Evolutionary Psychology: A Primer
Leda Cosmides & John Tooby
The goal of research in evolutionary psychology is to discover and understand
the design of the human mind. Evolutionary psychology is an approach to
psychology, in which knowledge and principles from evolutionary biology are put
to use in research on the structure of the human mind. It is not an area of
study, like vision, reasoning, or social behavior. It is a way of thinking about
psychology that can be applied to any topic within it.
In this view, the mind is a set of information-processing machines that were
designed by natural selection to solve adaptive problems faced by our hunter
gatherer ancestors. This way of thinking about the brain, mind, and behavior is
changing how scientists approach old topics, and opening up new ones. This
chapter is a primer on the concepts and arguments that animate it.
Debauching the mind: Evolutionary psychology's past and present
In the final pages of the Origin of Species, after he had presented the theory
of evolution by natural selection, Darwin made a bold prediction: "In the
distant future I see open fields for far more important researches. Psychology
will be based on a new foundation, that of the necessary acquirement of each
mental power and capacity by gradation." Thirty years later, William James tried
to do just that in his seminal book, Principles of Psychology, one of the
founding works of experimental psychology (James, 1890). In Principles, James
talked a lot of "instincts". This term was used to refer (roughly) to
specialized neural circuits that are common to every member of a species and are
the product of that species' evolutionary history. Taken together, such circuits
constitute (in our own species) what one can think of as "human nature".
It was (and is) common to think that other animals are ruled by "instinct"
whereas humans lost their instincts and are ruled by "reason", and that this is
why we are so much more flexibly intelligent than other animals. William James
took the opposite view. He argued that human behavior is more flexibly
intelligent than that of other animals because we have more instincts than they
do, not fewer. We tend to be blind to the existence of these instincts, however,
precisely because they work so well -- because they process information so
effortlessly and automatically. They structure our thought so powerfully, he
argued, that it can be difficult to imagine how things could be otherwise. As a
result, we take "normal" behavior for granted. We do not realize that "normal"
behavior needs to be explained at all. This "instinct blindness" makes the study
of psychology difficult. To get past this problem, James suggested that we try
to make the "natural seem strange":
"It takes...a mind debauched by learning to carry the process of making the
natural seem strange, so far as to ask for the why of any instinctive human
act. To the metaphysician alone can such questions occur as: Why do we smile,
when pleased, and not scowl? Why are we unable to talk to a crowd as we talk
to a single friend? Why does a particular maiden turn our wits so upside
down? The common man can only say, Of course we smile, of course our heart
palpitates at the sight of the crowd, of course we love the maiden, that
beautiful soul clad in that perfect form, so palpably and flagrantly made for
all eternity to be loved!
And so, probably, does each animal feel about the particular things it tends
to do in the presence of particular objects. ... To the lion it is the
lioness which is made to be loved; to the bear, the she-bear. To the broody
hen the notion would probably seem monstrous that there should be a creature
in the world to whom a nestful of eggs was not the utterly fascinating and
precious and never-to-be-too-much-sat-upon object which it is to her.
Thus we may be sure that, however mysterious some animals' instincts may
appear to us, our instincts will appear no less mysterious to them." (William
In our view, William James was right about evolutionary psychology. Making the
natural seem strange is unnatural -- it requires the twisted outlook seen, for
example, in Gary Larson cartoons. Yet it is a pivotal part of the enterprise.
Many psychologists avoid the study of natural competences, thinking that there
is nothing there to be explained. As a result, social psychologists are
disappointed unless they find a phenomenon "that would surprise their
grandmothers", and cognitive psychologists spend more time studying how we solve
problems we are bad at, like learning math or playing chess, than ones we are
good at. But our natural competences -- our abilities to see, to speak, to find
someone beautiful, to reciprocate a favor, to fear disease, to fall in love, to
initiate an attack, to experience moral outrage, to navigate a landscape, and
myriad others -- are possible only because there is a vast and heterogenous
array of complex computational machinery supporting and regulating these
activities. This machinery works so well that we don't even realize that it
exists -- We all suffer from instinct blindness. As a result, psychologists have
neglected to study some of the most interesting machinery in the human mind.
An evolutionary approach provides powerful lenses that correct for instinct
blindness. It allows one to recognize what natural competences exist, it
indicates that the mind is a heterogeneous collection of these competences and,
most importantly, it provides positive theories of their designs. Einstein once
commented that "It is the theory which decides what we can observe". An
evolutionary focus is valuable for psychologists, who are studying a biological
system of fantastic complexity, because it can make the intricate outlines of
the mind's design stand out in sharp relief. Theories of adaptive problems can
guide the search for the cognitive programs that solve them; knowing what
cognitive programs exist can, in turn, guide the search for their neural basis.
(See Figure 1.)
The Standard Social Science Model
One of our colleagues, Don Symons, is fond of saying that you cannot understand
what a person is saying unless you understand who they are arguing with.
Applying evolutionary biology to the study of the mind has brought most
evolutionary psychologists into conflict with a traditional view of its
structure, which arose long before Darwin. This view is no historical relic: it
remains highly influential, more than a century after Darwin and William James
Both before and after Darwin, a common view among philosophers and scientists
has been that the human mind resembles a blank slate, virtually free of content
until written on by the hand of experience. According to Aquinas, there is
"nothing in the intellect which was not previously in the senses." Working
within this framework, the British Empiricists and their successors produced
elaborate theories about how experience, refracted through a small handful of
innate mental procedures, inscribed content onto the mental slate. David Hume's
view was typical, and set the pattern for many later psychological and social
science theories: "...there appear to be only three principles of connexion
among ideas, namely Resemblance, Contiguity in time or place, and Cause or
Over the years, the technological metaphor used to describe the structure of the
human mind has been consistently updated, from blank slate to switchboard to
general purpose computer, but the central tenet of these Empiricist views has
remained the same. Indeed, it has become the reigning orthodoxy in mainstream
anthropology, sociology, and most areas of psychology. According to this
orthodoxy, all of the specific content of the human mind originally derives from
the "outside" -- from the environment and the social world -- and the evolved
architecture of the mind consists solely or predominantly of a small number of
general purpose mechanisms that are content-independent, and which sail under
names such as "learning," "induction," "intelligence," "imitation,"
"rationality," "the capacity for culture," or simply "culture."
According to this view, the same mechanisms are thought to govern how one
acquires a language, how one learns to recognize emotional expressions, how one
thinks about incest, or how one acquires ideas and attitudes about friends and
reciprocity -- everything but perception. This is because the mechanisms that
govern reasoning, learning, and memory are assumed to operate uniformly,
according to unchanging principles, regardless of the content they are operating
on or the larger category or domain involved. (For this reason, they are
described as content-independent or domain-general.) Such mechanisms, by
definition, have no pre-existing content built-in to their procedures, they are
not designed to construct certain contents more readily than others, and they
have no features specialized for processing particular kinds of content. Since
these hypothetical mental mechanisms have no content to impart, it follows that
all the particulars of what we think and feel derive externally, from the
physical and social world. The social world organizes and injects meaning into
individual minds, but our universal human psychological architecture has no
distinctive structure that organizes the social world or imbues it with
characteristic meanings. According to this familiar view -- what we have
elsewhere called the Standard Social Science Model -- the contents of human
minds are primarily (or entirely) free social constructions, and the social
sciences are autonomous and disconnected from any evolutionary or psychological
foundation (Tooby & Cosmides, 1992).
Three decades of progress and convergence in cognitive psychology, evolutionary
biology, and neuroscience have shown that this view of the human mind is
radically defective. Evolutionary psychology provides an alternative framework
that is beginning to replace it. On this view, all normal human minds reliably
develop a standard collection of reasoning and regulatory circuits that are
functionally specialized and, frequently, domain-specific. These circuits
organize the way we interpret our experiences, inject certain recurrent concepts
and motivations into our mental life, and provide universal frames of meaning
that allow us to understand the actions and intentions of others. Beneath the
level of surface variability, all humans share certain views and assumptions
about the nature of the world and human action by virtue of these human
universal reasoning circuits.
Back to Basics
How did evolutionary psychologists (EPs) arrive at this view? When rethinking a
field, it is sometimes necessary to go back to first principles, to ask basic
questions such as "What is behavior?" "What do we mean by 'mind'?" "How can
something as intangible as a 'mind' have evolved, and what is its relation to
the brain?". The answers to such questions provide the framework within which
evolutionary psychologists operate. We will try to summarize some of these here.
