Boyd – Replicated Typo

Some Links #18: GxExC

The depression map: genes, culture, serotonin, and a side of pathogens. Another new science blog network (Wired) and once again a new stable of good science writers. I’m particularly pleased to see that David Dobbs, a former SciBling and top science writer, has found a new home for Neuron Culture. I was also pleased to see he had written an article on studies into the interactions between genes and culture, namely: Chiao & Blizinsky (2009) and Way & Lieberman (2010). And I was even more pleased to see that he’d mentioned both mine and Sean’s posts on the social sensitivity hypothesis. Suffice to say, I was pleased.

Take home paragraph:

In a sense, these studies are looking not at gene-x-environment interactions, or GxE, but at genes x (immediate) environment x culture — GxExC. The third variable can make all the difference. Gene-by-environment studies over the last 20 years have contributed enormously to our understanding of mood and behavior. Without them we would not have studies, like these led by Chiao and Way and Kim, that suggest broader and deeper dimensions to what makes us struggle, thrive, or just act differently in different situations. GxE is clearly important. But when we leave out variations in culture, we risk profoundly misunderstanding how these genes — and the people who carry them — actually operate in the big wide world.

Razib also has some thoughts on the topic:

The same issues are not as operative when it comes to culture. Two tribes can speak different dialects or languages. If a woman moves from one tribe to another her children don’t necessarily speak a mixture of languages, rather, they may speak the language of their fathers. The nature of cultural inheritance is more flexible, and so allows for the persistence of more heritable variation at different levels of organization. Differences of religion, language, dress, and values, can be very strong between two groups who have long lived near each other and may be genetically similar.

Homo was born vocalizing. Babel’s Dawn links to a recently finished PhD thesis that supposedly argues for a relatively recent emergence for language (approx. 120,000 years ago). She defends her assertions by stating: “[…] all of the unique cognitive traits attributed to humans arose as the consequence of one crucial mutation, which radically altered the architecture of the ancestral primate brain.” I haven’t read the thesis, and I probably won’t as I’m already stretched in regards to my reading, but I’m completely unconvinced by the hopeful mutation hypothesis. Plus, as Bolles notes in his post, there is plenty of available evidence to the contrary.

Primed for Reading. Robert Boyd reviews Stanislas Dehane’s new book, Reading in the Brain: The Science and Evolution of a Human Invention, which I’ll be picking up soon. In the meantime, to give you a bit of background, I suggest you read Dehane’s (2007) paper on the Neuronal Recycling Hypothesis: the Cultural recycling of cortical maps. H/T: Gene Expression.

Through the looking glass (part 1). The Lousy Linguist reviews Guy Deutscher’s new book, Through the Language Glass: Why the World Looks Different in Other Languages, with the general takeaway message being that, in part one at least, one where the book is a bit science-lite. What really interested me, though, were these two paragraphs:

We discover quite quickly what Deutscher is doing as he begins to walk through complexity issues of “particular areas of language” (page 109), namely morphology, phonology, and subordination. And these last 15 pages are really the gem of Part 1. He shows that there is an interesting, somewhat illogical, entirely engaging but as yet unexplained set of correlations between speaker population size and linguistic complexity.

For example, languages with small numbers of speakers tend to have more morphologically rich grammars (hence one could claim that small = more complex). However, small languages with small numbers of speakers also tend to have small phonological inventories. Hmmm, weird, right? [My emphasis]

As those of you who read this blog will know: I don’t think it’s weird that small speaker populations also tend to have small phonological inventories.

Clothing lice out of Africa. A cool new paper by Troups et al which looks at the evolutionary history of clothing lice to provide specific estimates on the origin of clothing. Using a Bayesian coalescent modelling approach, they estimate that clothing lice diverged from head louse ancestors between 83,000 and 170,000 years ago. H/T: Dienekes.

Selection on Fertility and Viability

So in my previous post on mathematical modelling I looked at viability selection and how it can be expressed using relatively simple mathematics. What I didn’t mention was fertility. My reasoning largely being because the post was already getting unwieldy large for a blog, and, from now on, I’m going to limit the length on these math-based posts. I personally find I get more out of small, bite-sized chunks of information that are easily digestible, than overloading myself by trying understand too many concepts all at once. With that said, I’ll now look at what happens when the two zygote types, V(A) and V(B), differ in their fertility.

