TC paper - Cognitive Adaptations for Social Exchange

Cognitive Adaptations for Social Exchange

Tooby and Cosmides suggest that there are a number of adaptations specifically for situations of social exchange, and provide evidence for one specific component, cheater detection, from Wason selection experiments. They also discuss the relevance of social exchange adaptations for culture.

Cultural Evolution Evolution Psychology

TC paper - The psychological foundations of culture

The psychological foundations of culture

TC make the case that a full and accurate science of culture requires a recognition of the important role played by the information-processing mechanisms of the mind.

They note that historically in the social sciences, the perspective has been taken that the human mind starts off as something of a blank slate, with content being written into it by external culture. They attribute this perspective in part to the (erroneous) deduction that since infants are born apparently knowing nothing and yet grow into contentful adults, therefore all content must have come in from outside sources.

They note that in contrast, the evolved mind is full of content-rich, adaptive information-processing systems, and that the growth of humans in knowledge and abilities from infancy is the result of a complex interplay between the information available in the environment and the mechanisms of the mind. To take just one example, although we clearly get the words of our local language from our environment, we contain evolved language acquisition mechanisms that enable us to do this. They note that the explanations often given in the social sciences explaining human behaviours and faculties, such as "learning", "culture", "rationality" and "intelligence" are not really explanations at all, but rather substitutes for the required elaborations of how the mechanisms of the mind actually work.

In a wide-ranging, fascinating paper, they suggest among other things that culture, rather than being viewed as a unitary phenomenon, can be usefully subdivided into a number of different categories:

  1. "Metaculture" - the elements of our culture attributable to human universals, for example parental care, sexual attraction, the consumption of food, play, participation in coalitions and so on
  2. "Evoked culture" - alternative, domain-specific mechanisms, triggered by local circumstances, for example different food-sharing dynamics depending on the characteristics of locally-available food sources.
  3. "Epidemiological culture" - what some others may term "transmitted culture". This is the closest match to what is currently recognised in the social sciences as culture: "Observer's inferential mechanisms construct representations similar to those present in others." However, note that the focus is on the fact that it is the observer's mechanisms that are determining what internal representations they form, in contrast to the standard social science view that culture is in some way writing itself into people. An important corollary is that the "domain-specific mechanisms influence which representations spread through a population easily and which do not." People are not simply passive absorbers of an independent "culture".

They go into considerable depth on why we should expect many or most of the mechanisms of the mind to be "domain-specific", rather than "domain-general" (including the evolutionary necessity of minimizing 'combinatorial explosion' and the 'frame problem'), and they outline methodologies for discovering and describing the domain-specific mechanisms of the mind.

A long, but essential read, almost a mini-book.

Cultural Evolution Evolution Psychology

TC paper - Evolutionary psychology and conceptual integration

Evolutionary psychology and conceptual integration

T&C suggest the desirability of 'conceptual integration' - "the principle that the various disciplines within the behavioral and social sciences should make themselves mutually consistent, and consistent with what is known in the natural sciences as well". They observe that while such integration has flourished between physics, chemistry and biology, for example, there has been little integration between the natural sciences on the one hand and the social sciences on the other.

They suggest that the science of evolutionary psychology can provide a bridge. The information-processing mechanisms of the mind are created by biology, and play a large role in our social behaviour and culture. Evolutionary psychology makes explicit the part of the causal stream as a whole that connects the natural sciences to the social sciences.

Computing Evolution Psychology

Why our genes do not doom us

In the world at large (and even, it would appear, sometimes within the biological sciences) there can be a certain amount of trepidation surrounding the idea of a 'genetic tendency'. For example, if we posit a 'genetic tendency' towards aggression in human males, it seems as if we are saying something terrible. It seems even worse to posit a gene or genes 'for' aggression, even though, as Dawkins explains in The Extended Phenotype, this is exactly the same thing. A genetic basis for, a gene for, a genetic tendency towards, genetically influenced - however we phrase it, we are saying the same thing - that genes in some way contribute in the causal path.

The bogeyman of genetic tendency is at root a fear that if we have a 'gene for' X, that means that we will ineluctably do X. It can also go under the name of 'genetic determinism'. It is the idea that genes in some way control us, despite what we would like, and we (or other people) are doomed to carry out certain actions contrary to our best interests or contrary to morality or contrary to some other important social goal depending on what 'X' is.