Psychology is that branch of biology that studies (1) brains, (2) how brains
process information, and (3) how the brain's information-processing programs
generate behavior. Once one realizes that psychology is a branch of biology,
inferential tools developed in biology -- its theories, principles, and
observations -- can be used to understand psychology. Here are five basic
principles -- all drawn from biology -- that EPs apply in their attempts to
understand the design of the human mind. The Five Principles can be applied to
any topic in psychology. They organize observations in a way that allows one to
see connections between areas as seemingly diverse as vision, reasoning, and
Principle 1. The brain is a physical system. It functions as a computer. Its
circuits are designed to generate behavior that is appropriate to your
The brain is a physical system whose operation is governed solely by the laws of
chemistry and physics. What does this mean? It means that all of your thoughts
and hopes and dreams and feelings are produced by chemical reactions going on in
your head (a sobering thought). The brain's function is to process information.
In other words, it is a computer that is made of organic (carbon-based)
compounds rather than silicon chips. The brain is comprised of cells: primarily
neurons and their supporting structures. Neurons are cells that are specialized
for the transmission of information. Electrochemical reactions cause neurons to
Neurons are connected to one another in a highly organized way. One can think of
these connections as circuits -- just like a computer has circuits. These
circuits determine how the brain processes information, just as the circuits in
your computer determine how it processes information. Neural circuits in your
brain are connected to sets of neurons that run throughout your body. Some of
these neurons are connected to sensory receptors, such as the retina of your
eye. Others are connected to your muscles. Sensory receptors are cells that are
specialized for gathering information from the outer world and from other parts
of the body. (You can feel your stomach churn because there are sensory
receptors on it, but you cannot feel your spleen, which lacks them.) Sensory
receptors are connected to neurons that transmit this information to your brain.
Other neurons send information from your brain to motor neurons. Motor neurons
are connected to your muscles; they cause your muscles to move. This movement is
what we call behavior.
Organisms that don't move, don't have brains. Trees don't have brains, bushes
don't have brains, flowers don't have brains. In fact, there are some animals
that don't move during certain stages of their lives. And during those stages,
they don't have brains. The sea squirt, for example, is an aquatic animal that
inhabits oceans. During the early stage of its life cycle, the sea squirt swims
around looking for a good place to attach itself permanently. Once it finds the
right rock, and attaches itself to it, it doesn't need its brain anymore because
it will never need to move again. So it eats (resorbs) most of its brain. After
all, why waste energy on a now useless organ? Better to get a good meal out of
In short, the circuits of the brain are designed to generate motion -- behavior
-- in response to information from the environment. The function of your brain
- this wet computer -- is to generate behavior that is appropriate to your
Principle 2. Our neural circuits were designed by natural selection to solve
problems that our ancestors faced during our species' evolutionary history.
To say that the function of your brain is to generate behavior that is
"appropriate" to your environmental circumstances is not saying much, unless you
have some definition of what "appropriate" means. What counts as appropriate
"Appropriate" has different meanings for different organisms. You have sensory
receptors that are stimulated by the sight and smell of feces -- to put it more
bluntly, you can see and smell dung. So can a dung fly. But on detecting the
presence of feces in the environment, what counts as appropriate behavior for
you differs from what is appropriate for the dung fly. On smelling feces,
appropriate behavior for a female dung fly is to move toward the feces, land on
them, and lay her eggs. Feces are food for a dung fly larva -- therefore,
appropriate behavior for a dung fly larva is to eat dung. And, because female
dung flies hang out near piles of dung, appropriate behavior for a male dung fly
is to buzz around these piles, trying to mate; for a male dung fly, a pile of
dung is a pick-up joint.
But for you, feces are a source of contagious diseases. For you, they are not
food, they are not a good place to raise your children, and they are not a good
place to look for a date. Because a pile of dung is a source of contagious
diseases for a human being, appropriate behavior for you is to move away from
the source of the smell. Perhaps your facial muscles will form the cross
culturally universal disgust expression as well, in which your nose wrinkles to
protect eyes and nose from the volatiles and the tongue protrudes slightly, as
it would were you ejecting something from your mouth.
For you, that pile of dung is "disgusting". For a female dung fly, looking for a
good neighborhood and a nice house for raising her children, that pile of dung
is a beautiful vision -- a mansion. (Seeing a pile of dung as a mansion -
that's what William James meant by making the natural seem strange).
The point is, environments do not, in and of themselves, specify what counts as
"appropriate" behavior. In other words, you can't say "My environment made me do
it!" and leave it at that. In principle, a computer or circuit could be designed
to link any given stimulus in the environment to any kind of behavior. Which
behavior a stimulus gives rise to is a function of the neural circuitry of the
organism. This means that if you were a designer of brains, you could have
engineered the human brain to respond in any way you wanted, to link any
environmental input to any behavior -- you could have made a person who licks
her chops and sets the table when she smells a nice fresh pile of dung.
But what did the actual designer of the human brain do, and why? Why do we find
fruit sweet and dung disgusting? In other words, how did we get the circuits
that we have, rather than those that the dung fly has?
When we are talking about a home computer, the answer to this question is
simple: its circuits were designed by an engineer, and the engineer designed
them one way rather than another so they would solve problems that the engineer
wanted them to solve; problems such as adding or subtracting or accessing a
particular address in the computer's memory. Your neural circuits were also
designed to solve problems. But they were not designed by an engineer. They were
designed by the evolutionary process, and natural selection is the only
evolutionary force that is capable of creating complexly organized machines.
Natural selection does not work "for the good of the species", as many people
think. As we will discuss in more detail below, it is a process in which a
phenotypic design feature causes its own spread through a population (which can
happen even in cases where this leads to the extinction of the species). In the
meantime (to continue our scatological examples) you can think of natural
selection as the "eat dung and die" principle. All animals need neural circuits
that govern what they eat -- knowing what is safe to eat is a problem that all
animals must solve. For humans, feces are not safe to eat -- they are a source
of contagious diseases. Now imagine an ancestral human who had neural circuits
that made dung smell sweet -- that made him want to dig in whenever he passed a
smelly pile of dung. This would increase his probability of contracting a
disease. If he got sick as a result, he would be too tired to find much food,
too exhausted to go looking for a mate, and he might even die an untimely death.
In contrast, a person with different neural circuits -- ones that made him avoid
feces -- would get sick less often. He will therefore have more time to find
food and mates and will live a longer life. The first person will eat dung and
die; the second will avoid it and live. As a result, the dung-eater will have
fewer children than the dung-avoider. Since the neural circuitry of children
tends to resemble that of their parents, there will be fewer dung-eaters in the
next generation, and more dung-avoiders. As this process continues, generation
after generation, the dung-eaters will eventually disappear from the population.
Why? They ate dung and died out. The only kind of people left in the population
will be those like you and me -- ones who are descended from the dung-avoiders.
No one will be left who has neural circuits that make dung delicious.
In other words, the reason we have one set of circuits rather than another is
that the circuits that we have were better at solving problems that our
ancestors faced during our species' evolutionary history than alternative
circuits were. The brain is a naturally constructed computational system whose
function is to solve adaptive information-processing problems (such as face
recognition, threat interpretation, language acquisition, or navigation). Over
evolutionary time, its circuits were cumulatively added because they "reasoned"
or "processed information" in a way that enhanced the adaptive regulation of
behavior and physiology.
Realizing that the function of the brain is information-processing has allowed
cognitive scientists to resolve (at least one version of) the mind/body problem.
For cognitive scientists, brain and mind are terms that refer to the same
system, which can be described in two complementary ways -- either in terms of
its physical properties (the brain), or in terms of its information-processing
operation (the mind). The physical organization of the brain evolved because
that physical organization brought about certain information-processing
relationships -- ones that were adaptive.
It is important to realize that our circuits weren't designed to solve just any
old kind of problem. They were designed to solve adaptive problems. Adaptive
problems have two defining characteristics. First, they are ones that cropped up
again and again during the evolutionary history of a species. Second, they are
problems whose solution affected the reproduction of individual organisms -
however indirect the causal chain may be, and however small the effect on number
of offspring produced. This is because differential reproduction (and not
survival per se) is the engine that drives natural selection. Consider the fate
of a circuit that had the effect, on average, of enhancing the reproductive rate
of the organisms that sported it, but shortened their average lifespan in so
doing (one that causes mothers to risk death to save their children, for
example). If this effect persisted over many generations, then its frequency in
the population would increase. In contrast, any circuit whose average effect was
to decrease the reproductive rate of the organisms that had it would eventually
disappear from the population. Most adaptive problems have to do with how an
organism makes its living: what it eats, what eats it, who it mates with, who it
socializes with, how it communicates, and so on. The only kind of problems that
natural selection can design circuits for solving are adaptive problems.
