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.

What Makes Humans Unique? (II): Six Candidates for What Makes Human Cognition Uniquely Human

ResearchBlogging.orgWhat makes humans unique? This never-ending debate has sparked a long list of proposals and counter-arguments and, to quote from a recent article on this topic,

“a similar fate  most likely awaits some of the claims presented here. However such demarcations  simply  have  to  be  drawn  once  and  again.  They  focus  our  attention, make us wonder, and direct and stimulate research, exactly because they provoke and challenge other researchers to take up the glove and prove us wrong.” (Høgh-Olesen 2010: 60)

In this post, I’ll focus on six candidates that might play a part in constituting what makes human cognition unique, though there are countless others (see, for example, here).

One of the key candidates for what makes human cognition unique is of course language and symbolic thought. We are “the articulate mammal” (Aitchison 1998) and an “animal symbolicum” (Cassirer 2006: 31). And if one defining feature truly fits our nature, it is that we are the “symbolic species” (Deacon 1998). But as evolutionary anthropologists Michael Tomasello and his colleagues argue,

“saying that only humans have language is like saying that only humans build skyscrapers, when the fact is that only humans (among primates) build freestanding shelters at all” (Tomasello et al. 2005: 690).

Language and Social Cognition

According to Tomasello and many other researchers, language and symbolic behaviour, although they certainly are crucial features of human cognition, are derived from human beings’ unique capacities in the social domain. As Willard van Orman Quine pointed out, language is essential a “social art” (Quine 1960: ix). Specifically, it builds on the foundations of infants’ capacities for joint attention, intention-reading, and cultural learning (Tomasello 2003: 58). Linguistic communication, on this view, is essentially a form of joint action rooted in common ground between speaker and hearer (Clark 1996: 3 & 12), in which they make “mutually manifest” relevant changes in their cognitive environment (Sperber & Wilson 1995). This is the precondition for the establishment and (co-)construction of symbolic spaces of meaning and shared perspectives (Graumann 2002, Verhagen 2007: 53f.). These abilities, then, had to evolve prior to language, however great language’s effect on cognition may be in general (Carruthers 2002), and if we look for the origins and defining features of human uniqueness we should probably look in the social domain first.

Corroborating evidence for this view comes from comparisons of brain size among primates. Firstly, there are significant positive correlations between group size and primate neocortex size (Dunbar & Shultz 2007). Secondly, there is also a positive correlation between technological innovation and tool use – which are both facilitated by social learning – on the one hand and brain size on the other (Reader and Laland 2002). Our brain, it seems, is essential a “social brain” that evolved to cope with the affordances of a primate social world that frequently got more complex (Dunbar & Shultz 2007, Lewin 2005: 220f.).

Thus, “although innovation, tool use, and technological invention may have played a crucial role in the evolution of ape and human brains, these skills were probably built upon mental computations that had their origins and foundations in social interactions” (Cheney & Seyfarth 2007: 283).

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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:

  1. 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.
  2. 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.
  3. 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:
  4. 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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