Luke Maurits

I recently finished reading Robin Dunbar’s “Grooming, Gossip and the Evolution of Language”, a fairly short and quite readable book which advances the hypothesis that the human language faculty evolved initially for the purpose of “social grooming”, i.e. strengthening social bonds between individuals in larger social groups, and then later was co-opted for the purpose of “gossiping”, i.e. talking to each other about each other.  More generally the book is concerned with the connection between cognition and social behaviour (and contains discussion of the well known “Dunbar’s number“).

The ideas in the book are well argued and I find myself without any good reason to reject most of them, although the possibility exists that there are better argued alternatives I’m not aware of.  My biggest complaint is that Dunbar’s rhetoric seems to blur the distinction between what language fundamentally does and how we preferentially use language.  I don’t have exact quotes, but there are several places in the book where Dunbar says things to the effect of “language evolved not for the purpose of exchanging information about the world around us, as we so often assume, but for exchanging information about each other for social purposes”.  This phrasing baffles me somewhat, because other human beings are part of the world around us.  Language is, by Dunbar’s hypothesis, for exchanging information about the world around us, it just happens that kind of information about the world around us which we preferentially exchange – and possibly the only kind that our ancestors exchanged – is each other.

This might seem like an odd or insignificant complaint to make, but my motivation for reading Dunbar’s book (and for reading more in general about the evolutionary origins of language) was considering the applicability of Andersonian rational analysis to language.  My recent research has centred around the Uniform Information Density (UID) hypothesis, which is theoretically motivated by the idea that human language is a roughly optimal solution to the problem of high-speed, high-reliability exchange of information (rational analysis in general holds that human cognitive faculties represent roughly optimal solutions to specific problems).  I have heard people question the applicability of this view to language, and in particular suggest that language is not essentially “for” information exchange but rather for various social interactions.  It seems implicit in this line of argument, and in much of Dunbar’s rhetoric, that these are orthogonal or mutually exclusive goals, but after reading this book I think that in fact one is simply a special case of the other, which is good news for rational theories of language.

This reassurance aside, I think the other thing I got most of reading Dunbar’s book was an appreciation for the continuity between the behavioural (and so presumably cognitive) traits of primates, including humans.  Cognitive science seems to be a fairly anthropocentric field – for obvious reasons – but it seems to me like it would often make a lot of sense to take a more inclusive view.  Perhaps attempts at modelling individual differences should also try as best they can to model inter-species differences at cognitive tasks which non-human primates are unanimously considered capable of?

The idea of applying an evolutionary perspective to the study of psychology has been hugely controversial, and I’ve never fully understood why.  Dualistic theory of mind has been effectively dead for a very long time.  Just about everybody who thinks about these things today is of the opinion that the structure and function of the mind is a reflection of the structure and function of the brain.  The brain is made of cells, and so when it comes to scientific explanations of its structure and function, evolution is the only game in town.  Any complete account of the human mind and how it came to be must necessarily include an evolutionary component.

These are all of the objections to integrating evolutionary thinking into psychology that I have either seen or can anticipate, and why none of them hold water:

• “Evolutionary psychology is bad because it will (or might) end up providing a scientific justification for racism, sexism, or some other unpleasant -ism”.  This is a straight up example of the well known is-ought fallacy.  It’s absolutely not a valid argument against EP or anything else.
• “Evolutionary psychology is a junk science full of speculative “just so” stories without any hard evidence”.  This may well be a valid criticism against some, or even many, of the particular ideas which have been developed under the guise of evolutionary psychology, but it’s not a good reason to discard the entire field.  The appropriate response to bad science which combines evolution and psychology is good science which combines evolution and psychology, not a declaration that evolution and psychology shall never meet.   Such a declaration can only be justified by an argument that good evolutionary psychology is impossible in principle.  As far as I know, no convincing argument of this kind has been made, and it seems unlikely to me that one ever will.
• “But, but, but, what about culture?”.  Bringing evolutionary thinking into psychology does not eliminate the possibility of cultural explanations for some phenomena, and in turn the explanatory power of culture does not completely eliminate the need for evolutionary thinking.  Culture isn’t magic: any given cultural influence on thought necessarily has to (i) be an influences on something, and that something has to have existed prior to the particular cultural influence under consideration, and (ii) have bene acquired by some mechanism, which again has to have existed prior to this particular cultural influence.  Evolution is the only thing which can terminate the infinite regress which results from trying to use culture as an explanation for everything.  Biological evolution and cultural evolution are complementary processes, and not at odds with one another.
• “Not everything in an organism’s phenotype represents an adaptation to some function.  The evolutionary process can also lead to traits which are exaptations, or spandrels”.  This is a prefectly valid claim in and of itself (although it’s not necessarily true – I haven’t done enough reading on the “Darwin Wars” to definitively come down on the side of Dawkins or Gould, although I’d love to have done so and fully intend to do so), but it doesn’t really remove the role for evolution in psychology.  It discredits one particular conception of evolutionary psychology, i.e. the conception in which everything is an adaptation.  But all this does is force us to widen our conception of what evolutionary psychology is, from “explaining as much of psychology as possible in terms of adaptations” to “explaining as much of psychology as possible in terms of adaptations, exaptations or spandrels”.
• “The mind is emergent magic!  Chaos, fractals, self-organisation!”.  My characterisation of this school of thought is tongue-in-cheek, but it does represent a perfectly legitimate take on the human mind.  However, once again, it’s not a magic ticket away from evolution.  The full complexity of the human mind may well emerge in some seemingly magical way from the simple, local interactions of individual neurons in the human brain, but that same complexity does not emerge from extremely similar simple, local interactions of individual neurons in the brains of cats and dogs.  Clearly there is some set of preconditions for the emergence of various psychological traits, and if we establish what those preconditions are we will be faced with the task of explaining how evolution drove our brains to meet those preconditions from a previous state which did not.  Evolution cannot have proceeded with foresight toward those preconditions because of the benefits that would emerge from them: each step along the way must have yielded its own benefits, and the story isn’t complete until we know what those steps were and why they happened.

