Computational behavior theory and cultural evolution

Tag Archives: science

New paper: `Aesop’s fable’ experiments demonstrate trial-and-error learning in birds, but no causal understanding

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.



The age of human cultural capacity

Venus of SchelklingenWhen did humans evolve, to its full extent, the capacity to create complex culture? We consider this question in a paper appearing in the May 7th issue of Scientific ReportsHere is a quick summary.

Human cultural capacity has been traditionally dated to about 30-40 thousands of years ago, based on an impressive cultural explosion in Europe around that time, leaving us such evidence as sophisticated stone tools and plenty of “art” (objects without any clear practical use), like the figurine depicted to the right, the lion man, and striking cave paintings.

There is a problem, though. If cultural capacity evolved in Europe 30-40 thousand years ago, how did all the human groups that where living outside Europe get it? We have no evidence of genetic flow from Europe to the rest of the world, through which the genes responsible for cultural capacity could have spread. It appears that humans must have had the capacity to create complex culture before they fragmented geographically over a large area. This conclusion, however, appears equally problematic because the first split between human populations is currently dated at about 170,000 years ago. Thus humans would have had the capacity for complex culture for more than 100,000 years before complex culture actually appeared. Although this appears unreasonable, we argue that things actually went this way.

First, we note that archaeologists have unearthed stone tools of complexity comparable to that of the European cultural explosion, but much older (more than 200,000 years old). We also note that other indicators of behavioral modernity appeared earlier than 170,000 years ago, such as genes believed to be important for language and the morphology of the speech apparatus.

Second, we summarize recent work in cultural evolutionary theory showing that cultural evolution is, in its initial stages, exceedingly slow. The reason is essentially that culture is a cumulative process: Complex culture can be created only by building on already existing culture. Thus in the initial stages of cultural evolution there was not enough raw material to be elaborated upon, and the creation of culture was slow. Additionally, human groups were at this time small and scattered over a large area, hence it is likely that cultural elements have been invented many times but disappeared (we make a couple of examples in the paper).

The bottom line is that there is no evidence inconsistent with an early origin of cultural capacity, and current understanding of cultural evolution shows that a long gap between the genetic evolution of the capacity and the actual invention is, in fact, quite expected.

And, we suggest in the paper, Neanderthals may have had the same cultural capacity as ourselves.

An identity on falling powers

Here is a bit of combinatorics I encountered when preparing a paper on the co-evolution of behavioral repertoire, brain size, and lifespan (I will talk about the paper another time…). Let’s begin with two definitions:

Definition 1: The falling power n^{\underline{m}} is defined as the product n(n-1)\cdots(n-m+1), or:

(1)    {\displaystyle n^{\underline{m}}=\prod_{i=1}^m (n-i+1)}

If you are familiar with binomial coefficients, they are related to falling powers by n^{\underline m} = m! {n \choose m}.

Definition 2: The Stirling numbers of the second kind are a double series of numbers that tell us how many ways there are to partition n objects into k non-empty subsets. This is written sometimes S(n,k) and sometimes \left\{n\atop k\right\}. I will use the fancier notation. For example, there are only 3 ways to partition 3 elements in to 2 non-empty subsets: (12)(3), (13)(2), (1)(23), where (xy) means that x and y have been put together in the same subset. These numbers, beyond the simplest case, are nowhere near intuitive (at least to me). For example, There are 7 ways to partition 4 objects into 2 subsets, hence \left\{4\atop 2\right\}=7 (you can figure these ways out for yourself), and 1701 ways to partition 8 objects in 4 subsets, hence \left\{8\atop 4\right\}=1701 (I suggest you do not try this on your own).