Obviously, we are able to solve problems that no hunter-gatherer ever had to
solve -- we can learn math, drive cars, use computers. Our ability to solve
other kinds of problems is a side-effect or by-product of circuits that were
designed to solve adaptive problems. For example, when our ancestors became
bipedal -- when they started walking on two legs instead of four -- they had to
develop a very good sense of balance. And we have very intricate mechanisms in
our inner ear that allow us to achieve our excellent sense of balance. Now the
fact that we can balance well on two legs while moving means that we can do
other things besides walk -- it means we can skateboard or ride the waves on a
surfboard. But our hunter-gatherer ancestors were not tunneling through curls in
the primordial soup. The fact that we can surf and skateboard are mere by
products of adaptations designed for balancing while walking on two legs.
Principle 3. Consciousness is just the tip of the iceberg; most of what goes on
in your mind is hidden from you. As a result, your conscious experience can
mislead you into thinking that our circuitry is simpler that it really is. Most
problems that you experience as easy to solve are very difficult to solve -
they require very complicated neural circuitry
You are not, and cannot become, consciously aware of most of your brain's
ongoing activities. Think of the brain as the entire federal government, and of
your consciousness as the President of the United States. Now think of yourself
-- the self that you consciously experience as "you" -- as the President. If you
were President, how would you know what is going on in the world? Members of the
Cabinet, like the Secretary of Defense, would come and tell you things -- for
example, that the Bosnian Serbs are violating their cease-fire agreement. How do
members of the Cabinet know things like this? Because thousands of bureaucrats
in the State Department, thousands of CIA operatives in Serbia and other parts
of the world, thousands of troops stationed overseas, and hundreds of
investigative reporters are gathering and evaluating enormous amounts of
information from all over the world. But you, as President, do not -- and in
fact, cannot -- know what each of these thousands of individuals were doing when
gathering all this information over the last few months -- what each of them
saw, what each of them read, who each of them talked to, what conversations were
clandestinely taped, what offices were bugged. All you, as President, know is
the final conclusion that the Secretary of Defense came to based on the
information that was passed on to him. And all he knows is what other high level
officials passed on to him, and so on. In fact, no single individual knows all
of the facts about the situation, because these facts are distributed among
thousands of people. Moreover, each of the thousands of individuals involved
knows all kinds of details about the situation that they decided were not
important enough to pass on to higher levels.
So it is with your conscious experience. The only things you become aware of are
a few high level conclusions passed on by thousands and thousands of specialized
mechanisms: some that are gathering sensory information from the world, others
that are analyzing and evaluating that information, checking for
inconsistencies, filling in the blanks, figuring out what it all means.
It is important for any scientist who is studying the human mind to keep this in
mind. In figuring out how the mind works, your conscious experience of yourself
and the world can suggest some valuable hypotheses. But these same intuitions
can seriously mislead you as well. They can fool you into thinking that our
neural circuitry is simpler that it really is.
Consider vision. Your conscious experience tells you that seeing is simple: You
open your eyes, light hits your retina, and -- voila! -- you see. It is
effortless, automatic, reliable, fast, unconscious and requires no explicit
instruction -- no one has to go to school to learn how to see. But this apparent
simplicity is deceptive. Your retina is a two-dimensional sheet of light
sensitive cells covering the inside back of your eyeball. Figuring out what
three-dimensional objects exist in the world based only on the light-dependent
chemical reactions occurring in this two dimensional array of cells poses
enormously complex problems -- so complex, in fact, that no computer programmer
has yet been able to create a robot that can see the way we do. You see with
your brain, not just your eyes, and your brain contains a vast array of
dedicated, special purpose circuits -- each set specialized for solving a
different component of the problem. You need all kinds of circuits just to see
your mother walk, for example. You have circuits that are specialized for (1)
analyzing the shape of objects; (2) detecting the presence of motion; (3)
detecting the direction of motion; (4) judging distance; (5) analyzing color;
(6) identifying an object as human; (7) recognizing that the face you see is
Mom's face, rather than someone else's. Each individual circuit is shouting its
information to higher level circuits, which check the "facts" generated by one
circuit against the "facts" generated by the others, resolving contradictions.
Then these conclusions are handed over to even higher level circuits, which
piece them all together and hand the final report to the President -- your
consciousness. But all this "president" ever becomes aware of is the sight of
Mom walking. Although each circuit is specialized for solving a delimited task,
they work together to produce a coordinated functional outcome -- in this case,
your conscious experience of the visual world. Seeing is effortless, automatic,
reliable, and fast precisely because we have all this complicated, dedicated
In other words, our intuitions can deceive us. Our conscious experience of an
activity as "easy" or "natural" can lead us to grossly underestimate the
complexity of the circuits that make it possible. Doing what comes "naturally",
effortlessly, or automatically is rarely simple from an engineering point of
view. To find someone beautiful, to fall in love, to feel jealous -- all can
seem as simple and automatic and effortless as opening your eyes and seeing. So
simple that it seems like there is nothing much to explain. But these activities
feel effortless only because there is a vast array of complex neural circuitry
supporting and regulating them.
Principle 4. Different neural circuits are specialized for solving different
A basic engineering principle is that the same machine is rarely capable of
solving two different problems equally well. We have both screw drivers and saws
because each solves a particular problem better than the other. Just imagine
trying to cut planks of wood with a screw driver or to turn screws with a saw.
Our body is divided into organs, like the heart and the liver, for exactly this
reason. Pumping blood throughout the body and detoxifying poisons are two very
different problems. Consequently, your body has a different machine for solving
each of them. The design of the heart is specialized for pumping blood; the
design of the liver is specialized for detoxifying poisons. Your liver can't
function as a pump, and your heart isn't any good at detoxifying poisons.
For the same reason, our minds consist of a large number of circuits that are
functionally specialized. For example, we have some neural circuits whose design
is specialized for vision. All they do is help you see. The design of other
neural circuits is specialized for hearing. All they do is detect changes in air
pressure, and extract information from it. They do not participate in vision,
vomiting, vanity, vengeance, or anything else. Still other neural circuits are
specialized for sexual attraction -- i.e., they govern what you find sexually
arousing, what you regard as beautiful, who you'd like to date, and so on.
We have all these specialized neural circuits because the same mechanism is
rarely capable of solving different adaptive problems. For example, we all have
neural circuitry designed to choose nutritious food on the basis of taste and
smell -- circuitry that governs our food choice. But imagine a woman who used
this same neural circuitry to choose a mate. She would choose a strange mate
indeed (perhaps a huge chocolate bar?). To solve the adaptive problem of finding
the right mate, our choices must be guided by qualitatively different standards
than when choosing the right food, or the right habitat. Consequently, the brain
must be composed of a large collection of circuits, with different circuits
specialized for solving different problems. You can think of each of these
specialized circuits as a mini-computer that is dedicated to solving one
problem. Such dedicated mini-computers are sometimes called modules. There is,
then, a sense in which you can view the brain as a collection of dedicated mini
computers -- a collection of modules. There must, of course, be circuits whose
design is specialized for integrating the output of all these dedicated mini
computers to produce behavior. So, more precisely, one can view the brain as a
collection of dedicated mini-computers whose operations are functionally
integrated to produce behavior.
Psychologists have long known that the human mind contains circuits that are
specialized for different modes of perception, such as vision and hearing. But
until recently, it was thought that perception and, perhaps, language were the
only activities caused by cognitive processes that are specialized (e.g., Fodor,
1983). Other cognitive functions -- learning, reasoning, decision-making -- were
thought to be accomplished by circuits that are very general purpose: jacks-of
all-trades, but masters of none. Prime candidates were "rational" algorithms:
ones that implement formal methods for inductive and deductive reasoning, such
as Bayes's rule or the propositional calculus (a formal logic). "General
intelligence" -- a hypothetical faculty composed of simple reasoning circuits
that are few in number, content-independent, and general purpose -- was thought
to be the engine that generates solutions to reasoning problems. The flexibility
of human reasoning -- that is, our ability to solve many different kinds of
problems -- was thought to be evidence for the generality of the circuits that
An evolutionary perspective suggests otherwise (Tooby & Cosmides, 1992).
Biological machines are calibrated to the environments in which they evolved,
and they embody information about the stably recurring properties of these
ancestral worlds. (E.g., human color constancy mechanisms are calibrated to
natural changes in terrestrial illumination; as a result, grass looks green at
both high noon and sunset, even though the spectral properties of the light it
reflects have changed dramatically.) Rational algorithms do not, because they
are content-independent. Figure 2 shows two rules of inference from the
propositional calculus, a system that allows one to deduce true conclusions from
true premises, no matter what the subject matter of the premises is -- no
mattter what P and Q refer to. Bayes's rule, an equation for computing the
probability of a hypothesis given data, is also content-independent. It can be
applied indiscriminately to medical diagnosis, card games, hunting success, or
any other subject matter. It contains no domain-specific knowledge, so it cannot
support inferences that would apply to mate choice, for example, but not to
hunting. (That is the price of content-independence.)
