Practice

if you ask a chatbot like ChatGPT a question, it will answer quickly and probably correctly. If you

ask it many questions, it will seem to recall everything you told it earlier. If you challenge any of its

answers, it may report, in fluent sentences, that it finds your response frustrating. Plainly a great

deal is happening behind the screen. But is any of it understanding? Does the program want

anything? Does it feel anything? Or is it an intricate mechanism arranging the right words in the

right order, with no one home inside? You may have a firm intuition here. Consider, how you would

go about proving it to someone who disagreed?

Now ask the same kind of question about yourself. Take a single moment of fear. You might be

out hiking in the forest when suddenly you notice a large animal moving nearby in your peripheral

vision. In that instant your heart pounds. Adrenaline pours into your blood. A small structure buried

in the brain called the amygdala drives the whole cascade. That is one description of the moment. It

is a completely physical description. There is a second description we can use. The fear feels like

something. There is the lurch, the jolt, the urgent wish for it to stop. Both descriptions are about the

very same instant. Are they two different accounts of one event? Or one event rendered in two

languages? And would the experience be different, in principle, if the system doing the fearing were

built from silicon instead of cells? Or if it were built from a brain organized very differently from your

own?

These puzzles are old, but they are not solved or trivial. A lot is at stake based on how we

answer them. In artificial intelligence, if a machine grows fluent, does it thereby have a mind? And if

so, what kind? In ethics and law, we hold people responsible for what they do. Could we ever hold a

machine responsible, and what would have to be true of it before that made any sense? In

neuroscience, what should even count as a good explanation of a thought or a feeling? In

psychology, can mental notions like belief and desire be translated into the language of biology? Or

do they name something biology will never fully reach?

Two general questions sit underneath all of these, and this module is built around those

questions. The first is, what is the relationship between the mind and the brain? The second is, what

would it take for something that is not a human brain (whether an animal, an alien, or a machine) to

have a mind? These are not two separate subjects. The second question is a version of the first,

asked about a different kind of stuff. Whatever we conclude about how minds relate to brains will, at

the same time, tell us what else besides a brain might have a mind.

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It is worth pausing on why a course largely about how minds and brains evolved and how they

work should open with questions this abstract. The rest of this book treats minds and brains as

natural objects with histories. They are something that arose in living things, took different forms in

different lineages, and can be studied piece by piece like any other product of biology. But that

whole project takes something for granted. It assumes there is a mind there to have a history, and

that a mind is the kind of thing the methods of science can reach. Both assumptions can be

doubted, and thoughtful people have doubted them, in more than one way. If the mind were a non-

physical soul, its story would not be a chapter of biology at all. But the doubts do not end with souls,

and the ones this chapter dwells on longest are more modern. Even granting that the mind is fully

physical, we can wonder whether our everyday mental words for things like belief, desire, and fear

name anything real, or whether a mature brain science will find nothing there for them to pick out.

And even granting that they do name something real, one can still ask whether the mind can be

understood in its own terms at all, or whether any real understanding of it must run through the

biological details of the brain beneath. Each of these doubts, taken seriously, changes what a

science of the mind could be. So before we can ask how a mind evolved, how a brain produces

one, or whether a machine could ever have one, we have to know what a mind is, and whether it

sits within the reach of science. This chapter clears that ground. It is what makes “the evolution of

the mind” a coherent subject rather than a figure of speech.

There is a second thing to flag before we begin. This chapter is philosophy more than it is

science, and that is not an accident or an apology. Science answers questions by observation and

experiment: you form a hypothesis, you test it against the world, and the world gives you

information you use to judge the value of your hypothesis. But some questions have to be settled

before that process can even start. What do we mean by “mind”? What would count as a physical

explanation of one? Is the mind the sort of thing an experiment could detect at all? No

measurement answers these, partly because they are questions about which measurements would

count. This is the work of philosophy. It sharpens concepts and draws distinctions. And for

questions that observation cannot now (or ever) settle, it evaluates whether a set of commitments

hangs together. Far from being opposed to science, this work establishes the foundation that

science is built on.

Because these questions are not settled by experiment, you will meet several competing

answers in the pages ahead, and no one of them can simply be proven the way a claim in the lab

can. That is easy to misread, so it is worth being careful. “Cannot be settled by experiment” does

not mean there is no fact of the matter, or that any answer is as good as any other. It means the

tools that decide the question are different ones. Philosophers and scientists reason toward these

views, and between them, by asking which account is internally consistent, which fits best with

everything else we know about the brain and the world, and which explains the most while

assuming the least. Judged that way, the answers are not equal. As we will see, most contemporary

brain and cognitive scientists have converged on one broad family of views, for reasons this chapter

will lay out. But argument rarely compels the way a decisive experiment does. A coherent, well-

supported position can almost always be resisted at some cost, by giving something up elsewhere.

That is why thoughtful people still disagree, and why this stays a live conversation rather than a

closed case.

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Mind

perceiving · recognizing · learning

remembering · reasoning · deciding

communicating

Consciousness

awake, aware, knowing that one knows

Qualia

the raw feel of an experience

The dashed boundaries are contested: some views treat qualia as the whole of consciousness, and

some hold that mind simply is consciousness.

Figure 1.1 — Mind, consciousness, and qualia, with the boundaries the chapter is careful about drawn as

contested rather than crisp.

People have been offering answers to these questions for as long as they have been asking

them. The answers pile up into a bewildering heap: souls and spirits, brain states, patterns of

behavior, flows of information. There are many named perspectives attempting to explain the mind–

body relationship, such as dualism, materialism, idealism, and emergentism. But rather than march

through different answers one at a time, we will try to do something more helpful. First we will pin

down what we even mean by “mind.” Then we will ask a short sequence of questions about how the

mind might relate to the brain, and show how the famous positions fall out of how one answers

these questions.

