Evolution of Colour Terms: 10 Universal Patterns are not Evidence for Innate Constraints

In a series of posts, I’ve been discussing constraints on the evolution of colour terms. Here, I discuss the role of drift and also argue that universal patterns are not necessarily good evidence for innate constraints. For the full dissertation and references, go here.

Drift

An important point which has not been highlighted in the literature is the drift introduced by cultural transmission.  Perceptual systems are noisy, and change over lifetimes.  Therefore, systems of categorising these perceptions may drift over time.  However, if concepts are shared, this drift is influenced by more than one system.  This may cause a different kind of drift from a stand-alone system for self-thought.  Communication has an additional semantic bottleneck which self-though does not have.  Using language for self thought, if you don’t know a label, you can make one up.

However, for communication, this won’t work.  For example, in models of cultural transmission (e.g., Steels & Belpaeme, 2005) agents do create new labels but, importantly, accept the speaker’s label when available.  That is, communicative systems are more flexible than systems for self-thought (communicators must be more willing to change their minds), and so are more subject to drift.  The drift allows the system to move around the possible space of coding efficiency and object categorisation efficiency.  Peaks in these landscapes will attract the drift, hence environmental and perceptual constraints being projected into language.

Although systems of colour categorisation for self-thought may be more efficient if they were constrained by the environment, shared cultural systems are more likely to reflect constraints in the environment because they are more flexible.  That is, perceptual constraints have projected themselves into language because of a communicative pressure, rather than a perceptual or environmental pressure.

I suggest that this drift, together with an ability for categories to warp perceptual spaces, would mean that individuals converge on a shared perceptual system.  If comprehension involves the activation of perceptual representations, then communication involves individuals reaching similar perceptual representations or, in a perfect world, activation of the same neural substrates.  Therefore, a population with a shared perceptual system would be able to communicate much more effectively.  In this sense, Embodied systems improve communicative success, whereas the same effect is not necessarily true of Symbolist systems. Furthermore, this drift means that populations can still converge on similar solutions, without having to assume that Universal biases are the main driving force.  It has been argued that the similarities in colour categorisation between cultures contradicts Relativism, which would predict a large variation in colour categorisation between cultures (e.g., Belpaeme & Bleys, 2005).  I argue that this inference is not necessarily valid.

Summary

This series of posts has shown that a wide range of factors constrain the categorisation of colour, including the physiology of perception, the environment and cultural transmission.  Why is there evidence for Colour Terms being adapted to so many domains?

This study considered the idea that categorisation acquired by individuals can feed back into perception and itself become a constraint both on the development of categorisation, the environment and genetic inheritance.  In this sense, the feedback from categorisation allows Niche Construction dynamics to apply to linguistic categorisations.  It was argued that this dynamic fits with the Cultural implication of an Embodied account of language comprehension.  That is, this study has concluded, similarly to Kirby et al. (2007), that universal patterns across populations do not necessarily imply strong innate biases.  This was done by arguing that Cultural, Embodied systems tend to drift towards better representations of the real world, which involves better coherence with perceptual and environmental constraints, creating cross-cultural patterns.  Furthermore, an Embodied approach to cultural dynamics incorporating a mechanism for perceptual warping predicts that the perceptual spaces of individuals can be synchronised through language to achieve better communication.

Steels, L., & Belpaeme, T. (2005). Coordinating perceptually grounded categories through language: A case study for colour Behavioral and Brain Sciences, 28 (04) DOI: 10.1017/S0140525X05000087

Belpaeme, T. (2005). Explaining Universal Color Categories Through a Constrained Acquisition Process Adaptive Behavior, 13 (4), 293-310 DOI: 10.1177/105971230501300404

Kirby, S., Dowman, M., & Griffiths, T. (2007). Innateness and culture in the evolution of language Proceedings of the National Academy of Sciences, 104 (12), 5241-5245 DOI: 10.1073/pnas.0608222104

Specific Language Impairment, Autism and Language Evolution

My last post speculated about what some conditions which manifest impaired theory of mind could tell us about the evolution of ToM. Of these conditions autism was one which could be the most informative when it comes to looking at the genetics of how ToM evolved, in this post I will look at what autism could tell us, not only about theory of mind, but also about other aspects of the language faculty.