Evolved problem-solvers, however, are equipped with crib sheets: they come to a
problem already "knowing" a lot about it. For example, a newborn's brain has
response systems that "expect" faces to be present in the environment: babies
less than 10 minutes old turn their eyes and head in response to face-like
patterns, but not to scrambled versions of the same pattern with identical
spatial frequencies (Johnson & Morton, 1991). Infants make strong ontological
assumptions about how the world works and what kinds of things it contains -
even at 2 1/2 months (the point at which they can see well enough to be tested).
They assume, for example, that it will contain rigid objects that are continuous
in space and time, and they have perfered ways of parsing the world into
separate objects (e.g., Baillergeon, 1986; Spelke, 1990). Ignoring shape, color,
and texture, they treat any surface that is cohesive, bounded, and moves as a
unit as a single object. When one solid object appears to pass through another,
these infants are surprised. Yet a system with no "privileged" hypotheses -- a
truly "open-minded" system -- would be undisturbed by such displays. In watching
objects interact, babies less than a year old distinguish causal events from
non-causal ones that have similar spatio-temporal properties; they distinguish
objects that move only when acted upon from ones that are capable of self
generated motion (the inanimate/animate distinction); they assume that the self
propelled movement of animate objects is caused by invisible internal states -
goals and intentions -- whose presence must be inferred, since internal states
cannot be seen (Baron-Cohen, 1995; Leslie, 1988; 1994). Toddlers have a well
developed "mind-reading" system, which uses eye direction and movement to infer
what other people want, know, and believe (Baron-Cohen, 1995). (When this system
is impaired, as in autism, the child cannot infer what others believe.) When an
adult utters a word-like sound while pointing to a novel object, toddlers assume
the word refers to the whole object, rather than one of its parts (Markman,
Without these privileged hypotheses -- about faces, objects, physical causality,
other minds, word meanings, and so on -- a developing child could learn very
little about its environment. For example, a child with autism who has a normal
IQ and intact perceptual systems is, nevertheless, unable to make simple
inferences about mental states (Baron-Cohen, 1995). Children with Williams
syndrome are profoundly retarded and have difficulty learning even very simple
spatial tasks, yet they are good at inferring other people's mental states. Some
of their reasoning mechanisms are damaged, but their mind-reading system is
Different problems require different crib sheets. For example, knowledge about
intentions, beliefs, and desires, which allows one to infer the behavior of
persons, will be misleading if applied to inanimate objects. Two machines are
better than one when the crib sheet that helps solve problems in one domain is
misleading in another. This suggests that many evolved computational mechanisms
will be domain-specific: they will be activated in some domains but not others.
Some of these will embody rational methods, but others will have special purpose
inference procedures that respond not to logical form but to content-types -
procedures that work well within the stable ecological structure of a particular
domain, even though they might lead to false or contradictory inferences if they
were activated outside of that domain.
The more crib sheets a system has, the more problems it can solve. A brain
equipped with a multiplicity of specialized inference engines will be able to
generate sophisticated behavior that is sensitively tuned to its environment. In
this view, the flexibility and power often attributed to content-independent
algorithms is illusory. All else equal, a content-rich system will be able to
infer more than a content-poor one.
Machines limited to executing Bayes's rule, modus ponens, and other "rational"
procedures derived from mathematics or logic are computationally weak compared
to the system outlined above (Tooby and Cosmides, 1992). The theories of
rationality they embody are "environment-free" -- they were designed to produce
valid inferences in all domains. They can be applied to a wide variety of
domains, however, only because they lack any information that would be helpful
in one domain but not in another. Having no crib sheets, there is little they
can deduce about a domain; having no privileged hypotheses, there is little they
can induce before their operation is hijacked by combinatorial explosion. The
difference between domain-specific methods and domain-independent ones is akin
to the difference between experts and novices: experts can solve problems faster
and more efficiently than novices because they already know a lot about the
William James's view of the mind, which was ignored for much of the 20th
century, is being vindicated today. There is now evidence for the existence of
circuits that are specialized for reasoning about objects, physical causality,
number, the biological world, the beliefs and motivations of other individuals,
and social interactions (for review, see Hirschfeld & Gelman, 1994). It is now
known that the learning mechanisms that govern the acquisition of language are
different from those that govern the acquisition of food aversions, and both of
these are different from the learning mechanisms that govern the acquisition of
snake phobias (Garcia, 1990; Pinker, 1994; Mineka & Cooke, 1985). Examples
"Instincts" are often thought of as the polar opposite of "reasoning" and
"learning". Homo sapiens are thought of as the "rational animal", a species
whose instincts, obviated by culture, were erased by evolution. But the
reasoning circuits and learning circuits discussed above have the following five
properties: (1) they are complexly structured for solving a specific type of
adaptive problem, (2) they reliably develop in all normal human beings, (3) they
develop without any conscious effort and in the absence of any formal
instruction, (4) they are applied without any conscious awareness of their
underlying logic, and (5) they are distinct from more general abilities to
process information or behave intelligently. In other words, they have all the
hallmarks of what one usually thinks of as an "instinct" (Pinker, 1994). In
fact, one can think of these special purpose computational systems as reasoning
instincts and learning instincts. They make certain kinds of inferences just as
easy, effortless, and "natural" to us as humans, as spinning a web is to a
spider or dead-reckoning is to a desert ant.
Students often ask whether a behavior was caused by "instinct" or "learning". A
better question would be "which instincts caused the learning?"
Principle 5. Our modern skulls house a stone age mind.
Natural selection, the process that designed our brain, takes a long time to
design a circuit of any complexity. The time it takes to build circuits that are
suited to a given environment is so slow it is hard to even imagine -- it's like
a stone being sculpted by wind-blown sand. Even relatively simple changes can
take tens of thousands of years.
The environment that humans -- and, therefore, human minds -- evolved in was
very different from our modern environment. Our ancestors spent well over 99% of
our species' evolutionary history living in hunter-gatherer societies. That
means that our forebearers lived in small, nomadic bands of a few dozen
individuals who got all of their food each day by gathering plants or by hunting
animals. Each of our ancestors was, in effect, on a camping trip that lasted an
entire lifetime, and this way of life endured for most of the last 10 million
Generation after generation, for 10 million years, natural selection slowly
sculpted the human brain, favoring circuitry that was good at solving the day
to-day problems of our hunter-gatherer ancestors -- problems like finding mates,
hunting animals, gathering plant foods, negotiating with friends, defending
ourselves against aggression, raising children, choosing a good habitat, and so
on. Those whose circuits were better designed for solving these problems left
more children, and we are descended from them.
Our species lived as hunter-gatherers 1000 times longer than as anything else.
The world that seems so familiar to you and me, a world with roads, schools,
grocery stores, factories, farms, and nation-states, has lasted for only an
eyeblink of time when compared to our entire evolutionary history. The computer
age is only a little older than the typical college student, and the industrial
revolution is a mere 200 years old. Agriculture first appeared on earth only
10,000 years ago, and it wasn't until about 5,000 years ago that as many as half
of the human population engaged in farming rather than hunting and gathering.
Natural selection is a slow process, and there just haven't been enough
generations for it to design circuits that are well-adapted to our post
In other words, our modern skulls house a stone age mind. The key to
understanding how the modern mind works is to realize that its circuits were not
designed to solve the day-to-day problems of a modern American -- they were
designed to solve the day-to-day problems of our hunter-gatherer ancestors.
These stone age priorities produced a brain far better at solving some problems
than others. For example, it is easier for us to deal with small, hunter
gatherer-band sized groups of people than with crowds of thousands; it is easier
for us to learn to fear snakes than electric sockets, even though electric
sockets pose a larger threat than snakes do in most American communities. In
many cases, our brains are better at solving the kinds of problems our ancestors
faced on the African savannahs than they are at solving the more familiar tasks
we face in a college classroom or a modern city. In saying that our modern
skulls house a stone age mind, we do not mean to imply that our minds are
unsophisticated. Quite the contrary: they are very sophisticated computers,
whose circuits are elegantly designed to solve the kinds of problems our
ancestors routinely faced.
