Computational behavior theory and cultural evolution
The New York Times reports on newly discovered rock art in Borneo, dated to 40,000 years old and providing further evidence that figurative art was not born in Europe. The idea that a full-fledged capacity for complex culture evolved in Europe is a traditional one, based on the fact that, until some time ago, the earliest finds of complex artifacts (art, stone tools, and so on) were from Europe. However, the idea is problematic because it fails to explain why every extant human population has the same cultural capacity, as there is no record of gene flow from Europe to all the rest of the world successive to the appearance of European complex culture. A few years ago, we analyzed evidence of cultural capacity and we came to the conclusion that this capacity is probably as old as the human species. The new find in Borneo joins the ones we had examined in pointing in this direction. In fact, we argued that even Neanderthals may have had the same cultural capacity as Homo sapiens, in agreement with the discovery 40,000 year-old paintings that may have been made by Neanderthals (announced as our paper was being published).
A summary of our paper on the origin of human cultural capacity is in a previous post.
Note: This is a somewhat technical post
While writing my previous JMP paper, On elemental and configural models of associative learning, I was also working out how the equivalence between elemental and configural models could be exploited for better analytical methods. My rationale for this research was that, in most cases, associative learning models are studied either intuitively or with computer simulation, making it difficult to establish general claims rigorously. After some time and fantastic input from reviewers and the editor, I am happy that Studying associative learning without solving learning equations came finally out over the Summer. This paper shows that the predictions of many models can be calculated analytically simply by solving systems of linear equations, which is much easier than trying to solve the models’ learning equations. For example, we can calculate that, in a simple summation experiment (training an associative strength 
and in Pearce’s (1987) model:
where, in both cases, 
I hope the method presented in the paper will be used also by others to derive new theoretical predictions and design new theory driven experiments!
Well, it seems I have not written here since two years ago! It has been a busy and exciting period, largely occupied by a book project that is looking at cognitive differences between humans and other animals. One of the by-products of this project is the title paper, a meta-analysis effort in collaboration with Johan Lind. In this paper, we offer a critical look at recent claims that birds, and in particular corvids, can “understand” properties of the physical world such as “light objects float, heavy objects sink,” and are able to use such knowledge to solve new problems. The performance of these birds in some tasks has been compared to that of 5-7 year old children.
The best way to understand the puzzles presented to the crows is to watch this video, from Jelbert et al. (2014) :
From the video, the performance of New Caledonian crows appears impressive. The results of our meta-analysis, however, are not supportive of the original claims. In summary, it seems that crows learn the correct behavior by trial-and-error as they perform the task. In almost all tasks, the birds start choosing one of the two options at chance, and only gradually they switch to the more functional option. The video shows the final stage of learning, rather than the initial random behavior.
We also compared the crow data with data from children, and we found clear differences. While younger children do not do well on most tasks, children aged 6 and older perform much, much better than birds, despite having received much less training.
There are one or two examples of tasks in which birds do well from the very beginning, as well as some tasks in which birds do not learn at all. In our paper, we argue that both occurrences can be understood based on established knowledge of animal learning, and especially associative learning.
The full article has appeared in Animal Behaviour.
Diana Tamir and Jason Mitchell of the Social Cognitive and Affective Neuroscience Lab at Harvard have just published a paper showing that people find it rewarding to talk about themselves, especially if others are listening (summarized here). Although, put it that way, you may or may not find the result astonishing, it touches upon an important issue in our understanding of ourselves: the difference between proximate and ultimate causes. Konrad Lorenz explained this difference in the fewest words when he said: the ultimate cause of a car is to travel, the proximate cause is the engine. That is, the ultimate cause is the function, and the proximate is the mechanism that achieves it.
Tamir and Mitchell show that brain areas that respond to reward (food, sex, money, etc.) are also activated when answering questions about oneself, more than when answering questions about Barack Obama (chosen perhaps for his interesting opinions, perhaps because he is familiar to everyone) or about dry facts. And knowing that a friend or relative would read your answer activated the reward areas even more. This, they argue, is the proximate cause of our obsession with talking about ourselves: it activates the reward areas of our brain.
The authors have been careful in validating their results conducting not one, but four distinct experiments. I will just mention that the participants were sure to know the answer to questions about themselves, but not to the other questions. So the reward they felt could reflect the anticipation of knowing the answer rather than the self-referential aspect of the question (we know the same brain areas respond to anticipated reward). After all, we are rewarded all our lives for knowing the answer to questions. But this is not my main point.