Dorothy Bishop has recently written a paper exploring the above average co-occurrence of Specific Language Impariment (SLI) and Autistic Spectrum Disorders (ASD).

SLI is a condition where a child fails to develop spoken language on the normal schedule, for no observable or obvious reason (Bishop and Norbury 2008). Whilst ASD and SLI are regarded as distinct conditions, these disorders co-occur at above chance levels.

Bishop (2010) explores why this might be. Bishop begins her paper by painting a textbook example of a child with SLI. This example is of a child with normal social interaction and nonverbal communication, but with specific difficulties in mastering structural aspects of language, especially syntax and phonological skills. So this typical picture is not one of an autistic child in that one of the defining features of autism is a limited capacity for normal social interaction and a child is much more likely to be deficient in pragmatic skills than syntactic or phonological skills.

Bishop states that despite the fact that according to conventional diagnostic frameworks, SLI andASD are mutually exclusive diagnoses, similarities exist between the two conditions and these include:

  • They are both highly heritable
  • Identical, monozygotic twins are significantly more concordant than fraternal, dizygotic twins for autism and SLI
  • In both conditions rates of impairment in first degree relatives are higher than in the general population
  • First degree relatives of affected individuals of both conditions often manifest sub-threshold symptoms
  • These conditions correspond to points on a continuum of impairment, rather than all-or-none diseases

So any model of causation for either condition must take into account the following considerations:

  • Above chance levels of comorbidity between SLI and ASD
  • Rates of language impairment in relatives of probands with SLI and ASD
  • Molecular genetic findings of shared genetic risk factors for ASD and SLI

Now the article goes on to explore etiological models which explain these considerations with varying degrees of success. I’m not going to pretend to understand these models as I have only ever been formally taught in linguistics and so I’m a bit stumped by genetic psychology. If you’re much smarter than me you can read the article yourself here:

http://www.springerlink.com/content/gg087q4h51j5127g/fulltext.pdf

So what I got from this article was that the genetic factors involved in autism can not only cause the characteristics typical of a person with autism (pragmatic impairments) but also other language impairments which are typical of a person with a Specific Language Impairment. Specifically the CNTNAP2 gene has been found in independent samples to be associated with both ASD and SLI. This is interesting because it could show that gene mutations which cause improved social abilities could have also caused changes in our linguistic ability on a syntactic or phonological level.

Disclaimer: Sorry if I’ve made too many assumptions in the conclusion I’ve just drawn. As I said above I know next to nothing about genetic psychology but I just felt this research would have interesting consequences in the field of language evolution. I’d love to hear the thoughts of people who know better than I do.

References

Bishop, DVM. (2010) Overlaps Between Autism and Language Impairment:
Phenomimicry or Shared Etiology? Behavior Genetics 40:5, 618-629

Bishop DVM, Norbury CF (2008) Speech and language disorders. In: Rutter M, Bishop DVM, Pine D, Scott S, Stevenson J, Taylor E, Thapar A (eds) Rutter’s child

Evolution of Colour Terms: 4 Learning Constraints

Continuing my series on the Evolution of Colour terms, this post reviews how learning constrains colour naming. For the full dissertation and for references, go here.

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New Blog: Culture Evolves!

… Well, new to me at least. It’s run by Fiona Jordan of the Max Planck Institute for Psycholinguistics, and her latest post is an interview with one of my favourite researchers, Simon Greenhill (I didn’t know he designed a sudoku solving program). Also, after having done a little digging into her publications, I found the following forthcoming paper: The effect of population size and density on rates of linguistic evolution. Here is the abstract:

Evolutionary theory from population genetics predicts that demography may play an important role in determining the rate at which cultural and linguistic traits change over time. However, relatively few studies have explored this relationship for language at an appropriate scale and in a quantitative way, nor controlled for the problem of non-independence induced by the historical relationships between languages. Here we use phylogenetic trees of 351 Austronesian languages to test whether the rate of change in core vocabulary is affected by population size and population density. We detected a strong phylogenetic signal in both population size and density, indicating the need for historical control. We find a significant inverse relationship between lexical replacement and population size, no relationship with population density, and we confirm that splitting events influence lexical evolution. These results support the idea that a process analogous to genetic drift may be an important factor in lexical evolution. Furthermore, the strong phylogenetic signal in these demographic factors suggests that despite repeated population splits the social conditions that influence speech community size and density are maintained and inherited from one generation to the next.