The fear has a certain superficial credibility. After all, we all have 'genes for' hands, and we all (or almost all) end up with hands. Similarly for feet, hearts, livers, eyes, stomachs and so on. If men have a 'gene for' aggression, then surely all men end up violent?

This tempting line of reasoning contains a critical flaw. While genes for hands build hands, genes for aggression do not build violence, whatever that could mean, rather they build an aggression mechanism. Any time we have genes 'for' a behaviour, that necessarily means that the genes build a mechanism for producing that behaviour. Possessing a mechanism for a behaviour does not tell us much about how much if at all we should expect to see that behaviour. Let's consider why.

Let's suppose that we have a subroutine for punching people. I'm going to go ahead and write some pseudo-code:

function punch(thePersonToPunch){

Wow! How deterministic! If you call function 'punch' with an input of who to punch, it just punches them!

But while a mechanism for aggression must call something like the 'punch' function, that can't be the whole of the aggression mechanism. There's nothing in this code to say when it runs. It surely can't run constantly, or the poor human would just spend all its time and energy punching people and we know that doesn't happen. So the mechanism might look a bit more like this:

function aggressionMechanism(context){
if (context.containsThreat()){

This makes a bit more sense. We can run the aggressionMechanism constantly, and it will only output a punch if it detects that its environment contains a threat. We can say that the aggression mechanism outputs violence conditionally, in other words only under certain conditions.

Of course we can make the mechanism a bit more sensitive:

function aggressionMechanism(context){
    threatLevel = context.getThreatLevel();
    if (threatLevel < 10){
    else if (threatLevel < 20){
    else if (threatLevel < 30){
    else {

Now our mechanism is somewhat sensitive to the level of the threat. If it's only a mild threat, it ignores it. Slightly bigger threats gain a hard stare or a puffing of the chest. Finally, it is only very large threats that get a punch.

Hopefully we're already starting to feel somewhat safer. An 'aggression mechanism' does not need to mean constant mayhem and indeed no gene that generated constant mayhem would be likely to make it into the next generation.

But a 'gene for' a mechanism does not necessarily mean that the code of the mechanism is completely determined by the genes. We can imagine an aggression mechanism that is sensitive to the way that other people have historically treated the person who possesses the mechanism. Let's say that after a year of being treated nicely, the owner of the above mechanism now has a mechanism that looks like:

function aggressionMechanism(context){
    threatLevel = context.getThreatLevel();
    if (threatLevel < 20){ // Was 10 before
    else if (threatLevel < 40){ // Was 20 before
    else if (threatLevel < 60){ // Was 30 before
    else {

For this hypothetical example, the structure of the mechanism has stayed the same, but the thresholds have changed. It takes a lot more for this individual to get riled up now. There's no reason a neural mechanism should not change like this. Just because something is a 'mechanism' does not mean it is completely unchangeable. Much encoding of mechanisms in the brain is done with synapse strengths and those are highly flexible.

So a 'genetically determined' aggression mechanism can nevertheless be highly conditional, firing rarely if at all, and it can be highly sensitive to environmental input, meaning that it can be configured by external input.

But we can go further. When you're considering a complex system (like a human brain), it's always a mistake to consider one component in isolation. It's important to consider the interactions between all components if we're going to understand the behaviour of the whole.

Let's make a crude model of human behavioural output. At any given moment we can decide to do any of a great number of behaviours - singing, dancing, talking, walking, running, jumping and so on. We can imagine some kind of central authority that decides which of the many possible available actions to execute. We could suppose something like:

function centralExecutive(){
    options = new List;
    /* And our aggression mechanism */

    actionToExecute = getHighestScoring(options);

In this toy central executive, lots of different mechanisms are polled, each one 'recommending' an action. I imagine you've experienced something similar. In the mid-afternoon, you might ask yourself, "What do I feel like doing this evening?", and you might be aware of several different options, that we can suppose come from different parts of your brain. One part says "I quite fancy some Indian food." Another part says "I should really go to the gym". Another part says "I'm tired, I just want to go home." Maybe another part says, "I'd like to meet up with some friends."

We can suppose that we have something roughly like the central executive function running all day every day. At every moment, our brain is trying to figure out what is the best thing to be doing right now, and then executing its choice.

We can also suppose that the different parts of the brain must make their recommendations in some kind of common currency, otherwise how is the executive to choose between them? We experience this subjectively as how much we 'feel like' or 'want to' do something. Generally the thing we 'want to do' the most wins. But these feelings of wanting must somehow be in the same currency or we could not compare them.

