithms and/or learned reasoning skills, possibly leading to ad hoc theories or new formal theories. One might say one falls back on hunches and half-baked theories to fill in the gaps. It sounds haphazard, but our subconscious minds are great at these kinds of analyses, and we could not build our formal understandings without a rich network of this kind of informal support. Pragmatic often carries the connotation of favoring practical over theoretical because it generally goes unspoken that pragmatism applies where theory is unavailable. Note that, ironically, much of my task of explaining the mind is to develop a formal theory that will describe how we use pre-theoretic methods to think. I’m getting ahead of myself here because I haven’t formally described what a formal theory even is, but the common meaning is clear. But what I have said so far about the mind is that it manages information pragmatically, so it stands to reason that our approach for studying the mind should also start with pragmatism and refine from there to formal theory only as it proves appropriate.

Thinking is an effort to find information hiding in data. When a pattern emerges, we will call it a hunch, opinion, or theory if it seems uncertain, or knowledge, truth, or a fact if it does seems certain. Our primary basis for this categorical distinction is whether we have processed the pattern statistically or logically. Statistical patterns are supported purely by a predominance of the data. They can’t be certainties but do provide a statistical advantage over noise (data without pattern). Logical patterns are necessarily true because they follow from the rules of a formal system, aka a model. A model defines terms, how they are related, and rules of entailment from which one can connect causes to effects. If defined clearly enough, everything within the system is known and so is factual. Within the system, everything that is true is necessarily true; there are no doubts. Mathematics and physics define such logical systems. The standard rules of arithmetic and the Standard Model of particle physics are examples, and within these systems, one can know things with certainty. From the infinity of possible formal systems we choose systems that we find most helpful, often using Occam’s razor to pick the simplest (“Among competing hypotheses, the one with the fewest assumptions should be selected”). Does thinking really break down into either statistical or logical pattern analysis? Yes, it really does, but there is a lot of overlap between them, and the kind of logic we do in our minds is much more fluid than that of scientific formal systems. I will be discussing these two approaches in much more detail further on.

Because pragmatism is entirely reflective of the function of the mind, being the perspective of the practical effects of our ideas, it is a comprehensive epistemology for the mental world. Since our understanding of the physical world, i.e. physicalism, is also knowledge based on information, pragmatism must also be a comprehensive epistemology for the physical world. In other words, having a non-pragmatic knowledge of physical things would be nonsensical. But contrast it with rationalism, which holds that reason (i.e. logic) is the chief source of knowledge. Rationalism captures the logical side of knowledge but ignores knowledge gathered from statistical processes. So rationalism is a proper subset of pragmatism and so it is necessarily weaker. Also note that since statistical knowledge is vital to the mind, rationalism is overly glorified as the ultimate epistemology when it only solves half the problem. Also contrast pragmatism with empiricism, which holds that knowledge comes only or primarily from sensory experience, that is, what can be supported by experimental evidence. Empiricism has become the de facto epistemology of science because our ability to make physical predictions necessarily hinges on relevant physical evidence. The scope of empiricism includes all data we acquire about the physical world, both in statistical or logical form, and does not preclude either statistical or rational (logical) use of that data. But while it has put science on a firm footing and has stood the test of time, empiricism is an inadequate epistemology because it ignores the existence of mental things. One can’t gather physical evidence of mental phenomena; one can only gather implementation details (e.g. neural wiring, equivalent to code and memory dumps). The physical evidence can reveal the form but never the function. This is enough to study systems that don’t leverage information (e.g. pre-biological physics and chemistry) but leaves us out in the cold in the study of systems that do manage information. Pragmatism is the epistemology that will get us there. So empiricism is a proper subset of pragmatism for dealing with observable phenomena, but again solves only half the problem. In practice, what does pragmatism bring to the table that rationalism and empiricism don’t? When we look at genes, memory, and culture, we learn nothing about the function from studying the physical structure, so we need to go beyond the evidence to consider the purpose. That a gene makes a bone a certain length or strength doesn’t matter; what matters is that these traits have a time-tested general value for survival. This is information you could just never guess from physical considerations alone, even though it is encoded in the system using physical mechanisms in DNA and proteins. And we can’t limit our reasoning to provable logical consequences because we can never collect all the factors that caused them to develop the way they did and fit them into a strict formal model, so we need statistical reasoning.

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 spacetime3. 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 Revolutions4, 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!”5. 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.”67. 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 talents8. 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). 9

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. Sean Carroll, “Free Will Is as Real as Baseball“, Discover Magazine, 2011
  2. 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.
  3. 37 particles (counting antiparticles) are considered confirmed (12 quarks, 12 leptons, 13 bosons) and dozens more are hypothesized. See List of particles, Wikipedia
  4. Thomas Kuhn, The Structure of Scientific Revolutions, The University of Chicago Press, 1962
  5. Aaron Souppouris, “Richard Dawkins on science: ‘it works, bitches’“, The Verge, at Oxford’s Sheldonian Theater, 2013
  6. Amos Tversky and Daniel Kahneman, “Belief in the law of small numbers.“, Psychological Bulletin, 1971
  7. Michael Lewis, The Undoing Project: A Friendship That Changed Our Minds, W. W. Norton & Company, 2016
  8. 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.
  9. Margaret Boden, Mind as Machine, vol 2, Clarendon Press, 2006

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