§1.1.1 — What We Mean by “Mind”

Before asking how the mind relates to the brain, we had better say what we mean by the word

mind. Loose terms often lead to misunderstandings or debates that wouldn’t exist with clearer

definitions. And few terms are looser than mind. In this book, we use mind to refer to the broad set

of capacities a system has for taking in information about the world, holding onto it in some usable

form, and putting it to work in guiding what the system does. Perceiving, recognizing, learning,

remembering, reasoning, deciding, communicating: each is a way of turning information into

something useful. Together, they are what we will mean by a mind. This is the same information-

processing picture the previous module introduced, now serving as our working definition.

Stated that broadly, our definition of mind has blurry edges, and these edges are where

interesting fights sometimes break out. Consider a motion sensor on a door. It takes in information

that something moved, and acts on it, sliding a door open. Or consider a smart thermostat that

senses the temperature, compares it against a goal, and switches the air conditioner on or off.

Almost no one wants to grant that a thermostat has a mind. Yet it is doing, in miniature, the very

thing our definition names. Consider a much more complex artificial entity. Large language models

like ChatGPT, Claude, and Gemini produce strikingly fluent and apt responses. Many nonetheless

insist they do not and cannot have minds. One common dismissal is that they are “only predicting

the next word,” and therefore are not really thinking at all. Is that right? Does how a system

processes information, or how much of it, decide whether the word mind applies? We will not settle

these questions here. The point for now is that “mind” has no crisp boundary you can check a

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system against. Where the line falls is itself one of the field’s live debates, and it is the very problem

the machine cases keep forcing us to think about. Our definition is a starting place, not a test.

One aspect of the mind sits apart from the rest, and it is worth setting aside for now. When we

imagined that jolt of fear a moment ago, part of what we noted was that there is something it is like

to feel afraid. That felt, qualitative character of an experience is what philosophers call qualia.

Qualia are closely tied to consciousness, though the two are not simply the same thing

(Figure 1.1). Consciousness is used broadly, covering everything from being awake and aware to

knowing that one knows. Qualia name something narrower: the raw feel of an experience. Some

philosophers treat qualia as the defining core of consciousness, while others keep the wider sense

in view. We need not adjudicate between those views right now. What matters here is that this

cluster of problems is hard and distinctive enough that a longer version of this course would give it a

week to itself. Consciousness is a real part of the mind, or at least of the human mind. But it is only

one part. And at least descriptively it can be pried away from the rest. A system might perceive,

remember, and reason at a high level while it stays a separate question whether anything is

experienced along the way. Some views resist that separation, holding that mind simply is

consciousness. But this is a book about the mind in the broad sense, and consciousness is one

subtopic within it rather than its center. Whatever one finally concludes about consciousness, the

relationship between mind and the physical stays a rich and largely tractable question across the

whole territory of mental life. So we flag consciousness here, and then mostly set it aside for now.

With “mind” defined well enough to work with, we can take up the relationship we came for.

Philosophers call it the mind–body problem: how does the mind relate to the physical stuff it

seems to arise from? We will often put it even more neutrally, as the relation between mind and the

physical, because part of what is in question is whether that physical stuff has to be a brain at all.

We will build our understanding of it from a short sequence of questions, and the familiar named

positions will fall out along the way as answers we can label, rather than as boxes we start from.

§1.1.2 — What Kind of Thing Is a Mind?

Our first question is the most basic one there is: what kind of thing is a mind? This is a question

about the mind’s ontology, or its basic nature. Three answers have serious defenders. The first is

that mind is a distinct kind of thing in its own right, separate from physical stuff. The second is that

mind is nothing but the physical stuff of the body, so that “mind” is only another word for what the

brain is doing. And the third is that mind is a real property of physical stuff, as genuine as the stuff

itself but not identical to any particular piece of it. We will take the three in turn.

One quick test sorts the first answer from the other two. Imagine two systems physically identical

down to the last particle, in exactly the same state at the same instant. Must they have exactly the

same mind, the same thoughts and the same feelings? If you think they could differ, that one could

have a mind, or an experience, the other lacks, then you hold the first answer, that the mind is

something over and above the physical. If you think the physical facts settle the mental ones, so

that identical bodies must house identical minds, then you hold that the mind is physical, and your

quarrel is only between the second answer and the third. Most scientists take the physical side; the

general commitment that the physical fixes the mental is called materialism.

Start with the first answer, that the mind is a thing in its own right, separate from physical stuff.

Its classic defense (though by no means the first) comes from 17th-century philosopher René

Descartes. Descartes was wondering what he could actually be certain about. Perhaps some

powerful deceiver was tricking him at every turn. Maybe all of his experiences were a dream, or a

hallucination, or a fabrication. Resolving to doubt everything he possibly could, Descartes found that

he could doubt that he even had a body, or that the world outside him even existed. The one thing

he could not doubt was that he was thinking. The very act of doubting proved there was a mind

doing the thinking. Descartes argued that the mind’s existence itself was a certainty, entirely

separate from the body, which could be doubted away. This intuition is far older than Descartes,

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running back through the medieval philosopher Avicenna to Augustine to Plato’s eternal soul, and it

carries deep religious roots. Descartes simply gave the argument its sharpest edge.

Now aim Descartes’ intuition at a machine. Picture a being physically identical to a person.

Wired the same, behaving the same, flinching from pain, saying “that hurts,” and seeming to mean it

as far as anyone could tell. But this being has no inner experience of any kind. No actual feeling of

pain. No light on inside. Nobody home. The philosopher David Chalmers made this thought

experiment famous and named its subject a philosophical zombie. Notice that what the zombie is

missing is exactly the consciousness we set aside a moment ago. This is no accident: the

independent view draws most of its force from felt experience. That we can apparently imagine

such a being at all is what gives the view its grip. If the physical facts leave it open whether

experience is present, then experience looks like something over and above the physical facts.

At the center of this answer to the mind–body problem sits the view called substance dualism,

sometimes just called dualism. Dualism is the view that mind and body are two different kinds of

substance, and the non-physical mind, though it interacts with the brain, cannot be reduced to it.