So let's suppose that there is a threat in the environment. It's a threat at level 70, so even our most recent, milder aggression mechanism is triggered. But to incorporate this new element of the central executive, we need to change our aggression mechanism slightly.

function aggressionMechanism(context){
    threatLevel = context.getThreatLevel();
    if (threatLevel < 20){
    else if (threatLevel < 40){
    else if (threatLevel < 60){
    else {

The difference to the previous mechanism is that now, the mechanism only recommends courses of action to the central executive, rather than taking those actions itself.

So even though the threat level is now at 70, the aggression mechanism just says to the central mechanism "Hey, I'm strongly recommending that we punch this threat."

But crucially, the central executive has a chance to evaluate this course of action before executing it. Perhaps it runs it by the 'future simulator' function, asking it "Hey future simulator, what would happen if I punch the threat?" The future simulator might calculate the likely results and say, "Well, I think you would damage the threat but it's likely you'd end up in jail and that would be very bad."

So the central executive weighs up the pros and cons and decides against the violence, despite the aggression mechanism strongly recommending it.

Hopefully now we're feeling a lot safer.

The fact is that we can have a 'gene for' a behaviour, and that gene can 100% reliably (or close to it) build a mechanism for producing that behaviour, just as genes build hands, and yet that behaviour only be produced under very rare or specific circumstances if at all.

The existence of a mechanism does not mean it's going to produce the product it was designed to produce. I've lived in my flat for 5 years and I've never once used the central heating mechanism.

Of course we know that people aren't aggressive all the time or even most of the time, and we know that it only happens under tightly circumscribed circumstances. The point of this article is to illustrate how this is perfectly compatible with a genetic tendency towards or even genes for (it's the same thing!) aggression. The same goes for genes for any other behaviour we may care to consider.

Cultural Evolution

Leda Cosmides talk - Modeling cultural evolution requires evolutionary psychology

Modeling cultural evolution requires evolutionary psychology

Talk given in Sept 2021 as part of the awarding to Leda Cosmides and John Tooby of the Jean Nicod Prize 2020. (Unfortunately the first 13 minutes are quite quiet due to a technical problem but they're still audible.)

The central point of the talk is that cultural evolution (a la Rob Boyd, Peter Richerson, Joe Henrich etc) should not be treated as entirely separate from evolutionary psychology. She gives several fascinating examples of evolved cognitive mechanisms (eg incest avoidance mechanisms, reciprocal altruism mechanisms, toxicity avoidance mechanisms etc) that have considerable innate content as well as considerable impact on our cultural behaviours, such that it would be unwise to treat cultural evolution as operating in isolation from the neural mechanisms that necessarily underpin it.

Evolution Psychology

TC paper - On the universality of human nature and the uniqueness of the individual: The role of genetics and adaptation.

On the universality of human nature and the uniqueness of the individual: The role of genetics and adaptation.

All Tooby and Cosmides publications

T&C make the case that there is likely to be a universal human nature, with "a species-typical collection of complex psychological adaptations," despite the considerable genetic variation in the human population.

Their reasoning is that any complex adaptation requires many genes, and genes get shuffled so thoroughly in a population that there is no way a complex adaptation could reliably appear, unless the different alleles produce only superficial differences between people, and the broad functions built by the genes are universal.

Computing Consciousness Evolution Psychology

Is the human brain a computer?

Sometimes people object when I describe the human brain as a computer. The most common objections are things like:

  • Computers are made by humans whereas brains are biological
  • Computers are made out of silicon and our brains are made of cells
  • Computers have addressable memory whereas our brains have neural networks
  • Computers don't have emotions whereas we do
  • Computers are mechanical whereas we have flexibility and free will

Some of the disagreement is simply arguing over definitions. If someone is using the word 'computer' to refer solely to devices made out of silicon designed by humans then of course they're not going to agree that human brains are computers. So let me short circuit some of the disagreement by making the following stipulative definition of 'computer':

A computer is a device that transforms input into useful output.

If we substitute in my definition, we can rephrase the question in the title of this post as "Is the human brain a device that transforms input into useful output?"

Using this definition, there doesn't seem to be much room for disagreement. The human brain takes input from the senses, and from internally stored information (for example memories), and transforms that input into useful behaviours.