No matter what your particular beliefs are about how the mind or evolution works (well, assuming they’re not dualist in the case of the mind), there’s just no way you can completely separate the two.  “Evolutionary psychology” isn’t some distinct subfield which you can ignore or choose not believe in.  All of psychology is under the influence of evolution, and it is a totally legitimate goal of the science to eventually account for all of these influences.  It’s conceivable that we can completely ignore this part of psychology until “the end”, characterising the functionality of all the various psychological traits without any recourse to evolutionary thinking at all and then tying it all together with an evolutionary story at the end.  But why should we do that?  To the extent that we can without sacrificing scientific rigour, we should try to uncover the evolutionary story as we go.  Not only is it damned interesting, but it can be legitimately used to constrain hypotheses in areas of the science where we haven’t yet made a lot of progress, making it a useful tool.  Besides, it’s just as conceivable that we can’t completely ignore this influence.  If nothing else, I think a basic grounding in the latest facts and hypotheses surrounding the evolutionary emergence of human intelligence should be considered an essential part of a thorough education in psychology.

Another short thought on the nature of the modular mind. When I first started doing a lot of reading on psycholinguistics at the start of my PhD, I made a few brief notes of interesting facts and observations that I thought I might like to be able to easily reference later on. One of those notes is this:

No more than 20,000 – 25,000 genes have to account for the entire human body and brain – the vast majority of these genes are shared with other apes and even other mammals. There are apparently only a few kilobytes of new genetic information in our genome since our last common ancestor with chimps.

This note is annotated as being sourced from Sverker Johansson’s 2005 “Origins of Language: Constraints on hypotheses”, which I recall being excellent. I don’t remember if the note above is a direct quote from Johansson or if I condensed a paragraph down into a few sentences.

Anyway, this is interesting from the point of view of thinking about the extent to which our mental modules rely on a shared toolbox. Considering that the differences in cognitive capacity between humans and chimps are significant, the fact that this difference represents only a few kilobytes of information suggests (although I am admittedly unsure as to how much our intuition about the difference a few KB of DNA can make to an organism’s mind should be allowed to be guided by our intuition as to the difference a few KB of source code can make to a computer program) that our higher cognitive abilities are coded for very efficiently, which argues more in favour of the common toolbox approach than the specialised tools.

In fact, moving into highly speculative territory that I’m not really qualified to talk about, maybe the difference between chimps and humans today corresponds directly to taking these two paths away from our common ancestor: humans spent their few “bonus kilobytes” investing in a rich toolbox of reusable, domain-general processing methods, whereas the other primates coming from the same starting point spent theirs on expanding and refining their kit of specialist, “one-trick pony” algorithms. My concern in my previous entry that general purpose methods were harder to evolve (that q was significantly lower than p) then becomes in fact an explanation as to why humans alone amongst the primates have progressed to the extent we have. Maybe we are the one roll of the evolutionary dice where the unlikely outcome with probability qⁿ actually happened, and our fellow primates all followed the more likely path of getting stuck in local optima. I don’t know enough about the learning abilities displayed by other primates in fields other than language (where, incidentally, they typically perform much more poorly than laypeople and sensationalist media outlets give them credit for) to have a good idea of how plausible this hypothesis is, but it seems plausible enough on the surface. I wish I knew more people knowledgeable about this sort of thing.

