2.4 Civilization: 10,000 years ago to present

The Origins of Culture

Human civilization really began at about the same time that the human species started developing noticeably human traits several million years ago. For our purposes here, I am going to define civilization in terms of the presence and reuse of informational artifacts. An artifact is a pattern not found in nature, or, more accurately, a pattern created by cognitive processes using real-time information. In other words, we can exclude instinctive behavior that “seems” arbitrarily clever but is not a customized solution to a specific problem. Language is certainly the most pervasive and greatest of the early artifacts of civilization. For language to work, patterns must be created on a custom basis to carry semantic content. Humans have probably been creating real-time semantic content using language for millions of years, as opposed, for example, to genetically-driven warning calls. We have no real evidence of language or proto-language from back then; the first artifacts from which a case for language can be made before written languages are about 100,000 years old, but I think language must have evolved rather steadily over our whole history.12 Homo erectus used a variety of stone tools, and probably also non-stone tools3, and was able to control fire about a million years ago. This suggests early humans were learning new ways of getting food that had to be discovered and taught, and was thus able to expand into new ranges. Huts have been found in France and Japan that date back 400,000 to 500,000 years.

While we can’t say just how capable very early humans were, by about about 40,000–50,000 years ago humans had achieved behavioral modernity. While culture may take thousands of years to develop, it seems likely that some genetic breakthroughs facilitated later advancements. That said, I suspect that most people born in the past 100,000 years could probably pass for normal if born today. After all, all living races of humans alive today seem cognitively equivalent, despite having been separated from each other for 10,000 to 40,000 years. The range of human genes produces people with a range of intelligence which has gradually increased, slowly pushing up both the normal and the genius ranges. So rather than a night-and-day difference between early and modern man, we will see a shift in the bell curve to greater intelligence. But whatever mix of genes and culture contributed to it, we usually demarcate the dawn of civilization at about the 10,000-year point because that is about when the first large civilizations seem to have arisen.

According to the evidence we have, the large-scale domestication of plants and animals did not begin until about 12,000 years ago in Mesopotamia, although the Ohalo people of Israel were cultivating plants 23,000 years ago. This suggests that small-scale domestication may go back much further. Beyond Mesopotamia, ancient India, China, Mesoamerica, and Peru formed independent cradles of civilization starting around 7,000 to 4,000 years ago. These civilizations collectively comprise the Neolithic or Agricultural Revolution as they were founded principally on the stability of agricultural living and the cultural trappings that accompany it.

The Cultural Ratchet

A great deal has been made in recent years about the significance of memes to culture. While the word is most widely used now to refer to the catchiest of ideas, the idea that informational artifacts can be broken down into functionally atomic units called memes can be useful for discussing the subject. After all, if culture has a ratchet, then there must be some teeth (memes) that click in that don’t want to slide back. The only protection culture has from sliding back is memory; we have to pass our culture on or it will be lost. Every idea continuously mutates and is held in different ways by every person, but culture critically depends on locking gains in through traditions, which standardize memes. If I had to list key memes in the development of civilization, aiming for a high level of summarization, I would start with the idea of specialized tasks, especially using the hands, which are central to nearly every task we perform. The sharing of tasks brought about the development of language. These two metamemes drove cultural progress for the first few million years of our evolution in countless ways that we now take for granted, but they completely overshadow everything we have done since. Long before the establishment of civilizations, people made many hand tools from stone, wood, and bone. People also learned to hunt, control fire and build shelters, and they developed art and music. All of these things could be taught and passed down through mimicry; the power of language to describe events probably emerged only very gradually. An era of cognition in which people were learning to think about things likely preceded our era of metacognition in which reflections pervade most of our waking thoughts. As our innate ability to think abstractly gradually improved, mostly over the past million years, language also kept up and let us share new thoughts. Genes and culture coevolved, with the bell curve starting to overlap our present-day capacities between 200,000 and 50,000 years ago.