If it really is fair to see the human brain as a computer, that suggests that we should be able to use much of the content of computer science to characterise and analyse the workings of the brain. We might expect to find some or all of the following concepts usefully applicable:

  • Variables
  • Subroutines
  • Data encoding
  • Memory storage and retrieval
  • Lookup tables
  • Ranking and sorting (eg in action prioritization subroutines)
  • Daemon processes (processes with their own largely separate cause and effect chains)
  • Concurrency
  • Testing
  • Bugs and debugging
  • Caching
  • Mechanisms optimised for speed or low resource usage or for accuracy
  • Subsystems

and many others.


Arguing over definitions

If you have spent even a modest amount of time on internet forums, you will have found people having arguments about questions like whether golf is a sport, whether bitcoin is a Ponzi scheme or whether a tomato is a vegetable.

What these arguments have in common is that the actual properties of golf, bitcoin or a tomato are not in dispute. Rather the issue is whether these objects are properly seen as belonging to a certain category, like sport, Ponzi schemes or vegetables.

The frustrating thing about these arguments is that the (usually unacknowledged) root cause of the disagreement is simply that the participants are using different definitions of the categories. One person has in mind a definition of 'sport' that includes the criterion that the activity must require significant exertion whereas the other doesn't. One person is using a definition of 'Ponzi scheme' that requires that a single person benefit and one isn't. And so on.

The key to resolving these arguments is to recognize that definitions of words are essentially arbitrary. Words are just labels that we come up with so we can talk with each other about things in the world. It is to be expected that there will be mild discrepancies between people about what precisely a given label covers.

If we want to have very clear communication, a useful device is a 'stipulative definition', where we say explicitly what we are using a given word to mean.

Computing Consciousness Evolution Psychology

List of Axioms

When discussing a topic as broad as human behaviour, it helps to make any philosophical/scientific assumptions explicit so that any reader can see if he or she has some fundamental difference of position to that of the author. I therefore give the following list of assumptions as axioms, which I take for granted elsewhere on the site (justifications/discussions are in the links [coming soon]):

  1. The universe is deterministic. If precisely the same starting conditions are set up twice, precisely the same result will occur each time. Every process can therefore be described as mechanical, including consciousness.
  2. Natural selection is the only known process in the universe capable of building complex adaptations.
  3. "The ultimate goal that the mind was designed to attain is maximizing the number of copies of the genes that created it." (Steven Pinker)
  4. It's legitimate to hypothesize about function in plain language or any computer language, even though functions are actually implemented in neurons, synapses etc in the brain. Functional hypotheses are at a layer of abstraction above implementation and can therefore be implementation-agnostic.
Computing Consciousness Evolution Psychology

Relevant Quotations

"We are survival machines — robot vehicles blindly programmed to preserve the selfish molecules known as genes." - Richard Dawkins - The Selfish Gene

"I am not apologizing for using the language of robotics. I would use it again without hesitation." - Richard Dawkins - The Extended Phenotype

"The ultimate goal that the mind was designed to attain is maximizing the number of copies of the genes that created it." - Steven Pinker - How The Mind Works

"Talk is cheap. Show me the code." - Linus Torvalds, creator of Linux operating system

"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 empirically 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 achieving 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." - Leda Cosmides & John Tooby - Evolutionary Psychology: A Primer

"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. Light will be thrown on the origin of man and his history." - Charles Darwin - On the Origin of Species

"The human mind consists of a set of evolved information-processing mechanisms … produced by natural selection over evolutionary time." John Tooby and Leda Cosmides in The Adapted Mind

"Information and computation reside in patterns of data and in relations of logic that are independent of the physical medium that carries them." Steven Pinker - How The Mind Works

"The brain’s special status comes from a special thing the brain does, which makes us see, think, feel, choose, and act. That special thing is information processing, or computation." Steven Pinker - How The Mind Works

"I have a friend who's an artist, and he sometimes takes a view which I don't agree with. He'll hold up a flower and say, "Look how beautiful it is," and I'll agree. But then he'll say, "I, as an artist, can see how beautiful a flower is. But you, as a scientist, take it all apart and it becomes dull." I think he's kind of nutty. ... There are all kinds of interesting questions that come from a knowledge of science, which only adds to the excitement and mystery and awe of a flower. It only adds. I don't understand how it subtracts." - Richard Feynman

"You are a computer, built by selection, and melted or disordered by entropy." John Tooby