A necessary (though not sufficient) component of any explanation of behavior -
modern or otherwise -- is a description of the design of the computational
machinery that generates it. Behavior in the present is generated by
information-processing mechanisms that exist because they solved adaptive
problems in the past -- in the ancestral environments in which the human line
For this reason, evolutionary psychology is relentlessly past-oriented.
Cognitive mechanisms that exist because they solved problems efficiently in the
past will not necessarily generate adaptive behavior in the present. Indeed, EPs
reject the notion that one has "explained" a behavior pattern by showing that it
promotes fitness under modern conditions (for papers on both sides of this
controversy, see responses in the same journal issue to Symons (1990) and Tooby
and Cosmides (1990a)).
Although the hominid line is thought to have evolved on the African savannahs,
the environment of evolutionary adaptedness, or EEA, is not a place or time. It
is the statistical composite of selection pressures that caused the design of an
adaptation. Thus the EEA for one adaptation may be different from that for
another. Conditions of terrestrial illumination, which form (part of) the EEA
for the vertebrate eye, remained relatively constant for hundreds of millions of
years (until the invention of the incandescent bulb); in contrast, the EEA that
selected for mechanisms that cause human males to provision their offspring -- a
situation that departs from the typical mammalian pattern -- appears to be only
about two million years old.
* * *
The Five Principles are tools for thinking about psychology, which can be
applied to any topic: sex and sexuality, how and why people cooperate, whether
people are rational, how babies see the world, conformity, aggression, hearing,
vision, sleeping, eating, hypnosis, schizophrenia and on and on. The framework
they provide links areas of study, and saves one from drowning in particularity.
Whenever you try to understand some aspect of human behavior, they encourage you
to ask the following fundamental questions:
Where in the brain are the relevant circuits and how, physically, do they
What kind of information is being processed by these circuits?
What information-processing programs do these circuits embody? and
What were these circuits designed to accomplish (in a hunter-gatherer
Now that we have dispensed with this preliminary throat-clearing, it is time to
explain the theoretical framework from which the Five Principles -- and other
fundamentals of evolutionary psychology -- were derived.
Understanding the Design of Organisms
Adaptationist Logic and Evolutionary Psychology
Phylogenetic versus adaptationist explanations. The goal of Darwin's theory was
to explain phenotypic design: Why do the beaks of finchs differ from one species
to the next? Why do animals expend energy attracting mates that could be spent
on survival? Why are human facial expressions of emotion similar to those found
in other primates?
Two of the most important evolutionary principles accounting for the
characteristics of animals are (1) common descent, and (2) adaptation driven by
natural selection. If we are all related to one another, and to all other
species, by virtue of common descent, then one might expect to find similarities
between humans and their closest primate relatives. This phylogenetic approach
has a long history in psychology: it prompts the search for phylogenetic
continuities implied by the inheritance of homologous features from common
An adaptationist approach to psychology leads to the search for adaptive design,
which usually entails the examination of niche-differentiated mental abilities
unique to the species being investigated. George Williams's 1966 book,
Adaptation and Natural Selection, clarified the logic of adaptationism. In so
doing, this work laid the foundations of modern evolutionary psychology.
Evolutionary psychology can be thought of as the application of adaptationist
logic to the study of the architecture of the human mind.
Why does structure reflect function? In evolutionary biology, there are several
different levels of explanation that are complementary and mutually compatible.
Explanation at one level (e.g., adaptive function) does not preclude or
invalidate explanations at another (e.g., neural, cognitive, social, cultural,
economic). EPs use theories of adaptive function to guide their investigations
of phenotypic structures. Why is this possible?
The evolutionary process has two components: chance and natural selection.
Natural selection is the only component of the evolutionary process that can
introduce complex functional organization in to a species' phenotype (Dawkins,
1986; Williams, 1966).
The function of the brain is to generate behavior that is sensitively contingent
upon information from an organism's environment. It is, therefore, an
information-processing device. Neuroscientists study the physical structure of
such devices, and cognitive psychologists study the information-processing
programs realized by that structure. There is, however, another level of
explanation -- a functional level. In evolved systems, form follows function.
The physical structure is there because it embodies a set of programs; the
programs are there because they solved a particular problem in the past. This
functional level of explanation is essential for understanding how natural
selection designs organisms.
An organism's phenotypic structure can be thought of as a collection of "design
features" -- micro-machines, such as the functional components of the eye or
liver. Over evolutionary time, new design features are added or discarded from
the species' design because of their consequences. A design feature will cause
its own spread over generations if it has the consequence of solving adaptive
problems: cross-generationally recurrent problems whose solution promotes
reproduction, such as detecting predators or detoxifying poisons. If a more
sensitive retina, which appeared in one or a few individuals by chance mutation,
allows predators to be detected more quickly, individuals who have the more
sensitive retina will produce offspring at a higher rate than those who lack it.
By promoting the reproduction of its bearers, the more sensitive retina thereby
promotes its own spread over the generations, until it eventually replaces the
earlier-model retina and becomes a universal feature of that species' design.
Hence natural selection is a feedback process that "chooses" among alternative
designs on the basis of how well they function. It is a hill-climbing process,
in which a design feature that solves an adaptive problem well can be
outcompeted by a new design feature that solves it better. This process has
produced exquisitely engineered biological machines -- the vertebrate eye,
photosynthetic pigments, efficient foraging algorithms, color constancy systems
-- whose performance is unrivaled by any machine yet designed by humans.
By selecting designs on the basis of how well they solve adaptive problems, this
process engineers a tight fit between the function of a device and its
structure. To understand this causal relationship, biologists had to develop a
theoretical vocabulary that distinguishes between structure and function. In
evolutionary biology, explanations that appeal to the structure of a device are
sometimes called "proximate" explanations. When applied to psychology, these
would include explanations that focus on genetic, biochemical, physiological,
developmental, cognitive, social, and all other immediate causes of behavior.
Explanations that appeal to the adaptive function of a device are sometimes
called "distal" or "ultimate" explanations, because they refer to causes that
operated over evolutionary time.
Knowledge of adaptive function is necessary for carving nature at the joints. An
organism's phenotype can be partitioned into adaptations, which are present
because they were selected for, by-products, which are present because they are
causally coupled to traits that were selected for (e.g., the whiteness of bone),
and noise, which was injected by the stochastic components of evolution. Like
other machines, only narrowly defined aspects of organisms fit together into
functional systems: most ways of describing the system will not capture its
functional properties. Unfortunately, some have misrepresented the well
supported claim that selection creates functional organization as the obviously
false claim that all traits of organisms are funtional -- something no sensible
evolutionary biologist would ever maintain. Furthermore, not all behavior
engaged in by organisms is adaptive. A taste for sweet may have been adaptive in
ancestral environments where vitamin-rich fruit was scarce, but it can generate
maladaptive behavior in a modern environment flush with fast-food restaurants.
Moreover, once an information-processing mechanism exists, it can be deployed in
activities that are unrelated to its original function -- because we have
evolved learning mechanisms that cause language acquisition, we can learn to
write. But these learning mechanisms were not selected for because they caused
Design evidence. Adaptations are problem-solving machines, and can be identified
using the same standards of evidence that one would use to recognize a human
made machine: design evidence. One can identify a machine as a TV rather than a
stove by finding evidence of complex functional design: showing, e.g., that it
has many coordinated design features (antennaes, cathode ray tubes, etc.) that
are complexly specialized for transducing TV waves and transforming them into a
color bit map (a configuration that is unlikely to have risen by chance alone),
whereas it has virtually no design features that would make it good at cooking
food. Complex functional design is the hallmark of adaptive machines as well.
One can identify an aspect of the phenotype as an adaptation by showing that (1)
it has many design features that are complexly specialized for solving an
adaptive problem, (2) these phenotypic properties are unlikely to have arisen by
chance alone, and (3) they are not better explained as the by-product of
mechanisms designed to solve some alternative adaptive problem. Finding that an
architectural element solves an adaptive problem with "reliability, efficiency,
and economy" is prima facie evidence that one has located an adaptation
Design evidence is important not only for explaining why a known mechanism
exists, but also for discovering new mechanisms, ones that no one had thought to
look for. EPs also use theories of adaptive function heuristically, to guide
their investigations of phenotypic design.