Around dualism sit related views that also refuse to ground mind in the physical. Idealism is the odd

cousin that keeps only the mental, and demotes the physical. Idealism holds that the physical world

is in fact all an illusion in some entity’s mind (often God’s). Panpsychism is another view related to

dualism that holds that qualia (experience) are basic features of all matter, present in some dim

degree even in a rock or an electron. Neutral monism proposes that mind and matter are two

arrangements of a more basic stuff that is itself neither.

This commitment about the nature of mind fixes what dualism says about machine minds. If

having a mind requires a share of non-physical mind-stuff, then no feat of engineering can ever

produce one, because engineering only arranges physical parts. This is the philosophical zombie

from a moment ago, now turned on real hardware. However fluent or intelligent-seeming a

computer is, it is an empty mechanism, saying all the right things with no one inside. Panpsychism

is the family’s lone exception to this. If experience saturates all matter, then a chip has its faint

share too, strange as that sounds, though it holds no more of it for being a computer than a stone

does.

Why is this first answer, that mind and the physical world are distinct kinds of things, so

tempting? And if it is so tempting, why do so few scientists hold it? Its appeal is real. It sidesteps the

hardest puzzle of all, how mere matter could ever give rise to felt experience, by denying that matter

has to. It honors the strong sense that consciousness is special and not just more chemistry. And it

leaves room for the existence of an immaterial soul that might continue to exist independent of the

body. What makes it problematic as a scientific view is twofold. First is the interaction problem: if the

mind is genuinely non-physical, how does it affect the physical neurons that move your hand? Every

answer seems to smuggle the mind back into the physical world it was meant to transcend. Second

is untestability: a mind that leaves no physical trace is a mind no experiment can find. It is by its

very nature outside of science.

For all its intuitive pull, the first answer (mind is a separate kind of stuff from the physical) keeps

few working scientists in its camp, for the reasons just given. The great majority hold that the mind

is not a separate thing at all, but physical through and through. Yet that agreement hides a deep

disagreement, because there are two very different ways to be a materialist about the mind. They

are our second and third answers, and the difference between them shapes everything that follows.

§1.1.3 — The Mind as Nothing but the Physical

The second popular answer to the mind–body problem is quite blunt: the mind just is the brain, and

a separate mental vocabulary earns its keep only as a convenient shorthand, if at all. On this

answer, mental talk adds nothing that a physical vocabulary could not, in principle, replace.

The starkest historical form was radical behaviorism. John Watson and B. F. Skinner urged

psychology to study only what could be observed, namely behavior, and to banish talk of inner

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mental states as unscientific ghost-chasing. For a time (roughly from 1920-1960) behaviorism

dominated. What broke it was its inability to account for the very things minds most obviously do:

understand and produce language, reason, remember, and plan. The cognitive revolution of the

1960s and 1970s reopened the door to a science of inner mental processes.

Another variant of this answer is identity theory. Identity theory grants that mental states are

perfectly real, but insists each one simply is a physical brain state. Its slogan is that “pain is the

firing of C-fibers.” C-fibers are a particular kind of nerve fiber, the ones that carry the slow, dull,

aching kind of pain from the body toward the brain. The claim is not just that this firing causes pain,

or reliably comes with it, but that the pain is that firing. One event, two names. The model is the

earlier discovery that heat is the motion of molecules, not caused by it or merely correlated with it,

but the very same thing. Identity theory bets that every mental state will turn out this way, each one

revealed to be some specific state of the nervous system. But pinning a mental state to one specific

physical state carries a cost. If pain simply is the firing of C-fibers, then anything lacking C-fibers, an

octopus, an alien, or a silicon computer, could not be in pain no matter how it behaved.

Philosophers call this the chauvinism problem. Escaping it will be one of the attractions of the third

answer.

The boldest version of the second answer goes further still. Eliminative materialism agrees

that the mind is physical, but adds that our everyday way of talking about it is not merely

incomplete, it is mistaken. We typically explain someone’s behavior by saying she opened the fridge

because she wanted a snack and believed there was food inside. When we do this, we are leaning

on everyday language about beliefs, desires, hopes, and fears. The argument from eliminative

materialism is that these words are actually unscientific and misleading. Philosophers refer to these

kinds of words and the explanations behind them as folk psychology. The eliminativist’s claim is

that a mature brain science will no more find beliefs and desires inside the head than modern

medicine found the four humors it once blamed for disease. Concepts like those were not refined

and kept; they were dropped, because there was nothing there for them to name. Folk psychology,

the eliminativist says, is headed for the same fate. It will not be tidied up and matched to brain

states, but replaced outright. The best-known defenders of eliminative materialism are the

philosophers Patricia Churchland and Paul Churchland.

The reductive family’s strengths are genuine. It fits neuroscience’s methods hand in glove. It is

parsimonious, and posits no vague mental extras. But its troubles are equally real. There is the

chauvinism problem noted above, which implies that any difference in brain structure would make

for a different mental state. There is eliminativism’s air of self-refutation, since to assert “there are

no beliefs” is to express one. There is the stubborn fact of felt experience, the “what it is like” that a

list of brain states seems to leave out. And there is the working practice of psychology itself, which

explains and predicts behavior well in terms of memory, attention, and emotion. On the “do

machines have minds” question, identity theory says no by way of chauvinism. Eliminativism does

something stranger: it eliminates the question rather than answering it. If there are no folk “minds”

for us to have either, then asking whether a machine has one is as confused as asking it about a

person.

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Eliminated

The four humors named nothing, and

were dropped rather than refined.

Eliminative materialism’s bet

about beliefs and desires.

Identified

Heat turned out to be molecular

motion itself. One thing, two

names.

Identity theory’s bet: each mental

state is one particular brain

state.

Grounded, and kept

Temperature is explained by

statistical mechanics, and

thermodynamics still does real

work.

Where most of the field sits:

mental terms name something real,

explained by the brain without

being replaced by it.

Figure 1.2 — Three things that can happen to a higher-level concept, with the historical case above and the

matching claim about our mental vocabulary below.