I recently started reading Steven Pinker’s “How the Mind Works“. Pinker is a psychologist whose interests broadly overlap with mine, and he writes a lot of popular science books on these issues. I like him, even though our opinions on language acquisition are quite different, because while his books are occasionally fairly biased (a particular problem for his “The Language Instinct“), they also maintain a good sense of intellectual rigour whilst still being fun to read. It’s encouraging to be reminded from time to time that there are smart people thinking about these things sensibly.

I’m only a little way into the book as of yet but already it’s been quite rewarding because it has dispelled a misconception I had about evolutionary psychology, a school of thought of which Pinker is a strong advocate. The stance of this school is essentially summarised by this excerpt:

The mind is a system of organs of computation, designed by natural selection to solve the kinds of problems our ancestors faced in their foraging way of life…The mind is organized into modules or mental organs, each with a specialized design that makes it an expert in one arena of interaction with the world. The modules’ basic logic is specified by our genetic program. Their operation was shaped by natural selection to solve the problems of the hunting and gathering life led by our ancestors in most of our evolutionary history.

I have never taken issue with the essential issues of this school of thought. I have embraced the computational theory of mind for as long as I can remember, and I have no doubt that the structure of the brain – and hence the mind – has been shaped by evolutionary pressures that acted in the distant past. It’s the “module” thing that has always kind of bugged me. The reason for this is that I have always interpreted the modular view of the mind espoused by evolutionary psychology as implying a mind made up of separate and autonomous parts bolted together. This is apparently not uncommon, as Pinker goes on to say:

The word “module” brings to mind detachable, snap-in components, and that is misleading…mental modules need not be tightly sealed off from one another, communicating only through a few narrow pipelines.

It’s a relief to hear that evolutionary psychology does not consider this position to be required. Of course, it’s one thing to not claim that the modules of the mind are necessarily distinct, and another thing to actually make a claim about the extent to which they are. This question really interests me. Do the modules of the mind look like this:

i.e. a collection of highly domain-specific modules with minimal overlap, most of the work being done by specialised faculties with little sharing of data or tools between modules? Or do they look like this:

i.e. a collection of highly overlapping modules with minimal domain-specific components, most of the work being done by a large, shared toolbox of general purpose algorithms?

As an aside, these charts were produced using Google’s free Charts API, a pretty nifty tool.

It’s easy to frame this question in terms of object oriented programming, too. If each module of the mind is a class and each class is a subclass a common BaseMindModule class, then is the interface of BaseMindModule just a few simple attributes and methods dealing with common stuff like I/O, with each subclass adding a lot of domain specific behaviour, or is BaseMindModule a large class with a rich API of general purpose methods, with each subclass being a thin wrapper around this API?

There seem to be two questions to consider here: firstly, just how large can the centre of the Venn diagram be, i.e. how much of human cognition can, in principle be explained by general purpose tools; secondly, even if a large common toolbox is possible in principle, is evolution likely to favour it over a a dispirit pile of specialist tools?

On the first question, I’m actually fairly confident that domain general tools can get a tremendous amount of work done. A background in mathematics makes this seem almost obvious. Mathematics is full of “tools” which are defined at such an abstract level that they can be applied to just about anything, while still being sufficiently meaningful that are practical. The student who has learned basic differential and integral calculus, for example, can construct simple models of phenomena from domains as diverse as biology, chemistry, economics, epidemiology, physics, sociology and more. Markov chains are an example of a tool with a rather different flavour that still manages to be very broadly applicable – Markov chains can play a role in models of things from all of the above fields as well. And, of course, statistics is taught to a wide range of students as little but a tool box of techniques and tests that work on any kind of data whatsoever – linear regression, hypothesis tests and confidence intervals are probably the closest thing that exists in academia to a universally common component of education across departments and disciplines, from engineering to psychology. If the mind contained circuits that processed data by approximating the logic of coupled linear ordinary differential equations, or of building hidden Markov chain models, then couldn’t those circuits be the workhorses of a whole host of mental modules for a wide range of problems? I can’t think of a compelling reason why not.

The second question feels less straightforward. The large, common toolbox approach offers a certain economy of design that on the one hand should be preferable to reinventing essentially identical wheels again and again for each problem that is encountered. But simultaneously, it feels like general tools are in some sense harder things to come up with. Inventing m general tools instead of n specific ones is of course less work overall if m is significantly less than n, but as so many people fail to grasp, evolution is an emphatically blind watchmaker which cannot look ahead like this and is thus extremely susceptible to getting stuck in local optima. Sticking with our counts of m and n, if the probability of a sequence of mutations leading to a domain specific tool is p and the probability of it leading to a domain general tool is q, the question becomes one of which is greater: p^m or qⁿ? As the ratio of n to m tends to infinity, chance favours a general toolbox, but as the ratio of q to p tends to infinity, chance favours a collection of specialist tools. We can’t answer the question without sensible estimates for these ratios, but how could we even begin to make such estimates? It’s not a trivial task.