Now, it happens that falling powers and Stirling numbers of the second kind are related by the following identity:

(2)    {\displaystyle n^m = \sum_{k=0}^m\left\{m\atop k\right\}n^{\underline k}}

I have only seen this equation proved by induction, but working on the above-mentioned paper I stumbled upon a direct proof that goes as follows. Note first that n^m is the number of ways to arrange n objects in sequences of length m, with repetitions possible (by sequence I mean an ordered selection so that (1,2,2) and (2,1,2) are two different sequences). So we have

(4)    \mbox{\# different sequences of }m\mbox{ objects chosen with repetition among }n = n^m

Equation (2) then comes from the fact that its r.h.s. is a different (and more laborious) way to count the same sequences. In other words, we can first count the sequences that we can form using only k out of the n objects, and then sum over k:

(5)    {\displaystyle n^{m} = \sum_{k=0}^n \mbox{\# different sequences of }m\mbox{ objects using any }k\mbox{ objects out of }n}

 Now we have to calculate the expression in the sum. Consider thus constructing a sequence of length m out of k distinct object, which in turn have been selected among n. There are n^{\underline k} ways of selecting which of the k objects are going to be part of the sequence, given that the first object out of n, the second out of n-1, and so on, until the k-th object can be selected out of n-k+1. Once we have the k objects, in how many ways we can allocate them among the m places of the sequence? This is exactly the number of ways in which a set of size m can be partitioned in k non-empty subsets, or, if you want, the number of ways in which m balls can be placed in k bins without leaving any one bin empty. Thus

(6)    {\displaystyle \mbox{\# different sequences of }m\mbox{ objects using any }k\mbox{ objects out of }n} = \left\{m\atop k\right\} n^{\underline k}

which, together with (5), gives (2).

Empirical support for openness-persuasiveness dynamics

A recent study by Aral & Walker provides support that the openness-persuasiveness dynamics we suggested a few years ago actually goes on in cultural evolution. In short, we had put forward mathematical and simulation models to support the notion that learning from others produces individuals that, over time, become more conservative (less likely to learn from others) and more persuasive (more likely to convince others of one’s own ideas). These predictions have been confirmed by Aral & Walker, who showed that older Facebook users are more difficult to convince do adopt a Facebook app than younger users, and yet are better at convincing others to adopt the app. Up to now, we only had indirect evidence about openness (older people score low on openness in personality tests), and no evidence on persuasion.

We have submitted a comment to the journal relating Aral & Walker’s intriguing findings to our theory. You can find a slightly extended version here, essentially with more references to relevant work.

Human Cognitive Uniqueness conference videos are online!

Watch them here!

If baboons can read, can pigeons, too?

“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.

Understanding Human Uniqueness Flyer

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!

New paper: The logic of fashion cycles

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.

Video: Stefano Adamo, The Social Diffusion of Specialist Knowledge

Part of the Cultural Evolution Seminar Series at Brooklyn College

(Download video, 660 MB)

Abstract: I argue that the social diffusion of specialist knowledge is contingent upon a combination of environmental and cognitive factors that make such ideas significant to the lay person and motivates their social transmission and retention. The same combination of factors, however, also engenders an incomplete comprehension of the ideas being spread. I propose a qualitative method to understand what makes specialist knowledge relevant and anticipate how lay peoplemay retain and spread such knowledge.

Stefano Adamo is Reader in Italian History and Culture at the University of Banja Luka, Bosnia Herzegovina, and fellowat the International Center of Economic Research, Turin, Italy. His research interests include the history of ideas and the cognitive theory of culture,especially the history of economic concepts (money, market, etc.) and their social diffusion.

Video: R. Alex Bentley, Social Influence and Drift in Collective Behavior

Part of the Cultural Evolution Seminar Series at Brooklyn College

Download Video (500MB)

Abstract: Human decision models often begin with individual, cost-benefit analyses as the basic behavior, with any social influence as a secondary add-on. Thisoften underestimates social influence among humans, whose brains have actually evolved to handle social relations. In fact, a better starting point in many casesmay be to assume that people base their choices (consciously or not) primarily on the decisions of those around them. As captured by experiments and simpleevolutionary drift models, undirected social influence introduces an irrationality and unpredictability to collective behavior, with implications for anthropology,psychology and economics.

Alex Bentley is Reader in Anthropology at Durham University, where he is co-founder of the Centre for the Coevolution of Biologyand Culture, and Deputy Director of a 5 year project on ‘Tipping Points: Mathematics, Metaphors and Meanings”, funded by theLeverhulme Trust.