Stepping back, these versions of the second answer echo two things that can happen to a

higher-level idea in the history of science. It can be discarded as naming nothing, the way the four

humors were, which is the eliminativist’s bet about folk psychology. Or it can be kept but shown to

be identical to something lower down, the way heat is equated with molecular motion. This is the

identity theorist’s view. But a higher-level idea can meet a third fate, unlike either of the first two

(Figure 1.2). In this case it is grounded in a deeper theory without being replaced by or equated with

it. Most brain and cognitive scientists think the mind fits this pattern best. That third pattern we

explain next.

§1.1.4 — The Mind as a Real Property of the Physical

The third answer is the one most working brain and cognitive scientists hold. Contrary to the

eliminativist, it says our mental terms refer to something real. But the mind is neither a thing in its

own right (as a dualist would say), nor a redundant name for the brain (as an identity theorist would

say). The dualist’s deepest mistake, on this view, is a kind of category error. The dualist asks what

kind of thing the mind is, and reaches for a special, non-physical kind, when the mind is not a thing

at all. It is a property of the physical stuff we already have, not a further item standing beside it.

Compare mass. Mass is not an object tucked inside a rock; it is something the rock has, a property

of physical matter. Temperature is another such property, being alive is a third. In Aristotle’s older

language, mind is the form a lump of matter takes when it is organized a certain way. The mind, this

answer says, is a property of just that kind. It is a property a physical system has when its parts are

organized and active in the right way. Mind is as real as the matter itself, but it is not a separate

ingredient added to it.

Under this view, temperature repays a closer look, because it also marks the borders this

answer shares with its neighbors. We earlier noted that physicists came to say that heat is the

motion of molecules. But even knowing this, thermodynamics, the science of heat, temperature, and

pressure, still does real work above and beyond theories of molecular motion. Its laws tell an

engineer how an engine or a refrigerator must behave. A deeper theory, statistical mechanics, later

explained where those laws come from, by treating heat and pressure as the combined motion of

countless molecules. Yet no one throws temperature away to track molecules one by one. It is

grounded in molecular motion without being replaced by it, and without being identical to any one

arrangement of it, since countless different arrangements count as the same temperature. That is

the line between this answer and identity theory, which instead equates a mental state with one

specific physical state. A different line separates it from panpsychism: this property surfaces only

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where matter is organized and active in the right way, as life does. It is not, as the panpsychist

supposes, a basic ingredient present in every scrap of matter. A rock has a mass and a temperature

but no mind, because it is not arranged to have one. On the third answer, a mind is what suitably

organized matter has and does: a real, higher-level property, not a ghost added to it.

Let us zoom back out and contrast how scientists tend to evaluate the third view compared with

the other two. Why not the first answer? Because materialism keeps explaining things. There has

never been a scientifically successful account of how a mind, or anything else, could exist apart

from a physical instantiation. And we understand the mind better every time we learn more about

the brain. The second answer is therefore half right. It is right that the mind is physical and that the

brain carries real explanatory weight. Its error, as the next step argues, is the further leap that

mental-level explanation can then be thrown away.

Why not stop at reduction to the brain? Because psychological explanations work, and they add

something a neuron-by-neuron story does not. You cannot in practice predict or explain a person’s

decisions in terms of movement of molecules or atoms. The calculation is hopeless, and the higher-

level account is how the science actually gets done. Most sciences, moreover, reduce without

eliminating, as thermodynamics and the gene remind us. And there is a deeper reason many

cognitive scientists take seriously, one that deserves its own name.

That reason is multiple realizability, the idea that one and the same mental state can be

realized in different physical materials, just as one temperature is realized by countless different

arrangements of molecules (Figure 1.3). The idea has an intuitive starting point. When you and a

friend both feel hunger, both grow angry, both recognize an apple, or both believe that the Earth

goes around the Sun, we find a lot of value in saying the two of you are in the same mental state.

Yet no two brains are wired exactly alike, and it is very unlikely that the state is realized in precisely

the same physical pattern in both of you, which is just what a strict identity theory would need. If the

mental state is shared while the physical detail is not, that state cannot be identical to the detail.

The point only sharpens as the systems grow more different. Pain in a person, pain in an octopus,

pain in some hypothetical alien, perhaps pain in a machine, may be functionally equivalent while

implemented in vastly different ways or even materials. If that is right, then no single physical state,

no particular firing of C-fibers, can be pain, if pain turns up in systems that have no C-fibers at all.

Multiple realizability is what defeats the identity theorist’s chauvinism. It is also what turns “could a

non-brain have a mind?” from a confusion into a real scientific question. It ties the mind–body

question to the machine question, which is why it deserves more than a passing mention. It is not

beyond dispute. And we will see the multiple realizability perspective challenged later, especially

with regard to machines. But its plausibility carries much of the weight behind this view. Multiple

realizability, then, lets the mind be a real, physical property without chaining it to one kind of matter.

Our third main kind of answer to the mind–body problem is mind as a real property of the

physical: mind is a property of physical matter arranged in a certain way or doing certain things. It

is shared by a family of more specific views, which differ in how they spell it out. Emergentism

holds that mental properties genuinely arise from physical organization the way wetness arises from

many H₂O molecules even though no single molecule is wet. Functionalism defines a mental state

by the causal role it plays, by what brings it about and what it brings about in turn, rather than by

what it is made of. A calculator’s “add” operation is whatever plays the adding role, on a phone or a

laptop alike. And by the same logic a mental state is whatever plays its role, in a brain or perhaps

elsewhere. This is the position that opens the door to machine minds. If a mental state simply is a

functional role, then anything that fills the role has the state, brain or no brain. It is, more or less, the

working assumption of most people involved in artificial intelligence. In its sharpest contemporary

form, this family treats a mental state as a real pattern of causal organization, something a physical

system genuinely has, discoverable by experiment yet reducible to no single neuron, so that a mind

is real the way an organization is real rather than the way an object is. A third perspective,

Embodied or Enactive Cognition, enters as a pointed caveat. Perhaps thinking is not only

something the brain does, but something a whole body does in an environment. If this is true, it is

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the sharpest check on a naive functionalism that expects a disembodied program to suffice for

mind. A fourth perspective, the levels of analysis approach, we hold back for the whole of §1.2,

since it is the framework the rest of the course runs on.