It becomes easier to call out the specific memes that were the great inventions of early civilization. Agriculture and the more sedentary and community living that it brought is usually cited first. Other key early physical inventions of early civilizations notably include textiles, water management, boats, levers, wheels, and metalworking, but they also depended on the purely functional inventions of commerce, government, writing, and timekeeping. Some of the most prominent physical inventions of modern civilization include gunpowder, telescopes, powered industrial machinery (first with steam, then with gas), electricity, steel, medicine, planes, and plastic. And increasingly relevant to technological civilization are physical inventions that manage information like the printing press, phone, television, computer, internet, and smartphone. And perhaps most relevant of all, but often overlooked, are the concepts we have invented along the way, which from a high level in roughly chronological order include math, philosophy, literature, and science. Within these academic disciplines exist countless specialized refinements, creating an almost uncountably large pool of both detailed and generalized memes.

All of these products of civilization are memes struggling to stay relevant to survive another day. They all roughly have a time of origination and then spread until their benefit plateaus. But they also often have multiple points of origin and evolve dramatically over time, making it impossible to describe them accurately using rigid buckets. (Internet memes and fads are memes that spread only because they are novelties rather than providing any significant function. Ironically, it is often their abundant lack of functionality that drives fads to new heights; this perversion of the cultural ratchet is funny.) So while we can equate genes to memes as units that capture function, genes are physically constrained to a narrow range of change (though any one mutation could make a large functional difference), but memes can be created and updated quickly, potentially at the speed of thought. The cognitive ratchet improved our minds very quickly compared to the scope of evolutionary time but was still limited to the maximum rate of evolution physically possible. But the cultural ratchet has no speed limit, and, thanks to technology, we have been able to keep increasing the rate of change. Like the functional and cognitive ratchets before it, the cultural ratchet has no destination; it only has a method. That method, like the other ratchets, is to always increase functionality relative to the present moment as judged by the relevant IPs. The functional ratchet of genes always fulfills the objective of maximizing survival potential, but the cognitive ratchet maximizes the fulfillment of conscious desires. Our conscious desires stay pretty well aligned with the goal of survival because they are specified by genes that themselves need to survive, but, just as no engine is perfectly efficient, no intermediate level can exactly meet the needs of a lower level. But our desires do inspire us pretty effectively to stay alive and reproduce. Though we tend to think of our desires as naturally meshing well with the needs of survival, the feedback loops that keep them aligned can also run amok, as is seen with Fisherian runaway, for which excessive plumage of the peacock is the paradigmatic example. This effect doesn’t override the need to survive, but it can amplify one selection pressure at the expense of others. Could some human traits, such as warlike aggression, have become increasingly exaggerated to the point where they are maladaptive? It is possible, but I will argue below that it is much more likely that human traits have been evolving to become more adaptive (by which I mean toward survival). But if we act to fulfill our desires, and our desires are tuned to promote our survival, do we deserve credit for the creation of civilization, or is it just an expected consequence of genetic evolution? The surprising answer is yes to both, even though they sound like opposing questions. Even though civilization in some form or other was probably inevitable given the evolutionary path humans have been on, we are entirely justified in taking credit for our role because of the way free will and responsibility work, which I will cover in Part 4.

From the beginning, I have been making the case that we are exclusively information processors, meaning that everything we do has to have value, i.e. function. The practical applications of cooperation and engineering are limitless and reach their most functional expression in the sciences. Science is a cooperative project of global scope that seeks to find increasingly reliable explanations for natural phenomena. It is purely a project of information processing that starts with natural phenomena and moves on to perceptions of them which, where possible, are made using mechanical sensors to maximize precision and accuracy and minimize bias. From these natural and mechanical perceptions and impressions, scientists propose conceptual models. When we find conceptual models that seem to work, we not only gain explanatory control of the world, but we also get the feeling that we have discovered something noumenal about nature (although our physical models are now so counterintuitive that reality doesn’t seem as real anymore). But in any case, our explanatory models of the physical world have enabled us to develop an ever-more technological society which has given us ever-greater control over our own lives. They have fueled the creation of millions of artifacts out of matter and information.