Those who study species from an adaptationist perspective adopt the stance of an
engineer. In discussing sonar in bats, e.g., Dawkins proceeds as follows: "...I
shall begin by posing a problem that the living machine faces, then I shall
consider possible solutions to the problem that a sensible engineer might
consider; I shall finally come to the solution that nature has actually adopted"
(1986, pp. 21-22). Engineers figure out what problems they want to solve, and
then design machines that are capable of solving these problems in an efficient
manner. Evolutionary biologists figure out what adaptive problems a given
species encountered during its evolutionary history, and then ask themselves,
"What would a machine capable of solving these problems well under ancestral
conditions look like?" Against this background, they empically explore the
design features of the evolved machines that, taken together, comprise an
organism. Definitions of adaptive problems do not, of course, uniquely specify
the design of the mechanisms that solve them. Because there are often multiple
ways of acheiving any solution, empirical studies are needed to decide "which
nature has actually adopted". But the more precisely one can define an adaptive
information-processing problem -- the "goal" of processing -- the more clearly
one can see what a mechanism capable of producing that solution would have to
look like. This research strategy has dominated the study of vision, for
example, so that it is now commonplace to think of the visual system as a
collection of functionally integrated computational devices, each specialized
for solving a different problem in scene analysis -- judging depth, detecting
motion, analyzing shape from shading, and so on. In our own research, we have
applied this strategy to the study of social reasoning (see below).
To fully understand the concept of design evidence, we need to consider how an
adaptationist thinks about nature and nurture.
Nature and nurture: An adaptationist perspective
Debates about the "relative contribution" during development of "nature" and
"nurture" have been among the most contentious in psychology. The premises that
underlie these debates are flawed, yet they are so deeply entrenched that many
people have difficulty seeing that there are other ways to think about these
Evolutionary psychology is not just another swing of the nature/nurture
pendulum. A defining characteristic of the field is the explicit rejection of
the usual nature/nurture dichotomies -- instinct vs. reasoning, innate vs.
learned, biological vs. cultural. What effect the environment will have on an
organism depends critically on the details of its evolved cognitive
architecture. For this reason, coherent "environmentalist" theories of human
behavior all make "nativist" claims about the exact form of our evolved
psychological mechanisms. For an EP, the real scientific issues concern the
design, nature, and number of these evolved mechanisms, not "biology versus
culture" or other malformed oppositions.
There are several different "nature-nurture" issues, which are usually
conflated. Let's pull them apart and look at them separately, because some of
them are non-issues whereas others are real issues.
Focus on architecture. At a certain level of abstraction, every species has a
universal, species-typical evolved architecture. For example, one can open any
page of the medical textbook, Gray's Anatomy, and find the design of this
evolved architecture described down to the minutest detail -- not only do we all
have a heart, two lungs, a stomach, intestines, and so on, but the book will
describe human anatomy down to the particulars of nerve connections. This is not
to say there is no biochemical individuality: No two stomachs are exactly alike
-- they vary a bit in quantitative properties, such as size, shape, and how much
HCl they produce. But all humans have stomachs and they all have the same basic
functional design -- each is attached at one end to an esophagus and at the
other to the small intestine, each secretes the same chemicals necessary for
digestion, and so on. Presumably, the same is true of the brain and, hence, of
the evolved architecture of our cognitive programs -- of the information
processing mechanisms that generate behavior. Evolutionary psychology seeks to
characterize the universal, species-typical architecture of these mechanisms.
The cognitive architecture, like all aspects of the phenotype from molars to
memory circuits, is the joint product of genes and environment. But the
development of architecture is buffered against both genetic and environmental
insults, such that it reliably develops across the (ancestrally) normal range of
human environments. EPs do not assume that genes play a more important role in
development than the environment does, or that "innate factors" are more
important than "learning". Instead, EPs reject these dichotomies as ill
Evolutionary psychology is not behavior genetics. Behavior geneticists are
interested in the extent to which differences between people in a given
environment can be accounted for by differences in their genes. EPs are
interested in individual differences only insofar as these are the manifestation
of an underlying architecture shared by all human beings. Because their genetic
basis is universal and species-typical, the heritability of complex adaptations
(of the eye, for example) is usually low, not high. Moreover, sexual
recombination constrains the design of genetic systems, such that the genetic
basis of any complex adaptation (such as a cognitive mechanism) must be
universal and species-typical (Tooby and Cosmides, 1990b). This means the
genetic basis for the human cognitive architecture is universal, creating what
is sometimes called the psychic unity of humankind. The genetic shuffle of
meiosis and sexual recombination can cause individuals to differ slightly in
quantitative properties that do not disrupt the functioning of complex
adaptations. But two individuals do not differ in personality or morphology
because one has the genetic basis for a complex adaptation that the other lacks.
The same principle applies to human populations: from this perspective, there is
no such thing as "race".
In fact, evolutionary psychology and behavior genetics are animated by two
radically different questions:
What is the universal, evolved architecture that we all share by virtue of
being humans? (evolutionary psychology)
Given a large population of people in a specific environment, to what extent
can differences between these people be accounted for by differences in their
genes? (behavior genetics)
The second question is usually answered by computing a heritability coefficient,
based on (for example) studies of identical and fraternal twins. "Which
contributes more to nearsightedness, genes or environment" (an instance of the
second question), has no fixed answer: the "heritability" of a trait can vary
from one place to the next, precisely because environments do affect
A heritability coefficient measures sources of variance in a population (for
example, in a forest of oaks, to what extent are differences in height
correlated with differences in sunlight, all else equal?). It tells you nothing
about what caused the development of an individual. Let's say that for height,
80% of the variance in a forest of oaks is caused by variation in their genes.
This does not mean that the height of the oak tree in your yard is "80%
genetic". (What could this possibly mean? Did genes contribute more to your
oak's height than sunlight? What percent of its height was caused by nitrogen in
the soil? By rainfall? By the partial pressure of CO2?) When applied to an
individual, such percents are meaningless, because all of these factors are
necessary for a tree to grow. Remove any one, and the height will be zero.
Joint product of genes and environment. Confusing individuals with populations
has led many people to define "the" nature-nurture question in the following
way: What is more important in determining an (individual) organism's phenotype,
its genes or its environment?
Any developmental biologist knows that this is a meaningless question. Every
aspect of an organism's phenotype is the joint product of its genes and its
environment. To ask which is more important is like asking, Which is more
important in determining the area of a rectangle, the length or the width? Which
is more important in causing a car to run, the engine or the gasoline? Genes
allow the environment to influence the development of phenotypes.
Indeed, the developmental mechanisms of many organisms were designed by natural
selection to produce different phenotypes in different environments. Certain
fish can change sex, for example. Blue-headed wrasse live in social groups
consisting of one male and many females. If the male dies, the largest female
turns into a male. The wrasse are designed to change sex in response to a social
cue -- the presence or absence of a male.
With a causal map of a species' developmental mechanisms, you can change the
phenotype that develops by changing its environment. Imagine planting one seed
from an arrowleaf plant in water, and a genetically identical seed on dry land.
The one in water would develop wide leaves, and the one on land would develop
narrow leaves. Responding to this dimension of environmental variation is part
of the species' evolved design. But this doesn't mean that just any aspect of
the environment can affect the leaf width of an arrowleaf plant. Reading poetry
to it doesn't affect its leaf width. By the same token, it doesn't mean that it
is easy to get the leaves to grow into just any shape: short of a pair of
scissors, it is probably very difficult to get the leaves to grow into the shape
of the Starship Enterprise.
People tend to get mystical about genes; to treat them as "essences" that
inevitably give rise to behaviors, regardless of the environment in which they
are expressed. But genes are simply regulatory elements, molecules that arrange
their surrounding environment into an organism. There is nothing magical about
the process: DNA is transcribed into RNA; within cells, at the ribosomes, the
RNA is translated into proteins -- the enzymes -- that regulate development.
There is no aspect of the phenotype that cannot be influenced by some
environmental manipulation. It just depends on how ingenious or invasive you
want to be. If you drop a human zygote (a fertilized human egg) into liquid
nitrogen, it will not develop into an infant. If you were to shoot electrons at
the zygote's ribosomes in just the right way, you could influence the way in
which the RNA is translated into proteins. By continuing to do this you could,
in principle, cause a human zygote to develop into a watermelon or a whale.
There is no magic here, only causality.
Present at birth? Sometimes people think that to show that an aspect of the
phenotype is part of our evolved architecture, one must show that it is present
from birth. But this is to confuse an organism's "initial state" with its
evolved architecture. Infants do not have teeth at birth -- they develop them
quite awhile after birth. But does this mean they "learn" to have teeth? What
about breasts? Beards? One expects organisms to have mechanisms that are adapted
to their particular life stage (consider the sea squirt!) -- after all, the
adaptive problems an infant faces are different from those an adolescent faces.