One temperature, many arrangements of molecules

20 °C 20 °C 20 °C

One mental state, many kinds of matter — the claim of multiple realizability

a human brain an octopus a silicon chip

the claim is that all three could be in the same functional state

Figure 1.3 — One property, many realizations, drawn twice in the same grammar. The top row is settled

physics; the bottom row is the claim this section is making.

What does this view say about machines? It is the home of “yes, in principle” (Table 1.1).

Functionalism and the levels approach make a machine mind conceptually possible without

claiming that any machine yet built actually has one. Two thinkers who agree the mind is a real

physical property can still part ways on machines, because a further question stays open: does a

mind’s physical basis have to be biological? For the cognitive capacities, most are willing to say a

machine could in principle have them. It is usually over consciousness, the part we set aside, where

many philosophers and scientists are most likely to believe that biological matter is necessary. One

can hold the mind fully physical and still suspect that living matter has features, in how it organizes

and maintains itself, that a simulation would copy without reproducing. The neuroscientist Anil Seth

argues in this spirit; it is a bet about substrate, not a lapse back into dualism. The course returns to

weigh all of this in Module 2. Here we set the question up; we do not answer it.

§1.1.5 — How Freely Can We Study the Mind?

Settling that the mind is a real property of the physical still leaves one more question, which is as

contentious today as any in the field. How freely can we study the mind in its own terms? Grant that

a mental state is a real property of some physical system. Must a good theory of it keep the physical

details in view, or can it abstract them away and work at the mental level alone? Philosophers call

this the mind’s explanatory separability. It is a question about method, not about what the mind is

made of.

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Table 1.1 — Eight named positions, set beside one another. The machine column is the one to read across:

what each position says about minds and brains fixes what it can say about anything else that might have a

mind.

Position What a mind is

made of

Do mental terms

name anything

real?

Could a machine

have a mind? Its main problem

Substance

dualism

A non-physical

substance,

interacting with

the body

Yes

No — engineering

only arranges

matter

How anything

non-physical

could move a

neuron, and what

experiment could

ever find it

Idealism

Only the mental;

the physical is an

appearance within

a mind

Yes No

Why the physical

world is so

orderly, and so

reliably shared

Panpsychism

Ordinary matter,

with experience

basic to all of it

Yes

Faintly — but no

more than a stone

does

Why organized

matter has one

unified mind

rather than a dust

of tiny ones

Radical

behaviorism

The body; only

behavior is fit to

study

No — set aside as

unscientific

The question is

not asked

Cannot account

for language,

reasoning,

memory, or

planning

Identity theory

Brain states, one

for each mental

state

Yes No — no C-fibers,

no pain

Chauvinism: it

rules out any

differently built

system

Eliminative

materialism

Brain states; folk

psychology

names nothing

No

The question

dissolves rather

than gets an

answer

Asserting that

there are no

beliefs is itself

expressing one

Emergentism

Physical matter,

organized; mind is

a property it has

Yes Yes, in principle

Saying precisely

what emergence

amounts to

Functionalism

Whatever fills the

causal role, of any

material

Yes

Yes — the role is

what matters, not

the stuff

Leaves out felt

experience, and

perhaps the body

At one pole sits strong autonomy. Psychology, on this view, is a science in its own right, standing

to neuroscience roughly as chemistry stands to physics: grounded in it, but not replaceable by it.

You can describe the procedure a mind runs without much caring what it runs on, exactly as you

can describe a chess program without describing transistors, or for that matter describe chemical

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reactions without describing the micro-details of the underlying protons, neutrons, and electrons.

This is the liberal functionalist’s confidence, and it is one wing of the third answer. At the other pole

sits a more cautious view. The mind is real and physical, but the details of how it is realized shape

what it can be. A theory that abstracts away from the body and the brain will miss something, quite

possibly something important or critical. Embodied cognition pulls this way, and so does the view

that mental kinds are realization-sensitive. Two systems that seem to behave alike when we view

them from a zoomed out or abstract perspective may not actually share a mental state unless they

are organized alike underneath. And those differences in implementation will be noticeable at the

functional level if we look closely enough. On this view the levels of a mind are separable but not

independent. You can ask about each on its own, yet the answers constrain one another.

Q1. Do the physical facts fix the mental

facts?

No Mind is a distinct kind of thing

substance dualism · idealism · panpsychism ·

neutral monism

Yes

Q2. Do our mental terms name anything

real?

No Mind is nothing but the physical

eliminative materialism · radical behaviorism

Yes

Q3. Is a mental state identical to one

specific physical state?

Yes Each mental state is one brain state

identity theory

No

Mind is a real property of the physical

emergentism · functionalism · levels of analysis — where most of the field sits

Q4. How freely can such a mind be studied in its own terms?

a matter of degree, not another fork — and the field is genuinely split along it

Strong autonomy: psychology as chemistry is to physics Realization-sensitive: embodiment, the speed of neurons

Figure 1.4 — The positions as answers, arrived at by asking four questions in order rather than by sorting the

world into boxes.

Neither pole is obviously right, and the disagreement is not without consequences. It decides

how far a science of the mind can float free of a science of the brain. To make progress on it we

need a way to lay the levels of a mind side by side, so that we can see which questions belong to

which and how far each constrains the others.

§1.1.6 — Where the Field Sits

Step back, and the section has been a short sequence of questions. Before anything else we fixed

what “mind” means, and set consciousness aside as a subtopic of its own. Then we asked what

kind of thing a mind is, and found three serious answers: a distinct kind of thing (dualism, idealism),

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nothing but the physical (behaviorism, identity theory, eliminative materialism), or a real property of

the physical (emergentism and functionalism, including the view of a mind as a real pattern of

causal organization). Most people in the field tend to endorse a perspective in the third category.

