Culture, its evolution and anything inbetween
Here is a video of Terrence Deacon, someone who needs no introduction on this website, giving a talk at Irving K. Barber Learning Centre about his latest research into language evolution:
Continue reading “Language and Complexity: Evolution Inside Out”
According to the evolutionary psychologist Geoffrey Miller and his colleagues (e.g Miller 2000b), uniquely human cognitive behaviours such as musical and artistic ability and creativity, should be considered both deviant and special. This is because traditionally, evolutionary biologists have struggled to fathom exactly how such seemingly superfluous cerebral assets would have aided our survival. By the same token, they have observed that our linguistic powers are more advanced than seems necessary to merely get things done, our command of an expansive vocabulary and elaborate syntax allows us to express an almost limitless range of concepts and ideas above and beyond the immediate physical world. The question is: why bother to evolve something so complicated, if it wasn’t really all that useful?
Miller’s solution is that our most intriguing abilities, including language, have been shaped predominantly by sexual selection rather than natural selection, in the same way that large cumbersome ornaments, bright plumages and complex song have evolved in other animals. As one might expect then, Miller’s theory of language evolution has been hailed as a key alternative to the dominant view that language evolved because it conferred a distinct survival advantage to its users through improved communication (e.g. Pinker 2003). He believes that language evolved in response to strong sexual selection pressure for interesting and entertaining conversation because linguistic ability functioned as an honest indicator of general intelligence and underlying genetic quality; those who could demonstrate verbal competence enjoyed a high level of reproductive success and the subsequent perpetuation of their genes. Continue reading “The Problem With a Purely Adaptationist Theory of Language Evolution”
I’m not normally one for violent resolutions to sloppy science, but in taking inspiration from one such perpetrator I’m promoting a Falcon Punch policy. Above is a graphical example of a successful Falcon Punch: the goal being to hurl your target onwards and upwards into a flaming ball of scientific shame.
Space is the final frontier for evolution, study claims. I had planned on writing a more substantial article on how yet another science writer, in this case one Howard Falcon-Lang, is claiming that Darwin has once again been felled by a new study. Greg Laden, however, beat me to the punch with a damning critique:
Continue reading “Some Links #16: Why I want to Falcon Punch (some) BBC Science Writers”
Cultural differences are often attributed to events far removed from genetics. The basis for this belief is often based on the assertion that if you take an individual, at birth, from one society and implant them in another, then they will generally grow up to become well-adjusted to their adopted culture. Whilst this is more than likely true, even if there may be certain cultural features that may disagree with someone of a different ethnic background (e.g. degrees of alcohol tolerance), the situation is not as clear cut as certain political factions may have you believe. Yet, largely due to studies on gene-culture coevolution, we are now starting to understand the complex dynamics through which genes and culture interact.
First, a particular culture may exert selection pressures on genes that provide an advantageous benefit to the adoption of a particular cultural trait. This is evident in the strong selection of the lactose-tolerance allele due to the spread of dairy farming. Second, pre-existing gene distributions provide pressures through which culture adapts. Off the top of my head, one proposed example of this is a paper by Dediu and Ladd (2007), which looked at how the distribution of the derived haplotypes of ASPM and Microcephalin may have subtly influenced the development of tonal languages. The paper in question, however, is looking more broadly at culture. Specifically, the authors, Baldwin May and Matthew Lieberman, examine recent genetic association studies and how within-variation of genes involved in central neurotransmitter systems are associated with differences in social sensitivity. In particular, they highlight a correlation between the relative frequencies of certain gene-variants and the relative degree of individualism or collectivism within certain populations.
Continue reading “Genetic Components and Cultural Differences: The social sensitivity hypothesis”
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.
Recently, David Burkett and Tom Griffiths have looked at iterated learning of multiple languages from multiple teachers (Burkett & Griffiths 2010, see my post here). Here, I’ll describe a simpler model which allows bilingualism. I show that, counter-intuitively, bilingualism may be more stable than monolingualism.
Hello! This is my first post here at Replicated Typo and I thought I’d start with reposting a slightly modified version of a three-part series on the evolution of the human mind that I did last year over at my blog Shared Symbolic Storage.
So in this and my next posts I will have a look at how human cognition evolved from the perspective of cognitive science, especially ‘evolutionary linguistics,’ comparative psychology and developmental psychology.
In this post I’ll focus on the evolution of the human brain.
Human Evolution
We are evolved primates. (As are all other primates of course. So maybe it is better to say that we, like all other primates, are evolved beings with a unique set of specializations, adaptations and features. )
In our lineage, we share a common ancestor with orangutans (about 15 million years ago (mya)), gorillas (about 10mya), and most recently, chimpanzees and bonobos (5 to 7 mya). We not only share a significant amount of DNA with our primate cousins, but also major anatomical features (Gazzaniga 2008: 51f., Lewin 2005: 61) These include, for example, our basic skeletal anatomy, our facial muscles, or our fingernails (Lewin 2005: 218ff.).
