The Science of Function

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

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

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

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

Given these caveats about how science can only characterize subjects and can’t reveal their true nature, let’s take a look at which sciences study functional subjects. Viewed most abstractly, science divides into two branches, the formal and experimental sciences. Formal science is entirely theoretical but provides mathematical and logical tools for the experimental sciences, which study the physical world using a combination of hypotheses and testing. Testing gathers evidence from observations and correlates it to hypotheses to support or refute them. Superficially, the formal sciences are all creativity while the experimental sciences are all discovery, but in practice most formal sciences need to provide some real-world value, and most experimental sciences require creative hypotheses, which are themselves wholly formal. Experimental science further divides into fundamental physics, which studies irreducible fields and/or particles, and the special sciences (all other natural and social sciences), which are presumed by materialists to be reducible to fundamental physics, at least in principle. Experimental science is studied using the scientific method, which is a loop in which one proposes a hypothesis, then tests it, and then refines and tests it again ad infinitum.

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

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

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

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

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

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

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

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

Subconcepts. Before we get to subconcepts, let’s take a look at percepts. Percepts are the sensory feelings that flow into our minds continuously from our senses. The five classic senses are sight, hearing, taste, smell, and touch. Sight combines senses for color, brightness, and depth to create percepts about objects and movement. Smell combines over 1000 independent smell senses. Taste is based on five underlying taste senses (sweet, sour, salty, bitter, and umami). Hearing combines senses for pitch, volume, and other dimensions. And touch combines senses for pressure, temperature, and pain. Other somatosenses include balance, vibration sense, proprioception (limb awareness), hunger, erogenous sensation, and chemoreception (e.g. salt, carbon dioxide or oxygen levels in blood). Awareness and attention themselves have a feeling of time and space. We feel all of these things without reflection; they are immediate and hard-wired subconscious mechanisms that bring external information into conscious awareness.

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

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

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

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

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

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

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

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

  1. Sadly, it doesn’t work very well in practice because the peers who do the reviewing are not paid, and so they take too long and often don’t find the problems they should find.
  2. Kahneman and Tversky’s 1971 paper “Belief in the law of small numbers” will be discussed in the next chapter.
  3. The solution is to improve science by using science: if scientists can show that science can only work if the scientific method is not corrupted by partiality, then the partial forces who control science will let science be objective.
  4. The 7 biggest problems facing science, according to 270 scientists, Julia Belluz, Brad Plumer, and Brian Resnick , Vox, Sep 7, 2016
  5. Alex Byrne, Inverted Qualia, Stanford Encyclopedia of Philosophy, 2015

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