Then we asked how freely such a mind can be studied in its own terms, and found the field

genuinely split, from strong autonomy at one end to realization-sensitivity at the other (Figure 1.4).

The famous positions are not boxes we sorted the world into. They are just names for the different

ways of answering these and related questions.

So this is where most contemporary brain and cognitive science sits. The mind is a real property

of the physical, dependent on it and yet demanding explanation in its own terms, with the exact

degree of that independence still under active debate. That is a good description, but so far it is still

quite vague. “Study the mind at its own level” has a satisfying ring, yet we have not said what those

levels are or how they relate to the brain beneath them. In §1.2 we turn this into a concrete, working

framework, Marr’s levels of analysis, which gives us a way to lay a mind’s levels side by side and

see how each constrains the others. This is the structure the rest of the course is built on.

§1.2 — Levels of Analysis

§1.2.1 — One System, Many Kinds of Explanation

We ended the last section by stating that, to most brain and cognitive scientists, the mind is a real

property of physical systems. It is an entity that can be studied, at least in part, in its own terms. But

it is also answerable to the brain beneath it. That has a satisfying ring. But it stays empty until we

say what “its own terms” are, and how those terms relate to one another. Before we reach for the

mind, it helps to see the same layered situation in something simpler and fully physical, where

nothing about souls or consciousness can cloud the view.

Take the heart. Suppose you want to understand it. One good explanation describes what it is

made of and how the parts move. The heart is muscle fibers contracting in sequence, valves

opening and closing, and the electrical pulses that set the rhythm. That is a real kind of explanation,

of the kind that a physiologist can give in exquisite detail. But notice that it leaves a different

question untouched. What is the heart for? The answer, that it pumps blood to carry oxygen and

nutrients through the body, is not a fact about muscle tissue at all. It is a fact about a problem the

heart solves. And there is a third question sitting between the other two. How, by what strategy,

does it solve that problem? Pushing fluid one way through a system of one-way valves is a strategy,

and you could describe it without naming a single protein, or even knowing the pump was built from

biological tissue rather than metal.

We have identified three questions, then, about one object: 1) what problem is being solved, 2)

by what strategy, and 3) in what physical stuff (Figure 1.5). These are not competing questions with

mutually exclusive answers. The molecular story does not make the “what for” false, and knowing

the job does not tell you the tissue. Each is a real question with its own answer, and you have not

fully understood the heart until you can answer all three. This is the heart (so to speak) of the

multiple levels of analysis perspective, so it is worth stating plainly: a single system can demand

several different kinds of explanation at once, and they complete one another rather than compete

with each other.

There is a reflex worth resisting here, because it returns throughout the course. It says: surely

the physical story is the real explanation, and the talk of “problems” and “strategies” is loose

shorthand we will drop once we understand the tissue. We met the reasons against this in §1.1,

when we saw why the mind is not thrown out in favor of the brain. The same reasons apply one

level up, to explanation itself. A complete account of the muscle, every fiber and every firing

mapped, would still leave two questions open. It would not tell you what the heart is for, that it

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solves the problem of moving oxygen through a body. And it would not tell you how it does the job,

that its strategy is to push blood one way through a system of one-way valves. Those are further,

real facts about the system, and no amount of detail about the tissue will hand them to you. The

same holds when we turn from hearts to minds. A complete wiring diagram of the brain, every

neuron and every connection, would still not tell you what problem a given circuit is solving, say,

recognizing a face, or how it solves it, by what series of steps it turns a wash of light into a name. A

map of the stuff is not yet an account of the problem or the method.

The Heart at Three Levels

Computational

What problem is being

solved, and why?

Move oxygen and nutrients to every tissue in a body.

Algorithmic

By what strategy? Push fluid one way, through a system of one-way valves.

Implementational

In what physical stuff,

and how?

Muscle fibers contracting in sequence, one-way valves, and

electrical pulses setting the rhythm.

Figure 1.5 — The heart, answered three ways. Each band is one kind of question, and the three answers

complete one another rather than compete.

This insight, that one system supports several complementary kinds of explanation, is old, and it

has been carved up in more than one way. Aristotle held that fully explaining a thing takes answers

to four different questions about it: what it is made of, how its parts are arranged, what brought it

into being, and what it is for. The philosopher Daniel Dennett describes three “stances” one can

take toward a system, treating it as mere physics, as a piece of design, or as an agent with goals. A

long tradition in the philosophy of biology explains a phenomenon by laying out the mechanism that

produces it: its parts, their operations, and their organization. These frameworks overlap and mostly

agree. This course adopts a different one, a system of three levels of analysis proposed by the

vision scientist David Marr. We adopt it not because it is uniquely correct but because its three

levels line up cleanly with how brain and cognitive science already divides into disciplines, and with

the evolutionary spine of the course. Building that alignment is the work of the rest of this chapter.

We turn to Marr’s three levels now.

§1.2.2 — Marr’s Three Levels

David Marr’s framework was born from a complaint. Marr studied vision in the 1970s, at a time

when neuroscience was learning an enormous amount about individual visual neurons: which ones

fired, when, and in response to what. And yet, Marr felt, the field was not much closer to

understanding how seeing works. Piling up facts about neurons, he argued, was like trying to

understand the flight of a bird by studying only its feathers. You could catalog every barb and

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filament and still have no idea why birds can fly. Something essential was being missed, and no

amount of the same kind of detail would supply it.

Computational

What problem is being

solved, and why?

evolutionary biology,

behavioral ecology,

anthropology

Algorithmic

By what strategy?

cognitive psychology,

artificial intelligence

Implementational

In what physical stuff,

and how?

neuroscience

Addition: combine quantities into a total.

Order does not matter; adding nothing changes nothing.