I have said little about art, but I am not going to make that case that it is highly functional. Art is fundamental to our psychological well-being because beauty connects knowledge. It pulls the pieces of our mind together to make us feel whole. More specifically, the role of beauty is to reinforce the value of generalities over particulars. The physical world is full of particulars, so our mind is continuously awash in particulars. And we already have great familiarity with most of the particulars we recognize. When we are very young, everything is new and different, and we are forming new general categories all the time, but as we get older, everything starts to seem the same or like a minor variation of something we have seen before. We can’t stop knowing something we already know, but we need to stay excited by and engaged with the world. This is where art comes in. A physical particular, by which I mean its noumenon, is mundane (literally: of the world), but generalities are sublime (literally: uplifted or exalted) or transcendent (literally: beyond the scope of), both of which suggest an otherness that is superior to the mundane. Thus art is sublime and transcendent because it is abstract rather than literal. While any physical particular is only what it is, and so can be reduced to hard, cold fact, our imagination is unlimited. We can think about things from any number of perspectives, and we do like to engage our imagination, but not without purpose. To better satisfy the broad goal of gratifying our conscious desires, we have to understand what we want. We can’t depend on raw emotion alone to lead the way. So we project, we dream, we put ourselves in imaginary situations and let ourselves feel what that would be like. The dreams we like the best form our idealized view of the world and are our primary experience of art. We all create art in our minds just by thinking about what we want. For any object or concept, we will develop notions about aesthetic ideals and ugly monstrosities. Although the world is ultimately mundane and becomes increasingly known to us, the ways we can think about it are infinitely variable, some of which will be more pleasing and some more displeasing.

When we produce art physically, we give physical form to some of our idealized notions. The creation is a one-way path; even the artist may not know or be able to reconstruct the associations behind each artistic choice, but if they are good then many of their considerations will resonate with our own ideals. When we appreciate art, we are not looking at physical particulars, we are thinking about ideals or generalities, which are both the groupings in which we classify particulars and the way knowledge is interconnected. Generalities are all about patterns, which is the substance of information at both a high and a low level. Patterns of lines and colors charm or repel us based on very general associations they strike in us, which are not random but link to our ideals. Art can be very subtle that way, or it can directly reflect our strongest desires, for example for sex, fun, and security. By helping us better visualize or experience our ideals, art helps us stay interconnected and balanced and prioritize what matters to us. Art lets us know that the representational world in our heads is real in its own right, that our existence depends as much (or more) on generalities as it does on particulars. So its benefit is not direct; by glorifying patterns, ideas, and abstractions for their own sake, art validates all the mental energy we expend making sense of the world. Its appeal is both intellectual and visceral. Art stimulates the gratitude, interest, and enthusiasm, emotions which keep us engaged in life. Art seems inessential because its value is indirect, but it keeps our cognitive machinery running smoothly.

In summary, while we could view civilization as a natural and perhaps expected consequence of evolution, we have expended great conscious effort creating the arts and sciences and all the works of man. Before I can consider further just how much credit we should give ourselves for doing all that, I need to dig a lot deeper into our innate human abilities. In Part 3, I will look in more detail at the evolution of our inborn talents, from rational to intuitive to emotional. Then, in Part 4, I will look at the manmade world to unravel what we are, what we have done, and what we should be doing.

Part 3: Strategies of Control

I’ve spoken at a high level about the functional drivers that caused life, minds, humans, and civilization to arise, and I have implied that where there is a will, there is a way, which suggests a certain inevitability of this progression. But that isn’t really true. Function does drive form, but actually, where there is a will and a way, there is a way. Functions require both a physical and logical strategy to arise. Since we already exist, we know we are both physically and logically possible, so all we have to do is figure out the mechanisms. For most evolved functions, the logical requirements of the function can be readily worked out, and the physical mechanism that supports it can then be studied in detail to give us a fairly complete picture. However, because the functions of the mind are so indirectly connected to physical behavior, we haven’t really been able to work out the logical requirements to much detail, and we can only speak very approximately about what the brain is doing. In this part of the book, I am going to try to unravel the logical strategies the mind uses to achieve the functions we observe. Our current physical knowledge of the brain its chemistry provides only rough hints, so I am going to approach this almost entirely with arguments from functionality we observe and evolutionary needs.