This misconception frequently leads to misguided arguments. For example, people
think that if they can show that there is information in the culture that
mirrors how people behave, then that is the cause of their behavior. So if they
see that men on TV have trouble crying, they assume that their example is
causing boys to be afraid to cry. But which is cause and which effect? Does the
fact that men don't cry much on TV teach boys to not cry, or does it merely
reflect the way boys normally develop? In the absence of research on the
particular topic, there is no way of knowing. (To see this, just think about how
easy it would be to argue that girls learn to have breasts. Consider the peer
pressure during adolescence for having breasts! the examples on TV of glamorous
models! -- the whole culture reinforces the idea that women should have breasts,
therefore...adolescent girls learn to grow breasts.)
In fact, an aspect of our evolved architecture can, in principle, mature at any
point in the life-cycle, and this applies to the cognitive programs of our brain
just as much as it does to other aspects of our phenotype.
Is domain-specificity politically incorrect? Sometimes people favor the notion
that everything is "learned" -- by which they mean "learned via general purpose
circuits" -- because they think it supports democratic and egalitarian ideals.
They think it means anyone can be anything. But the notion that anyone can be
anything gets equal support, whether our circuits are specialized or general.
When we are talking about a species' evolved architecture, we are talking about
something that is universal and species-typical -- something all of us have.
This is why the issue of specialization has nothing to do with "democratic,
egalitarian ideals" -- we all have the same basic biological endowment, whether
it is in the form of general purpose mechanisms or special purpose ones. If we
all have a special purpose "language acquisition device", for example (see
Pinker, this volume), we are all on an "equal footing" when it comes to learning
language, just as we would be if we learned language via general purpose
"Innate" is not the opposite of "learned". For EPs, the issue is never
"learning" versus "innateness" or "learning" versus "instinct". The brain must
have a certain kind of structure for you to learn anything at all -- after all,
three pound bowls of oatmeal don't learn, but three pound brains do. If you
think like an engineer, this will be clear. To learn, there must be some
mechanism that causes this to occur. Since learning cannot occur in the absence
of a mechanism that causes it, the mechanism that causes it must itself be
unlearned -- must be "innate". Certain learning mechanisms must therefore be
aspects of our evolved architecture that reliably develop across the kinds of
environmental variations that humans normally encountered during their
evolutionary history. We must, in a sense, have what you can think of as "innate
learning mechanisms" or "learning instincts". The interesting question is what
are these unlearned programs? Are they specialized for learning a particular
kind of thing, or are they designed to solve more general problems? This brings
us back to Principle 4.
Specialized or general purpose? One of the few genuine nature-nurture issues
concerns the extent to which a mechanism is specialized for producing a given
outcome. Most nature/nurture dichotomies disappear when one understands more
about developmental biology, but this one does not. For EPs, the important
question is, What is the nature of our universal, species-typical evolved
cognitive programs? What kind of circuits do we actually have?
The debate about language acquisition brings this issue into sharp focus: Do
general purpose cognitive programs cause children to learn language, or is
language learning caused by programs that are specialized for performing this
task? This cannot be answered a priori. It is an empirical question, and the
data collected so far suggest the latter (Pinker, 1994, this volume).
For any given behavior you observe, there are three possibilities:
It is the product of general purpose programs (if such exist);
It is the product of cognitive programs that are specialized for producing
that behavior; or
It is a by-product of specialized cognitive programs that evolved to solve a
different problem. (Writing, which is a recent cultural invention, is an
example of the latter.)
More nature allows more nurture. There is not a zero-sum relationship between
"nature" and "nurture". For EPs, "learning" is not an explanation -- it is a
phenomenon that requires explanation. Learning is caused by cognitive
mechanisms, and to understand how it occurs, one needs to know the computational
structure of the mechanisms that cause it. The richer the architecture of these
mechanisms, the more an organism will be capable of learning -- toddlers can
learn English while (large-brained) elephants and the family dog cannot because
the cognitive architecture of humans contains mechanisms that are not present in
that of elephants or dogs. Furthermore, "learning" is a unitary phenomenon: the
mechanisms that cause the acquisition of grammar, for example, are different
from those that cause the acquisition of snake phobias. (The same goes for
What evolutionary psychology is not. For all the reasons discussed above, EPs
expect the human mind will be found to contain a large number of information
processing devices that are domain-specific and functionally specialized. The
proposed domain-specificity of many of these devices separates evolutionary
psychology from those approaches to psychology that assume the mind is composed
of a small number of domain general, content-independent, "general purpose"
mechanisms -- the Standard Social Science Model.
It also separates evolutionary psychology from those approaches to human
behavioral evolution in which it is assumed (usually implicitly) that "fitness
maximization" is a mentally (though not consciously) represented goal, and that
the mind is composed of domain general mechanisms that can "figure out" what
counts as fitness-maximizing behavior in any environment -- even evolutionarily
novel ones (Cosmides and Tooby, 1987; Symons, 1987, 1992). Most EPs acknowledge
the multipurpose flexibility of human thought and action, but believe this is
caused by a cognitive achitecture that contains a large number of evolved
Reasoning instincts: An example
In some of our own research, we have been exploring the hypothesis that the
human cognitive architecture contains circuits specialized for reasoning about
adaptive problems posed by the social world of our ancestors. In categorizing
social interactions, there are two basic consequences humans can have on each
other: helping or hurting, bestowing benefits or inflicting costs. Some social
behavior is unconditional: one nurses an infant without asking it for a favor in
return, for example. But most social acts are conditionally delivered. This
creates a selection pressure for cognitive designs that can detect and
understand social conditionals reliably, precisely, and economcally (Cosmides,
1985, 1989; Cosmides & Tooby, 1989, 1992). Two major categories of social
conditionals are social exchange and threat -- conditional helping and
conditional hurting -- carried out by individuals or groups on individuals or
groups. We initially focused on social exchange (for review, see Cosmides &
We selected this topic for several reasons:
Many aspects of the evolutionary theory of social exchange (sometimes called
cooperation, reciprocal altruism, or reciprocation) are relatively well
developed and unambiguous. Consequently, certain features of the functional
logic of social exchange could be confidently relied on in constructing
hypotheses about the structure of the information-processing procedures that
this activity requires.
Complex adaptations are constructed in response to evolutionarily long
enduring problems. Situations involving social exchange have constituted a
long-enduring selection pressure on the hominid line: evidence from
primatology and paleoanthropology suggests that our ancestors have engaged in
social exchange for at least several million years.
Social exchange appears to be an ancient, pervasive and central part of human
social life. The universality of a behavioral phenotype is not a sufficient
condition for claiming that it was produced by a cognitive adaptation, but it
is suggestive. As a behavioral phenotype, social exchange is as ubiquitous as
the human heartbeat. The heartbeat is universal because the organ that
generates it is everywhere the same. This is a parsimonious explanation for
the unversality of social exchange as well: the cognitive phenotype of the
organ that generates it is everywhere the same. Like the heart, its
development does not seem to require environmental conditions (social or
otherwise) that are idiosyncratic or culturally contingent.
Theories about reasoning and rationality have played a central role in both
cognitive science and the social sciences. Research in this area can, as a
result, serve as a powerful test of the central assumption of the Standard
Social Science Model: that the evolved architecture of the mind consists
solely or predominantly of a small number of content-independent, general
The evolutionary analysis of social exchange parallels the economist's concept
of trade. Sometimes known as "reciprocal altruism", social exchange is an "I'll
scratch your back if you scratch mine" principle. Economists and evolutionary
biologists had already explored constraints on the emergence or evolution of
social exchange using game theory, modeling it as a repeated Prisoners' Dilemma.
One important conclusion was that social exchange cannot evolve in a species or
be stably sustained in a social group unless the cognitive machinery of the
participants allows a potential cooperator to detect individuals who cheat, so
that they can be excluded from future interactions in which they would exploit
cooperators (e.g., Axelrod, 1984; Axelrod & Hamilton, 1981; Boyd, 1988; Trivers,
1971; Williams, 1966). In this context, a cheater is an individual who accepts a
benefit without satisfying the requirements that provision of that benefit was
made contingent upon.
Such analyses provided a principled basis for generating detailed hypotheses
about reasoning procedures that, because of their domain-specialized structure,
would be well-designed for detecting social conditionals, interpreting their
meaning, and successfully solving the inference problems they pose. In the case
of social exchange, for example, they led us to hypothesize that the evolved
architecture of the human mind would include inference procedures that are
specialized for detecting cheaters.
To test this hypothesis, we used an experimental paradigm called the Wason
selection task (Wason, 1966; Wason & Johnson-Laird, 1972). For about 20 years,
psychologists had been using this paradigm (which was originally developed as a
test of logical reasoning) to probe the structure of human reasoning mechanisms.