What most distinguishes us as humans on an anatomical level are our bizarre hair distribution, our upright posture and the skeletal modifications necessary for it, including a propensity for endurance running, our opposable thumbs, fat deposits that are unusually extensive (Preuss 2004: 5), and an intestinal tract only 60% the size expected of primates our size (Gibbons 2007: 1558).
Finally, there is also a distinguishing feature that is a much more remarkable violation of expectations – a brain three times the size expected of a primate our size. This is all the more interesting as primates are already twice as encephalized as other mammals (Lewin 2005: 217). A direct comparison shows this difference in numbers: Whereas human brains have an average volume of 1251.8 cubic centimetres and weigh about 1300 gram, the brains of the other great apes only have an average volume of 316.7 cubic centimetres and weigh between 350-500 gram (Rilling 2006: 66, Preuss 2004: 8). In a human brain, there are approximately a hundred billion neurons, each of which is connected to about one thousand other neurons, comprising about one hundred trillion synaptic connections (Gazzaniga 2008: 291). If you would count all the connections in the napkin-sized cortex alone, you’d only be finished after 32 million years (Edelman 1992: 17).
Expensive Tissue
The human brain is also extremely “expensive tissue” (Aiello & Wheeler 1995): Although it only accounts for 2% of an adult’s body weight, it accounts for 20-25% of an adult’s resting oxygen and energy intake (Attwell & Laughlin 2001: 1143). In early life, the brain even makes up for up 60-70% of the body’s total energy requirements. A chimpanzee’s brain, in comparison, only consumes about 8-9% of its resting metabolism (Aiello & Wells 2002: 330). The human brain’s energy demands are about 8 to 10 times higher than those of skeletal muscles (Dunbar & Shultz 2007: 1344), and, in terms of energy consumption, it is equal to the rate of energy consumed by leg muscles of a marathon runner when running (Attwell & Laughlin 2001: 1143). All in all, its consumption rate is only topped by the energy intake of the heart (Dunbar & Shultz 2007: 1344).
Consequently, if we want to understand the evolutionary trajectory that led to human cognition there is the problem that
“because the cost of maintaining a large brain is so great, it is intrinsically unlikely that large brains will evolve merely because they can. Large brains will evolve only when the selection factor in their favour is sufficient to overcome the steep cost gradient“ (Dunbar 1998: 179).
This is especially important for people who want to come up with an “adaptive story” of how our brain got so big: they have to come up with a strong enough selection pressure operative in the Pleistocene “environment of evolutionary adaptedness” that would have allowed such “expensive tissue” to evolve in the first place (Bickerton 2009: 165f.).
What About the Brain is Uniquely Human?
If we look to the brain for possible hints, we first find that presently, there is “no good evidence that humans do, in fact, possess uniquely human cortical areas” (although the jury is still out) (Preuss 2004: 9). In addition, we find that there are functions specific to humans which are represented in areas homologous to areas of other primates. Instead, it seems that in the course of human evolution some of the areas of the brain expanded disproportionally, “especially higher-order cortical areas, including the prefrontal cortex” (Preuss 2004: 9, Deacon 1998: 435-438). This means that humans are not simply ‘better’ at thinking than other animals, but that they think differently (Preuss 2004: 7). The expansion and apparent specializations of only certain kinds of neuronal areas could indicate a qualitative shift in neuronal activity brought about by re-organization of existing features, leading to a wholly different style of cognition (Deacon 1998: 435-438 Rilling 2006: 75).
This scenario squares well with what we know about the way evolution works, namely that it always has to work with the raw materials that are available, and constantly co-opts and tinkers with existing structures, at times producing haphazard, cobbled-together, but functional results (Gould & Lewontin 1979, Gould & Vrba 1982). Given the relatively short time span for the evolution of the “most complex structure in the know universe”, as it is sometimes referred to, we have to acknowledge how preciously little time the evolutionary process had for ‘debugging.’ It could well be that make the human mind is so unique because it is an imperfect ‘Kluge:’ “a clumsy or inelegant – yet surprisingly effective – solution to a problem,” like the Apollo 13 CO2 filter or an on-the-spot invention by MacGyver (Marcus 2008: 3f.). It may thus well turn out that what we think makes us so special is a mental “oddity of our species’ way of understanding” the world around us (Povinelli & Vonk 2003: 160). It is reasonable then to assume that human cognition did not just simply get better across the board, but that instead we owe our unique style of thinking to quite specific specializations of the human mind.
With this in mind, we can now ask the question how these neurological differences must translate into psychological differences. But this is where the problem starts: Which features really distinguish us as humans and which are more derivative than others? A true candidate for what got uniquely human cognition off the ground has to pass this test and solve the problem how such “expensive tissue” could evolve in the first place.