Decimal columns,

carrying Binary place value Beads on an abacus Counting on

fingers

A child with a

pencil Brass gears Silicon circuits the same expansion

could be drawn under

any of the four

Figure 1.6 — Addition at three levels, drawn as the branching it is. One problem admits several algorithms,

and each algorithm admits several implementations, so fixing any one level leaves the others open.

What was missing, Marr proposed, is that fully understanding any information-processing

system takes answers to three different kinds of question, at three levels. Marr’s contribution was to

name them, sharpen them, and insist that all three are needed.

The first is the computational level. It asks what problem the system solves, and what anything

solving that problem must respect. Notice that this is not a question about the machinery. It is a

question about the task itself, considered on its own. Take basic addition. The computational

description of addition says what the operation has to do: combine quantities into a total, in a way

that does not depend on the order the parts arrive (three apples and then two is the same as two

and then three), and where adding nothing leaves the total unchanged. These are facts about

addition, true no matter who or what is doing the adding. If described this way, you have specified

what addition is at the computational level, without yet saying anything about how the sum actually

gets computed.

The second is the algorithmic level. It asks how the system solves the problem: by what

procedure, working over what representations. Here the choices multiply, because one and the

same computation can be carried out in many ways. To add, you might line the numbers up as

decimal digits and work column by column, carrying as you go, the way many learn to do so in

school. You might instead represent the numbers in binary, as a computer does, and follow a

different procedure that reaches the very same totals. You might slide beads on an abacus, count

on your fingers, or manipulate Roman numerals by rules that look nothing like the digit-column

method. Each of these is a distinct algorithm, and all of them compute addition. The algorithmic

level is where we say which one a given system actually uses.

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The third is the implementational level. It asks what physical stuff carries out the algorithm,

and how. The column-by-column method can be run by a child with a pencil, by the gears of a

mechanical adding machine, or by the circuits of an electronic calculator. Same problem, same

algorithm, entirely different matter. The implementational level describes that actual substrate:

neurons and their firing, silicon and its voltages, brass gears and their teeth.

The three levels are genuinely distinct, and it is worth seeing why, because the reason is

structural rather than a matter of taste. The relations between the levels are many-to-one, in both

directions (Figure 1.6). A single computational problem, such as addition, can be solved by many

different algorithms. And a single algorithm can be implemented in many different physical

materials. So fixing one level leaves the others open. Knowing the problem does not tell you the

procedure, and knowing the procedure does not tell you the hardware. That is exactly why an

answer at one level cannot stand in for an answer at another. The three are not three descriptions

of a single fact. They are answers to three different questions, and a full understanding needs all

three.

Marr added a further, stronger, and more controversial claim, which we will mention now and

examine later. He held that the levels are not merely distinct but ordered, with the computational

level coming first. You cannot understand how a system works, he argued, until you know what

problem it is solving. This means analysis should run from the top down. This top-down priority is

central to Marr’s own view, and it is the natural reading of his complaint that studying feathers and

muscles doesn’t add up to understanding flight. But it is a claim, not an obvious truth. It has been

challenged, and we will discuss that challenge in §1.2.5. For now we have what we came for. We

have three distinct levels, each answering a question the others cannot. The next step is to see why

these particular three levels make a good backbone for this course.

§1.2.3 — The Course Backbone: Levels, Disciplines, and the Evolution of the

Human Mind

We now have three distinct levels. What turns them from a piece of philosophy into the backbone of

this course? You may have already noticed that the three levels line up with the way brain and

cognitive science already divides itself into fields. That alignment is what lets a framework guide our

curriculum.

Look at how the questions sort out. The computational question, what problem is being solved,

and why, is the home turf of the disciplines that study why organisms are built the way they are.

Evolutionary biology, behavioral ecology, anthropology, and parts of psychology ask what a capacity

is for. The algorithmic question, by what procedure, over what representations, is the province of

cognitive psychology and of artificial intelligence. These fields both describe the steps a mind runs

abstracted away from the physical materials they are built from. The implementational question, in

what physical stuff, and how, belongs to neuroscience. So we have levels corresponding to three

broad families of academic disciplines, each asking a different one of Marr’s questions about the

same object. This is not a coincidence. The fields grew up around different questions in the first

place. This is why they so often talk past one another, and why laying the levels side by side lets

their findings be combined rather than ranked.

One of these alignments between levels and academic disciplines needs more than a label,

because it is what gives this course its particular shape. Return to the computational level, the

question of what problem a system solves and why. For a human-made object, the “why” is easy.

The why is explainable in terms of the purpose of its creator or designer. Ask why a calculator does

addition and the answer is that its makers wanted a tool for doing arithmetic. But a heart has no

designer. Neither does an eye, or a memory system, or the circuit that recognizes a face. So what

grounds the “why” for a living thing? The primary answer for most organisms is natural selection.

For a biological system, the computational question usually becomes a question about ancestry and

survival. What problem did this organism’s ancestors face, such that solving it helped them survive

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and reproduce, and pass on whatever machinery did the solving? The “why” of a biological trait is

an adaptive why. This is the module’s evolutionary perspective, and it makes evolution the spine of

the whole course rather than one week’s topic. Again and again in this course, asking at the

computational level what a mental capacity is for will mean asking what problem it solved when it

evolved.

A word of caution comes with this approach. An adaptive why is a hypothesis to be tested, not a

story to be assumed. Not every trait is an adaptation shaped for a purpose. Some are byproducts of

other traits, some are the residue of chance, some are accidents of history that stuck around. The

computational level asks about the adaptive problem a capacity solves. It does not license inventing

a flattering tale about a trait or property. Being open-minded about adaptive hypotheses, and

demanding evidence for them, is critical.

There is a further reason to be careful about adaptive stories, and for our own species it is the

largest of all: culture. Many of the problems a modern human mind solves were set neither by a

designer nor directly by natural selection. Reading, symbolic arithmetic, handling money, driving a

car: natural selection has not had nearly enough time to adapt the human species to solve these

problems. And yet, they are among the most demanding things we do. Their what for bottoms out in

a goal set by a human practice and handed down socially, not in ancestral survival and

reproduction. Culture completes a triad of sources for a computational goal, one to match each kind

of system we have discussed: a designer’s intent for an artifact, natural selection for a biological

trait, and a cultural practice for learned human skills (Figure 1.7).