The most celebrated and significant capacity of the mind is neuroplasticity, the ability neural connections to self-organize to develop competence at tasks in response to training with feedback. We do know from physical studies that however plastic any given area of the brain could be, many areas perform the same kinds of work in all people. Most notably, the sensory cortex is prewired to receive neurons from sense organs to specific regions, most notably for sight, hearing, smell, taste, and touch. Touch maps each part of the body to corresponding areas of the primary somatosensory cortex1 The retina maps to corresponding areas of the visual cortex. The right hemisphere controls sight and touch for the left side of the body and vice versa. We don’t really know how neuroplasticity works, but we do from many studies of injuries that many functions localize in small areas or sets of areas and that other parts of the brain can develop these functions if the original areas are damaged. The areas of the brain that receive and send signals to the body tend to be the same areas where specialized control of those areas develops. Adjacent to primary sensory areas are secondary or higher-order sensory areas that integrate the information further. The rest of the cerebral cortex is sometimes called the association cortex to highlight its role in drawing more abstract associations, including memory and thought.

While we don’t know much about the physical mechanics of neuroplasticity, to a rough approximation we believe they detect and reinforce patterns, allowing them to create and process information in a general way that takes patterns into account at any level in proportion to their relevance. I have said from the beginning that the intuitive mind uses this basic approach. I am also saying that everything from sensory perception to high-level deduction is based principally on this strategy. On the plus side, we can use this idea to explain how all the functions of the mind arise: they develop a deeply intricate network of checks and balances that tends to produce useful results. But on the minus side, this perspective blocks us from being able to develop a more granular perspective on what is happening. Sure, the functionality near the input and output nerves specializes for the relevant senses, but everything else quickly develops into a gray mass of overlapping functionality. The truth is, to a large degree, all of brain functionality is a gray area and I won’t be able to explain it because it varies in everybody for innumerable reasons tied to our specific experiences. In other words, the wiring diagrams for the neurons of any two people will be wildly different and you could study them till the end of time and not be able to correlate them to each other. But there are macropatterns, which are strategies of control that have operated from a high level to create both natural and cultural techniques now used by all people. Because these macrostrategies all have microdifferences, we have to expect to find limits to how granular we can get, but we do need to know these broad strategies. We need to know because they are what make us tick.

Before I get into the strategies of control used by minds, let’s dial back to the concept of control itself. Unlike rocks, which are subject to the vagaries of the elements, living things maintain their integrity to persist indefinitely by exercising control. They don’t persist by retaining the same atoms indefinitely. The ship of Theseus is a thought experiment that asks whether a ship in which every piece has been replaced is still the same ship. Physically, it is not the same, but it has maintained its integrity functionally. A functional entity exists based on our expectations of it. It is fair to label any collection of matter, whether living or dead, as a persistent entity if it only undergoes small, incremental changes. Maintenance results in physical changes while upgrades and downgrades also produce functional changes. The maintenance of metabolism replaces about 98% of the atoms in the human body every year, and nearly everything every five years.23 Nearly all of this maintenance has no functional effect, however, over their lifespan organisms mature and then decline with appropriate functional transitions. Minds, and especially human minds, can learn continuously, effectively producing nonstop functional upgrades. We also forget a lot, resulting in functional downgrades.

Living things functionally persist because they are regulated by a control mechanism. Simple machines like levers, pulleys, and mathematical formulas use feed-forward control, in which a control signal that has been sent cannot be further adjusted. Locally, most physical forces usually operate by feed-forward control. Their effects cascade like dominoes with implications that cannot be stopped once set in motion. But some forces double back. A billiard ball struck on an infinite table produces feed-forward effects, but once balls can carom off bumpers then they come back to knock other balls. These knock-on effects are feedback, and feedback makes regulation possible. Regulation or true control uses feedback to keep a system operating within certain patterns of behavior without spiraling out of control. A regulating system monitors a signal and applies negative feedback to diminish it and positive feedback to amplify it.