In this task, the subject is asked to look for violations of a conditional rule
of the form If P then Q. Consider the Wason selection task presented in Figure
Part of your new job for the City of Cambridge is to study the demographics of
transportation. You read a previously done report on the habits of Cambridge
residents that says: "If a person goes into Boston, then that person takes the
The cards below have information about four Cambridge residents. Each card
represents one person. One side of a card tells where a person went, and the
other side of the card tells how that person got there. Indicate only those
card(s) you definitely need to turn over to see if any of these people violate
From a logical point of view, the rule has been violated whenever someone goes
to Boston without taking the subway. Hence the logically correct answer is to
turn over the Boston card (to see if this person took the subway) and the cab
card (to see if the person taking the cab went to Boston). More generally, for a
rule of the form If P then Q, one should turn over the cards that represent the
values P and not-Q (to see why, consult Figure 2).
If the human mind develops reasoning procedures specialized for detecting
logical violations of conditional rules, this would be intuitively obvious. But
it is not. In general, fewer than 25% of subjects spontaneously make this
response. Moreover, even formal training in logical reasoning does little to
boost performance on descriptive rules of this kind (e.g., Cheng, Holyoak,
Nisbett & Oliver, 1986; Wason & Johnson-Laird, 1972). Indeed, a large literature
exists that shows that people are not very good at detecting logical violations
of if-then rules in Wason selection tasks, even when these rules deal with
familiar content drawn from everyday life (e.g., Manktelow & Evans, 1979; Wason,
The Wason selection task provided an ideal tool for testing hypotheses about
reasoning specializations designed to operate on social conditionals, such as
social exchanges, threats, permissions, obligations, and so on, because (1) it
tests reasoning about conditional rules, (2) the task structure remains constant
while the content of the rule is changed, (3) content effects are easily
elicited, and (4) there was already a body of existing experimental results
against which performance on new content domains could be compared.
For example, to show that people who ordinarily cannot detect violations of
conditional rules can do so when that violation represents cheating on a social
contract would constitute initial support for the view that people have
cognitive adaptations specialized for detecting cheaters in situations of social
exchange. To find that violations of conditional rules are spontaneously
detected when they represent bluffing on a threat would, for similar reasons,
support the view that people have reasoning procedures specialized for analyzing
threats. Our general research plan has been to use subjects' inability to
spontaneously detect violations of conditionals expressing a wide variety of
contents as a comparative baseline against which to detect the presence of
performance-boosting reasoning specializations. By seeing what content
manipulations switch on or off high performance, the boundaries of the domains
within which reasoning specializations successfully operate can be mapped.
The results of these investigations were striking. People who ordinarily cannot
detect violations of if-then rules can do so easily and accurately when that
violation represents cheating in a situation of social exchange (Cosmides, 1985,
1989; Cosmides & Tooby, 1989; 1992). This is a situation in which one is
entitled to a benefit only if one has fulfilled a requirement (e.g., "If you are
to eat those cookies, then you must first fix your bed"; "If a man eats cassava
root, then he must have a tattoo on his chest"; or, more generally, "If you take
benefit B, then you must satisfy requirement R"). Cheating is accepting the
benefit specified without satisfying the condition that provision of that
benefit was made contingent upon (e.g., eating the cookies without having first
fixed your bed).
When asked to look for violations of social contracts of this kind, the
adaptively correct answer is immediately obvious to almost all subjects, who
commonly experience a "pop out" effect. No formal training is needed. Whenever
the content of a problem asks subjects to look for cheaters in a social exchange
-- even when the situation described is culturally unfamiliar and even bizarre
- subjects experience the problem as simple to solve, and their performance
jumps dramatically. In general, 65-80% of subjects get it right, the highest
performance ever found for a task of this kind. They choose the "benefit
accepted" card (e.g., "ate cassava root") and the "cost not paid" card (e.g.,
"no tattoo"), for any social conditional that can be interpreted as a social
contract, and in which looking for violations can be interpreted as looking for
From a domain-general, formal view, investigating men eating cassava root and
men without tattoos is logically equivalent to investigating people going to
Boston and people taking cabs. But everywhere it has been tested (adults in the
US, UK, Germany, Italy, France, Hong-Kong; schoolchildren in Ecuador, Shiwiar
hunter-horticulturalists in the Ecuadorian Amazon), people do not treat social
exchange problems as equivalent to other kinds of reasoning problems. Their
minds distinguish social exchange contents, and reason as if they were
translating these situations into representational primitives such as "benefit",
"cost", "obligation", "entitlement", "intentional", and "agent." Indeed, the
relevant inference procedures are not activated unless the subject has
represented the situation as one in which one is entitled to a benefit only if
one has satisfied a requirement.
Moreover, the procedures activated by social contract rules do not behave as if
they were designed to detect logical violations per se; instead, they prompt
choices that track what would be useful for detecting cheaters, whether or not
this happens to correspond to the logically correct selections. For example, by
switching the order of requirement and benefit within the if-then structure of
the rule, one can elicit responses that are functionally correct from the point
of view of cheater detection, but logically incorrect (see Figure 4). Subjects
choose the benefit accepted card and the cost not paid card -- the adaptively
correct response if one is looking for cheaters -- no matter what logical
category these cards correspond to.
To show that an aspect of the phenotype is an adaptation, one needs to
demonstrate a fit between form and function: one needs design evidence. There
are now a number of experiments comparing performance on Wason selection tasks
in which the conditional rule either did or did not express a social contract.
These experiments have provided evidence for a series of domain-specific effects
predicted by our analysis of the adaptive problems that arise in social
exchange. Social contracts activate content-dependent rules of inference that
appear to be complexly specialized for processing information about this domain.
Indeed, they include subroutines that are specialized for solving a particular
problem within that domain: cheater detection. The programs involved do not
operate so as to detect potential altruists (individuals who pay costs but do
not take benefits), nor are they activated in social contract situations in
which errors would correspond to innocent mistakes rather than intentional
cheating. Nor are they designed to solve problems drawn from domains other than
social exchange; for example, they will not allow one to detect bluffs and
double crosses in situations of threat, nor will they allow one to detect when a
safety rule has been violated. The pattern of results elicited by social
exchange content is so distinctive that we believe reasoning in this domain is
governed by computational units that are domain specific and functionally
distinct: what we have called social contract algorithms (Cosmides, 1985, 1989;
Cosmides & Tooby, 1992).
There is, in other words, design evidence. The programs that cause reasoning in
this domain have many coordinated features that are complexly specialized in
precisely the ways one would expect if they had been designed by a computer
engineer to make inferences about social exchange reliably and efficiently:
configurations that are unlikely to have arisen by chance alone. Some of these
design features are listed in Table 1, as well as a number of by-product
hypotheses that have been empirically eliminated. (For review, see Cosmides &
Tooby, 1992; also Cosmides, 1985, 1989; Cosmides & Tooby, 1989; Fiddick,
Cosmides, & Tooby, 1995; Gigerenzer & Hug, 1992; Maljkovic, 1987; Platt &
It may seem strange to study reasoning about a topic as emotionally charged as
cheating -- after all, many people (starting with Plato) talk about emotions as
if they were goo that clogs the gearwheels of reasoning EPs can address such
topics, however, because most of them see no split between "emotion" and
"cognition". There are probably many ways of conceptualizing emotions from an
adaptationist point of view, many of which would lead to interesting competing
hypotheses. One that we find useful is as follows: an emotion is a mode of
operation of the entire cognitive system, caused by programs that structure
interactions among different mechanisms so that they function particularly
harmoniously when confronting cross-generationally recurrent situations -
especially ones in which adaptive errors are so costly that you have to respond
appropriately the first time you encounter them (see Tooby & Cosmides, 1990a).
Their focus on adaptive problems that arose in our evolutionary past has led EPs
to apply the concepts and methods of the cognitive sciences to many
nontraditional topics: the cognitive processes that govern cooperation, sexual
attraction, jealousy, parental love, the food aversions and timing of pregnancy
sickness, the aesthetic preferences that govern our appreciation of the natural
environment, coalitional aggression, incest avoidance, disgust, foraging, and so
on (for review, see Barkow, Cosmides, & Tooby, 1992). By illuminating the
programs that give rise to our natural competences, this research cuts straight
to the heart of human nature.
We would like to thank Martin Daly, Irv DeVore, Steve Pinker, Roger Shepard, Don
Symons, and Margo Wilson for many fruitful discussions of these issues, and
William Allman for suggesting the phrase, "Our modern skulls house a stone age
mind", which is a very apt summary of our position. We are grateful to the James
S. McDonnell Foundation and NSF Grant BNS9157-499 to John Tooby, for their
financial support during the preparation of this chapter.
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Copyright John Tooby and Leda Cosmides, 1997 Updated January 13, 1997