A good place to start is by defining the average number of zygotes produced by each type as z(A) and z(B). We can then plug these into a modified version of the recursion equation I used in the earlier post:

So now we can consider both fertility and viability selection. Furthermore, this can be combined to give us W(A) = V(A)z(A) and W(B) = V(B)z(B):

Remember, , is simply the the average the fitness in the population, which can be used in the following difference equation:

That’s it for now. The next post will look at the long-term consequences of these processes.

Reference: McElreath & Boyd (2007). Mathematical Models of Social Evolution: A guide for the perplexed. University of Chicago Press. Amazon link.

Mathematical Modelling 101: Introduction & Viability Selection

I think the best place to start would be to state the following: Do not fear math. I spent far too long dodging equations and, when that wasn’t possible, freezing in a state of absolute confusion when faced with something like:

By the end of this post, you’ll hopefully be able to understand the above is not just a bunch of jibberish. Now before we get into the nitty gritty of the subject, I think a clarification of my assumptions is in order:

That you’ll have a basic understanding of evolutionary biology. If not, then may I suggest Evolution as a very good, and highly comprehensive, introductory text. Failing that, you can always pop over to the wikipedia page.
Although these posts will refer to evolutionary biology, my background is in linguistics and socio-cultural evolution — and as such, I will tend to default to the position of explaining these latter areas.
It might sound insulting, but you’ll also need a basic understanding of math. You’ll be surprised by the number of people who, despite being very bright, lack even an elementary grasp of the fundamentals. A good place to start is with Kahn Academy’s wonderful online resource: http://www.khanacademy.org/.
Having said that, I’m not really expecting anything beyond algebra level math, and I’ll do my best to try and clarify any confusions in the comments section. Also, I’m hardly a math guru, so I welcome anyone with a solid background in math to provide any hints, tips or suggestions, and, in the event I’m plain wrong, point out any mistakes.

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Some Links #5

The returns on homogeneity Razib Kahn writes about the potential costs of the world having diversity in its languages, instead of just one. He also asks: “The extreme linguistic diversity of less developed regions of the world, or even 18th century France and Italy, is probably detrimental to economic growth and economies of scale, but do diminishing returns kick in at some point?” I’m not too sure where my thoughts lie on this, as I’ve never really thought about it before, which, for me at least, is always the sign of a good blog post. Of course, the economic woes or pros will be negated once the universal translator is made…

Cultural Induction is hard Sean Roberts offers a very thought-provoking post about cultural induction. A week or so ago he ran a little experiment on Facebook, with the explicit aim of looking at Welsh Mutations and agreements between Welsh-speaking individuals in regards to simple sentences. All this fits into a larger picture, with Sean arguing, quite persuasively, that “cultural induction may not be easier than learning about the natural world if everybody is doing something different.”

Cultural Evolution I tend to think I write fairly in-depth posts about cultural evolution, but it appears Bill Benzon over at New Savanna has dethroned me with a knock out tome of posts. These include one on language games, which, in the spirit of being completely honest, I haven’t yet had chance to completely read. I think a New Savanna day is due at some point next week.

Simon Jenkins writes something stupid, and in doing so invites a whole number of science bloggers to have their very own spoof Jenks day, in which (apparently) evil boffins seek revenge.

A new Papua tribe is discovered. Numbering around 3000 the tribe will surely be of interest to field linguists. They also apparently live in trees and run around completely naked (apart from banana leaves covering their private parts).

Culture as an evolutionary phenomenon. An interesting lecture by Rob Boyd over at the ICCI’s website.

Population size predicts technological complexity in Oceania

Here is a far-reaching and crucially relevant question for those of us seeking to understand the evolution of culture: Is there any relationship between population size and tool kit diversity or complexity? This question is important because, if met with an affirmative answer, then the emergence of modern human culture may be explained by changes in population size, rather than a species-wide cognitive explosion. Some attempts at an answer have led to models which make certain predictions about what we expect to see when populations vary. For instance, Shennan (2001) argues that in smaller populations, the number of people adopting a particular cultural variant is more likely to be affected by sampling variation. So in larger populations, learners potentially have access to a greater number of experts, which means adaptive variants are less likely to be lost by chance (Henrich, 2004).

