change – Page 4 – Replicated Typo

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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Culture-driven population dynamics: sustainable or unsustainable?

When looking at culture-driven population dynamics, a common assumption is that there’s a positive feedback between cultural evolution and demographic growth. The general prediction, then, is for unlimited growth in population and culture. Yet models based on these assumptions tend to ignore important aspects of cultural evolution, namely: (1) cultural transmission is not perfect; (2) culture does not always promote population growth. Ghirlanda et al (2010) incorporate these two features into a model, and arrive at some interesting conclusions. In particular, they argue those populations maintaining large amounts of culture may run the risk of extinction rather than stability or growth.

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Bayesian Bilingualism

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.

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Learning Multiple languages from Multiple teachers

As Niyogi & Berwick (2009) point out, there is a tendency in modelling of Linguistic Evolution to assume chains of single learners inheriting single grammars from single teachers. This is, of course, not realistic – we learn language from many people and people can speak more than one language. However, Niyogi & Berwick suggest deeper objections.

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Language – An Embarrassing Conundrum for the Evolutionist?

Hello! This is my first post on the blog and whilst I didn’t want it to be an angry rant after I found this youtube video there seemed little could have been done to avoid it.

This is a video by a creationist named “ppsimmons” who writes on the front page of his youtube channel that he “apologizes for not knowing enough to scientifically refute the evidence for creation nor for being clever enough to “scientifically” support the theory of evolution.” And yet he feels to be enough of an authority to make videos refuting evolution using ‘science’.

I know I shouldn’t let this annoy me as much as it obviously has, I know that there will always be creationists out there and I know that these creationists will never listen to anything I have to say. However, in this case, I’ve decided to respond mostly to set straight the interpretation of Robert Berwick’s words used in this video.

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A history of evolution pt.1: Ancient Greece to Lamarck

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.

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Words as alleles: A null-model for language evolution?

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?

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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:

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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Can linguistic features reveal time depths as deep as 50,000 years ago?

Throughout much of our history language was transitory, existing only briefly within its speech community. The invention of writing systems heralded a way of recording some of its recent history, but for the most part linguists lack the stone tools archaeologists use to explore the early history of ancient technological industries. The question of how far back we can trace the history of languages is therefore an immensely important, and highly difficult, one to answer. However, it’s not impossible. Like biologists, who use highly conserved genes to probe the deepest branches on the tree of life, some linguists argue that highly stable linguistic features hold the promise of tracing ancestral relations between the world’s languages.

Previous attempts using cognates to infer the relatedness between languages are generally limited to predictions within the last 6000-10,000 years. In the present study, Greenhill et al (2010) decided to examine more stable linguistic features than the lexicon, arguing:

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