We tend to think of control as changing the future; it doesn’t actually do that. All natural causes are deterministic and so must happen exactly the way they do at each point in space. Billiard balls are still just knocking into each other, even if some bounce back. Choices, if they exist, cannot not be creating an alternate future. So what is happening? Feedback-based selection events are just using information from past patterns to regulate future patterns. Physically, the patterns, and hence the information, don’t exist. They are mathematical constructs that characterize similarity (actually they are functional constructs that could be represented mathematically, but the mechanisms used by living things are hard to reduce to math). Both the creation of information and its application back to the physical world are indirect, so no amount of physical understanding of the mechanism will ever reveal how the system will behave. Despite this, it remains completely deterministic. Internally, the information processor (IP) follows feed-forward logic using physical parts, just like everything else in the universe. The fact that much is fed back through the hopper again doesn’t change that. But because the IP has separated the control components (the information and information processing) from what is being controlled using layers of indirection, physical laws have lost their opportunity to explain how the system will behave; we need a different way to connect cause to effect.

This is why I have proposed functional existence. Physical and functional existence are not about what is noumenally present, although we can talk about their noumena. What they are really about is providing explanatory power. Physical laws are a practical way to explain phenomena that are subject to feed-forward control at a macro level, and functional laws are a practical way to explain phenomena that are subject to feedback control at a macro level. At a micro level, billions of gas particles bounce off each other, with many feedback effects, but because their behavior is uniform, macro-level gas laws explain their behavior quite adequately. Gas laws don’t actually make any physical sense; “pressure” is a statistical, informational phenomenon. But because of their uniformity, we can treat them as physical laws at the macro level. Conversely, at a micro level, genes encode proteins which may catalyze specific chemical reactions, all feed-forward phenomena. But knowing this sequence tells you nothing about why it happens; at the macro level you need to invoke natural selection to develop any explanatory power, and natural selection speaks to the function of genes and proteins.

It is a lucky thing for us that feedback control systems are not only possible in this universe, but that they can also self-organize and evolve under the right conditions. Living things are holistic feedback systems that refine themselves through a functional ratchet. Life didn’t have to choose to evolve; it just followed naturally from the way feedback loops developed into IPs that could refine their own development. While we don’t know in how many places life has evolved in the universe, it may have been inevitable given the environmental conditions on Earth. Quantum uncertainty creates the possibility that any given atomic or molecular interaction could play out differently despite adhering to physical laws, and this means that life may have depended on some “lucky breaks”. While I know we can’t predict quantum events, I don’t think the multiverse interpretation that every quantum event goes both ways, splitting the universe, is correct. It goes beyond the scope of this book, of my knowledge, and probably of anyone’s knowledge, to say what is correct, but all the evidence above the quantum level supports the idea of a strictly deterministic universe that precisely follows physical laws. If quantum events could always go both ways, I don’t think any of our macro-level physical laws would work because they depend on probabilities that are not equally likely. For example, it is very unlikely that a proton will decay (nearly all will outlive the universe), but the multiverse hypothesis forces us to split the universe every nanosecond for every proton to account for its possible decay. Perhaps a probabilistic multiverse hypothesis that splits universes “unevenly”, resulting in both likely and highly unlikely universes, could make sense if we could wrap our heads around the idea that one universe could exist “more” than another. In any case, we live in an extremely certain universe where our macro-level physical laws always hold, and in that universe, we can probably conclude that life was inevitable. That said, how it evolved could not be predicted without a computer bigger than the universe itself.4 The exact conditions at each moment contributed to the tree of life that emerged, which would be quite different on every other planet where life evolved (barring some universal supersymmetry not yet observed).