Models aside, and existing empirical evidence is limited with the results being mixed. I previously mentioned the gradual loss of complexity in Tasmanian tool kits after the population was isolated from mainland Australia. Elsewhere, Golden (2006) highlighted the case of isolated Polar Inuit, who lost kayaks, the bow and arrow and other technologies when their knowledgeable experts were wiped out during a plague.Yet two systematic studies (Collard et al., 2005; Read, 2008) of the Inuit case found no evidence for population size being a predictor of technological complexity.

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Experiments in cultural transmission and human cultural evolution

For those of you familiar with the formal mathematical models of cultural evolution (Cavalli-Sforza & Feldman, 1981; Boyd & Richerson, 1985), you’ll know there is a substantive body of literature behind the process of cultural transmission. It comes as a surprise, then, that experiments in this area are generally lacking.

For instance, if we look at evolutionary biology, then there are many experiments into small-scale microevolutionary processes, such as natural selection, sexual selection, mutation and drift, which are then applied in showing how these processes generate population-level, macroevolutionary patterns. It follows then, that this sort of population-level thinking can be applied to cultural evolution: the forces and biases of cultural transmission can be studied experimentally to see if they fit with population-level patterns of cultural change documented by scientists. As the current paper by Mesoudi & Whiten (2008) notes, this potentially gives cultural transmission experiments added significance: “cultural transmission should not only be studied for its own sake (i.e. in order to better understand cultural transmission itself), but also in order to explain broader cultural patterns and trends, all as part of a unified science of cultural evolution”.

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Cultural innovation, Pleistocene environments and demographic change

It is well documented that Thomas Robert Malthus’ An Essay on the Principle of Population greatly influenced both Charles Darwin and Alfred Russell Wallace’s independent conception of their theory of natural selection. In it, Malthus puts forward his observation that the finite nature of resources is in conflict with the potentially exponential rate of reproduction, leading to an inevitable struggle between individuals. Darwin took this basic premise and applied it to nature, as he notes in his autobiography:

In October 1838, that is, fifteen months after I had begun my systematic inquiry, I happened to read for amusement Malthus on Population, and being well prepared to appreciate the struggle for existence which everywhere goes on from long-continued observation of the habits of animals and plants, it at once struck me that under these circumstances favourable variations would tend to be preserved, and unfavourable ones to be destroyed. The results of this would be the formation of a new species. Here, then I had at last got a theory by which to work.

The interaction of demographic and evolutionary processes is thus central in understanding Darwin’s big idea: that exponential growth will eventually lead to a large population, and in turn will generate competition for natural selection to act on any heritable variation which conferred a greater fitness advantage. Under these assumptions we are able to interpret the evolutionary record of most species by appealing to two basic causal elements: genes and the environment. As we all know, in most cases the environment generates selection pressures to which genes operate and respond. For humans, however, the situation becomes more complicated when we consider another basic causal element: culture. The current paper by Richerson, Boyd & Bettinger (2009) offers one way to view this muddied situation by delineating the demographic and evolutionary processes through the notion of time scales:

The idea of time scales is used in the physical environmental sciences to simplify problems with complex interactions between processes. If one process happens on a short time scale and the other one on a long time scale, then one can often assume that the short time scale process is at an equilibrium (or in some more complex state that can be described statistically) with respect to factors governed by the long scale process. If the short time scale and long time scale interact, we can often imagine that at each time step in the evolution of the long time scale process, the short time scale process is at “equilibrium.” A separation of time scales, if justified, makes thinking about many problems of coupled dynamics much easier.

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Culture and the human genome: a synthesis of genetics and the human sciences

Humans are immersed in culture from birth. It is so fundamental to our experience, and what it means to be human itself, yet we often overlook the consideration that “cultural practices might have transformed the selection pressures acting on humans” (Laland, Odling-Smee & Myles, 2010, pg. 137).

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Cultaptation Conference

Earlier this year I went along to the Cultaptation Conference at St Andrews. Despite being a fascinating event, there appears to nothing on the blogsphere pertaining to the speakers and their talks. In fact, this generally holds true for cultural evolution: there are no dedicated blogs reporting what is undoubtedly a serious scientific endeavour. As a remedy I’m going to dedicate several future blog posts to the conference. Until then, here are the talk abstracts for some of my personal highlights:

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