My main point is about the ultimate reason why we feel rewarding to talk to others (especially if they listen). In genetic evolution the only ultimate cause is natural selection. Things happen because they make organisms survive and reproduce. It is not hard to imagine potential benefits of sharing your thoughts with others: exchanging knowledge, strengthening social bonds, and so on. But human behavior has another ultimate cause: cultural evolution. What drives cultural evolution is imperfectly understood, but one way to think about it is to ask what are the `magical ingredients’ that make ideas popular. One such ingredient is, rather obviously, that the idea should be able to spread. Other things being equal, ideas that spread faster, convincing person after person to adopt them, will become more popular than slow-spreading ideas. And what is the best way to spread ideas? To talk about them! If you like talking to others about your ideas, these will have a good chance of spreading, and among the ideas you spread there will be those that make you like talking to others. Simplifying a bit, if you think `talking to others is cool,’ then you will say, among other things, `talking to others is cool,’ and others may be convinced of it and start talking to others, furthering the spread of the `talking to others is cool’ idea. If this sounds like a tongue twister, it is because cultural evolution is full of self-referential loops in the dynamics of ideas (one example, and another).
Thus we may like to talk about ourselves because of the dynamics of ideas, rather than because this tendency has been built into us by genetic evolution. Can we distinguish between the two hypotheses? Not yet, I believe, and the main reason is that neither evolutionary psychology nor cultural evolutionary theory (I don’t even have a Wikipedia link for that, but you can look here) have formulated precise predictions about how and when ideas should or should not be shared. But adapting Tamir and Mitchell’s experimental setup to test such hypotheses should be easy. So come on, theoreticians, give us a hypothesis to test!
“Can pigeons read?” is the question asked at the beginning of this old video, aimed at illustrating techniques to teach animals complex discriminations by rewarding them for correct choices but not for incorrect ones.
These techniques, developed around 1930, have been used in a study teaching baboons to recognize English words from non-words. Soberly entitled “Orthographic processing in baboons,” the study has been often headlined “Baboons can read,” even by the very journal who published it. My colleague Johan Lind was delighted to hear the news: “If they can read, then I can write to them and ask about animal intelligence.” Unfortunately, the only thing the baboons would be able to tell Johan is which combinations of letters are more likely to appear in English words, which is what they learned by receiving food anytime they correctly identified four-letter sequences as an English word or a non-word.
The study actually demonstrates that you do not need to know language to tell words from non-words. All languages have a statistical signature, whereby some combinations of sounds (and, therefore, letters) are common, and others are rare. Baboons are smart enough, and see well enough, to learn this. I would not be surprised if pigeons could do it too, given that they can, for example, discriminate paintings by different artists, presumably learning something about the artists’ “visual grammar.” Pigeons can also associate different written words with different actions, as the video above shows. All this suggests that the evolutionary origin of our ability to read is even more ancient than “reading” baboons suggest, pigeons being separated from humans by some 150 million years of independent evolution. Analyzing the structure of visual stimuli is a natural task for many animals, and I do not think the key to understanding human uniqueness lies here.
We have prepared a flyer to advertise the Conference on Human Cognitive Uniqueness that will take place at Brooklyn College on May 29-30. Feel free to use it to advertise the Conference yourself!
As announced a few weeks ago, our paper “The Logic of Fashion Cycles” has been published, and is freely available on the PLoS ONE website. You can find a good summary at The National Post.
Plos ONE has accepted our paper “The logic of fashion cycles,” where Alberto Acerbi, Magnus Enquist and myself present a new theoretical model to understand fashion cycles (see my previous post on dog breeds). You can download a preprint, and here is the abstract:
Many cultural traits exhibit volatile dynamics, commonly dubbed fashions or fads. Here we show that realistic fashion-like dynamics emerge spontaneously if individuals can copy others’ preferences for cultural traits as well as traits themselves. We demonstrate this dynamics in simple mathematical models of the diffusion, and subsequent abandonment, of a single cultural trait which individuals may or may not prefer. We then simulate the coevolution between many cultural traits and the associated preferences, reproducing power-law frequency distributions of cultural traits (most traits are adopted by few individuals for a short time, and very few by many for a long time), as well as correlations between the rate of increase and the rate of decrease of traits (traits that increase rapidly in popularity are also abandoned quickly and vice-versa). We also establish that alternative theories, that fashions result from individuals signaling their social status, or from individuals randomly copying each other, do not satisfactorily reproduce these empirical observations.
I have recently attended a one-day course on data visualization with Edward Tufte and I have tried to put his advice on virtual paper in this supergraphic on the popularity of dog breeds, using AKC data (courtesy of Hal Herzog). The graph shows the popularity of 100 breeds over time (most popular breeds first), indicating the maximum in popularity and other peaks (if any). I have produced this graph as an inspiration for my ongoing work on cultural dynamics (some features are idiosyncratic to the data analyses I am making). Here are a few things I see in the graph:
What else can you see? And how to explain it? In am working, with Alberto Acerbi and Magnus Enquist on an explanation of fashion cycles based on the cultural dynamics of preferences, as foreshadowed in our previous work on how social learning influences openness to new information. The paper is now under review at Plos ONE.
drghirlanda 