In my next post I will have a look at six candidates for what makes human cognition unique.
References:
Aiello, L., & Wheeler, P. (1995). The Expensive-Tissue Hypothesis: The Brain and the Digestive System in Human and Primate Evolution Current Anthropology, 36 (2) DOI: 10.1086/204350
Aiello, L., & Wells, J. (2002). ENERGETICS AND THE EVOLUTION OF THE GENUS HOMO Annual Review of Anthropology, 31 (1), 323-338 DOI: 10.1146/annurev.anthro.31.040402.085403
Attwell, David and Simon B. Laughlin. (2001.) “An Energy Budget for Signaling in the Grey Matter of the Brain.” Journal of Cerebral Blood Flow and Metabolism 21:1133–1145.
Bickerton, Derek (2009): Adams Tongue: How Humans Made Language. How Language Made Humans. New York: Hill and Wang.
Deacon, Terrence William (1997). The Symbolic Species. The Co-evolution of Language and the Brain. New York / London: W.W. Norton.
Dunbar, Robin I.M. (1998): “The Social Brain Hypothesis Evolutionary Anthropology 6: 178-190.
Dunbar, R., & Shultz, S. (2007). Evolution in the Social Brain Science, 317 (5843), 1344-1347 DOI: 10.1126/science.1145463
Edelman, Gerald Maurice (1992) Bright and Brilliant Fire: On the Matters of the Mind. New York: Basic Books
Gazzaniga, Michael S. (2008): Human: The Science of What Makes us Unique. New York: Harper-Collins.
Gibbons, Ann. (2007) “Food for Thought.” Science 316: 1558-1560.
Gould, Stephen Jay and Richard Lewontin (1979): “The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme.” Proclamations of the Royal. Society of London B: Biological Sciences 205 (1161): 581–98.
Gould, Stephen Jay, and Elizabeth S. Vrba (1982), “Exaptation — a missing term in the science of form.” Paleobiology 8 (1): 4–15.
Lewin, Roger (2005): Human Evolution: An Illustrated Introduction. Oxford: Blackwell.
Marcus, Gary (2008): Kluge: The Haphazard Evolution of the Human Mind. London: Faber and Faber.
Povinelli, Daniel J. and Jennifer Vonk (2003): “Chimpanzee minds: Suspiciously human?” Trends in Cognitive Sciences, 7.4, 157–160.
Preuss Todd M. (2004): What is it like to be a human? In: Gazzaniga MS, editor. The Cognitive Neurosciences III, Third Edition. Cambridge, MA: MIT Press: 5-22.
Rilling, J. (2006). Human and nonhuman primate brains: Are they allometrically scaled versions of the same design? Evolutionary Anthropology: Issues, News, and Reviews, 15 (2), 65-77 DOI: 10.1002/evan.20095
The limitations of geological periods, imposed by physical science, cannot, of course, disprove the hypothesis of transmutation of species; but it does seem sufficient to disprove the doctrine that transmutation has taken place through ‘descent with modification by natural selection’. — Lord Kelvin (Of Geological Dynamics, 1869).
It might seem odd that I start a post about evolution with a quote claiming natural selection is inadequate to account for the transmutation of species. It is, though, highly relevant to what I’m going to discuss in the post, and strikes at the heart of why it’s fundamental for us to understand the theory of evolution by natural selection. See, in 1869, Lord Kelvin’s position was fairly reasonable, and, as you’d expect for a man of such high scientific standing, the available evidence in physics did seem to conflict with Darwin’s theory. The Sun was one particularly salient point of contention: to get the diversity of species we see on Earth, evolution needs a long time to work (on the order of hundreds of millions, if not billions of years), yet according to 19th-century physics the Sun could only have been burning for 40-million years.
Continue reading “A history of evolution pt.1: Ancient Greece to Lamarck”
For me, recent computational accounts of language evolution provide a compelling rationale that cultural, as opposed to biological, evolution is fundamental in understanding the design features of language. The basis for this rests on the simple notion of language being not only a conveyor of cultural information, but also a socially learned and culturally transmitted system: that is, an individual’s linguistic knowledge is the result of observing the linguistic behaviour of others. Here, this well-attested process of language acquisition, often termed Iterated Learning, emphasises the effects of differential learnability on competing linguistic variants. Sounds, words and grammatical structures are therefore seen to be the products of selection and directed mutation. As you can see from the use of terms such as selection and mutation it’s clear we can draw many parallels between the literature on language evolution and analogous processes in biology. Indeed, Darwin himself noted such similarities in the Descent of Man. However, one aspect evolutionary linguists don’t seem to borrow is that of a null model. Is it possible that the changes we see in languages over time are just the products of processes analogous to genetic drift?
Continue reading “Words as alleles: A null-model for language evolution?”
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:
Continue reading “Mathematical Modelling 101: Introduction & Viability Selection”