Whatever the laws of physics turn out to be (as we improve them over time), for all practical purposes, we can safely presume that both feed-forward and feedback control systems are entirely deterministic, meaning that physically the inputs completely determine the outputs. This is not the case functionally, because when we think about things functionally, the meaning of “the inputs” and “the outputs” changes. Specifically, they change from being exact conditions to being approximate conditions or descriptions of similar conditions. Instead, we would say that, with a given set of inputs, a functional system will choose between a variety of outputs. Because the inputs are interpreted functionally, this means they are generalized to describe a wide range of possible physical circumstances (if, indeed, they refer to physical circumstances at all), and that means that they can then connect those inputs to outputs using a choice algorithm. The choice never had anything to do with an exact physical situation; it was about degrees of similarity to physical situations. The algorithm itself is also entirely deterministic, but, interestingly, if it is running in a human brain, it has the potential to be continuously updated as new information, including information generated by the algorithm itself, arrives. You could say the result is the “illusion of free will”. Physically, it is an illusion because the physical outcome was essentially preordained. But functionally, the outcome was unpredictable and could only be said to be known once the algorithm reached a point where it had decided and acted. Before that point, it was essentially free, because it was subject only to its own internal algorithms, which can’t be superseded by external forces. A freely-operating mind identifies with and takes ownership of its own internal algorithms because that identity defines it. Dissociative identity disorders and natural limitations of the mind to manage its own algorithms place limits on the effective degree of this freedom. For example, people can easily be manipulated, which can undermine their free will.

Where physical systems work in a feed-forward way through a sequence of causes and effects that cascade, information feeds back through functional systems where it is interpreted, leading to decisions whose effect is to make things happen that are similar to things that have happened before. We call this decision-making or choice, but the only choices are relative to hypothetical situations leveraging the idea of similarity. In making such a choice, the desired effects then trigger causes that have produced similar effects. The cart pulls the horse. IPs predict; they know things about effects before they happen. Life does evolve by trial and error, but it is a mistake to think this means the trials are simply random. Yes, they must have been random at the start, and all received theories of evolution still hold that they remain entirely random, but I think this is naive. Just as inductive reasoning weighs prior evidence to make “educated” guesses, so too has evolution probably been tipping the scales in its favor almost from the beginning, becoming increasingly “educated” in ways that make useful mutations more likely to occur, an idea I discussed before. The key takeaway is that in both small and big ways, feedback can lead to “clever” behavior because it can “figure out” how to make a system do things that would never have happened by chance. No magic is necessary; life just has a knack for holding on to techniques that work in the hopes they will work again.

Living things manage information that impacts the long-term survival of the gene line using DNA, while minds manage information that impacts the survival of the individual through neurochemistry. Minds depend on gene-line information as well, so they integrate both kinds of information. The feedback of natural selection causes genes to evolve so as to improve the chances of survival of a gene line, most locally from the perspective of a direct line of descent of an organism, but also to lesser degrees to its tribe and symbiotes. Genes are kept, discarded, and used strictly on the basis of their value to survival. Every mind evolves over the course of its own lifetime using a natural selection of information which comes to use useful information more and harmful information less. Mental information is kept, discarded, and used based in part of its value to survival and in part on its value to the individual. Its value to survival is only relevant to the extent that a genetic trait that affects mental information management can be selected, but we have many innate mental talents which genetic natural selection created. Our capacity to keep, discard, and use mental information is itself mostly innate, but we have also developed manmade techniques. Manmade techniques are only found in the individuals and societies that created and shared them. In this book, I am only going to focus on innate information, i.e. on explanations of minds in general rather than specific minds.

Deriving an Appropriate Scientific Perspective for Studying the Mind

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

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

1. Our Common Knowledge Understanding of the Mind

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

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

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

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

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

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

2. Form & Function Dualism: things and ideas exist

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

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

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

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

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

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

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

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

Pipe

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

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

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

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

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

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

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

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

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

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

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

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

4. What Makes Knowledge Objective?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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