A designer’s intent

A calculator does addition

because its makers wanted a tool

for arithmetic.

for an artifact

Natural selection

An eye recovers the world because

ancestors who saw it survived and

reproduced.

for a biological trait — the

adaptive why

A cultural practice

Reading and handling money are

goals set by a practice and

handed down socially.

for a learned human skill

A human mind draws on all three at once.

Figure 1.7 — Three sources of a computational goal, one for each kind of system — and a human mind

draws on all three at once.

Culture is a critical source for the human mind’s computational goals, but it does not float free of

biology. The very capacity to acquire knowledge through culture, our unusual flexibility in learning,

our language, our habit of teaching one another, these abilities are themselves among our strongest

biological adaptations. This means cultural goals rest on an evolved foundation rather than

replacing it. And cultural skills tend to recycle machinery that evolved for something else. Reading is

only a few thousand years old, far too recent for a dedicated brain system. But the ability to read

makes heavy use of circuitry that evolved for recognizing objects. Similarly, symbolic arithmetic is a

cultural practice that is built atop an approximate sense of number that we share with other animals.

One word deserves care in all this, because it names two different things. We say a biological trait

is adapted across generations, by selection acting on a lineage. And we say a skill is adapted within

a single lifetime, by a person learning and practicing. Both shape what a mind computes, on entirely

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different timescales and by entirely different means, and telling them apart is one of the difficult

tasks of the field of brain and cognitive science (Table 1.2).

Table 1.2 — One word, two processes. Both leave a mind better matched to a problem, and almost nothing

else about them is the same.

Adapted across generations Adapted within a lifetime

Timescale Many generations Seconds to years — one telling

can be enough

Mechanism Natural selection acting on a

lineage

Learning and practice in one

individual

What changes The inherited machinery a

species is built with

One individual’s skills and

knowledge

What is passed on Genes Nothing biologically — culture

may carry it socially

Example

An eye shaped over evolutionary

time to recover the world from

light

Reading, or doing arithmetic on

paper

One more relationship between the three levels and the themes of our course deserves to be

mentioned. The algorithmic level is where minds and machines become comparable in a disciplined

way. Because an algorithm abstracts away from the stuff that runs it, the same procedure can in

principle run on neurons or on silicon. That is exactly what makes a comparison between a brain

and a machine a scientific claim rather than a loose metaphor. The algorithmic level is about

comparing procedures, not materials. This is the connection to the multiple realizability issue we

discussed in §1.1. It is also why artificial intelligence research tends to sit at the algorithmic level in

Marr’s framework. And it is why the question of machine minds, which §1.1 posed and left open, will

keep returning as a question about shared algorithms.

Because Marr’s levels map onto disciplines this cleanly, they give the course its structure. Most

weeks ahead take up one capacity: perception, memory, decision-making, and examine it at all

three levels in turn. What problem did it evolve to solve, what algorithms and representations carry

out the solution, and what neural machinery implements it. You will see the same three-part scaffold

return again and again, with the discipline labels attached, until the habit of asking “which level is

this explanation working at?” becomes second nature. To see the scaffold in action before it

becomes routine, in the next section we will examine a single capacity at all three levels.

§1.2.4 — Vision at All Three Levels

The best way to understand the framework, and to see its value, is to watch it work on a real case.

We will use vision as our demonstration, since it was Marr’s own example. It is also a capacity we

understand unusually well at all three levels, so the framework can be shown working rather than

merely asserted.

Begin at the computational level, which asks what problem vision solves. The obvious answer is

that vision uses the information in light to sense the objects and events around us, so that we can

respond to them appropriately. That answer is not wrong, but it hides a real difficulty, one that

appears only when we look closely at what the eye actually has to work with.

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What actually reaches the eye is far less than the world itself. At the back of the eye sits the

retina. The retina is a thin sheet of light-sensitive cells. Light bouncing off the things around an

organism passes through the lens, and is focused onto the retina, much as a camera focuses an

image onto its sensor. What the retina registers is simply how much light, and of what color, falls on

each point across its surface. That surface has only two dimensions: up-and-down and side-to-side,

with no depth of its own. This means the raw input to vision is in effect a flat picture, a two-

dimensional array of light.

Yet a flat picture is not what the animal needs. What it presumably needs to know about is the

three-dimensional world that produced the image. What surfaces are present and where are they?

What objects are present? What is food, and what is a predator? One conceptualization of vision’s

task, then, is to recover that solid world from the flat pattern of light.

But the big problem with recovering the 3D world from a 2D image is that, for a given 2D image,

there are an infinite number of different 3D scenes that could have generated that image. So the 2D

image doesn’t tell you all you need to know about what is actually out there. Different 3D scenes,

from different distances and under different lighting conditions, could cast the very same pattern of

light on the retina. Recovering one three-dimensional world from that image is what is called an ill-

posed problem. The data alone cannot precisely specify which scene generated the image

(Figure 1.8).

How does the visual system solve this ill-posed problem? The visual system is able to determine

what 3D world most likely generated the image by making assumptions about how the world usually

is. These include assumptions such as that light tends to come from above, and that surfaces tend

to be continuous. These assumptions help the mind single out the most likely scene from the

endless candidates. Discovering that the visual system makes these assumptions would be very

difficult, if not impossible, if we were just studying the visual system’s neurons without also thinking

about the nature of the problem that it needs to solve.

One of those assumptions can be caught in the act (Figure 1.9). Every disc in the grid is shaded

identically. The only difference among them is that the middle row has been turned upside down.

Yet one set reads as raised bumps and the other as hollow dimples. Turn the page around, and the

two sets exchange appearances. Nothing about the ink changed, so the bumps and the dimples

were never on the page at all. They were supplied by a visual syst