I’m not going to say anything on a paper I haven’t yet read, other than it looks pretty cool and that more people should be considering the influence of demographic factors in linguistics.

Evolution of Colour Terms: 2 Environmental Constraints

Continuing my series on the Evolution of Colour terms, this post reviews evidence for environmental constraints on colour perception. For the full dissertation and for references, go here.

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Evolution of Colour Terms: 1 Genetic Constraints

Continuing my series on the Evolution of Colour terms, this post reviews the evidence for genetic constrains on colour perception. For the full dissertation and for references, go here.

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Genetic Components and Cultural Differences: The social sensitivity hypothesis

ResearchBlogging.orgCultural 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.

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Population Size and Rates of Language Change

In previous posts, I’ve looked at the relationship between cultural evolution and demography (see here, here and here). As such, it makes sense to see if such methods are applicable in language which is, after all, a cultural product. So, having spent the last few days looking over the literature on language and demography, I found the following paper on population size and language change (free download). In it, the authors, Søren Wichmann and Eric Holman, use lexical data from WALS to test for an effect of the number of speakers on the rate of language change. Their general findings argue against a strong influence of  population size, with them instead opting for a model where the type of network influences change at a local level, through different degrees of connectivity between individuals. Here is the abstract:

Previous empirical studies of population size and language change have  produced  equivocal  results. We  therefore  address  the  question  with  a new set of lexical data from nearly one-half of the world’s languages. We first show that relative population sizes of modern languages can be extrapolated to ancestral languages, albeit with diminishing accuracy, up to several thousand years into the past. We then test for an effect of population against the null hypothesis that the ultrametric inequality is satisified by lexical distances among triples of related languages. The test shows mainly negligible effects of population, the exception being an apparently faster rate of change in the larger of two closely related variants. A possible explanation for the exception may be the influence on emerging standard (or cross-regional) variants from speakers who shift from different dialects to the standard. Our results strongly indicate that the sizes of speaker populations do not in and of themselves determine rates of language change. Comparison of this empirical  finding with previously published computer simulations suggests that the most plausible model  for  language  change  is  one  in  which  changes  propagate  on  a  local level in a type of network in which the individuals have different degrees of connectivity.

As I’m in the middle of several other things at the moment I don’t really have time to provide a thorough review of this paper. Having said that, I agree with their claim of population size being unlikely to account for rates of language change. I reckon their results would be stronger if they factored in population density. So those that are dense and large will change faster than those which are large and distributed. The main point being that population size and population density influence the degree of social interconnectivity. Nettle (1999), for instance, argues that “spreading an innovation over a tribe of 500 people is much easier and takes much less time than spreading one over five million people.” This is fairly reasonable if we are looking at the generation of a single innovation within each of these populations. However, if those 500 people are spread across a large distance, then their transmission chain is going to be stretched: effectively lowering the rate of transmission. The same applies for a population of five million individuals who are packed into a small area: Arguably, given the right conditions, we can arrive at a situation where a population of five million show greater levels of interconnectivity than 500. I think it’s this aspect, the level of social interconnectivity, which may be more relevant to the rate of language change (other things to test for, include: writing systems/literacy and inter-language contact).

Language evolution in the laboratory

When talking about language evolution there’s always a resistance from people exclaiming;  ‘but how do we know?’, ‘surely all of this is conjecture!’ and, because of this, ‘what’s the point?’

Thomas Scott-Phillips and Simon Kirby have written a new article (in press) in ‘Trends in Cognitive Science’ which addresses some of the techniques currently used to address language evolution using experiments in the laboratory.

The Problem of language evolution

The problem of language evolution is one which encompasses not only the need to explain biologically how language came about but also how language came to be how it is today through processes of cultural evolution. Because of this potential ambiguity arises when using the term ‘language evolution’. To sort this ambiguity the authors put forward the following:

Language evolution researchers are interested in the processes that led to a qualitative change from a non-linguistic state to a linguistic one. In other words, language evolution is concerned with the emergence of language

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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.