On the basis of Sean’s comment, about using a regression to look at how phoneme inventory size improved as geographic spread was incorporated along with population size, I decided to look at the stats a bit more closely (original post is here). It’s fairly easy to perform multiple regression in R, which, in the case of my data, resulted in highly significant results (p<0.001) for the intercept, area and population (residual standard error = 9.633 on 393 degrees of freedom; adjusted R-Squared = 0.1084). I then plotted all the combinations as scatterplots for each pair of variables. As you can see below, this is fairly useful as a quick summary but it is also messy and confusing. Another problem is that the pairs plot is on the original data and not the linear model.
Experimental studies (e.g. Jones & Munhall 2000) indicate that humans monitor their own speech through hearing in order to maintain accurate vocal articulation throughout the lifespan. Similarly, songbirds not only rely on song input from tutors and conspecifics in the early stages of song development, but also on the ability to hear and detect production errors in their own song and adjust it accordingly with reference to an internal ‘sensory target’ following the initial song learning phase.
This phenomenon also extends to ‘closed-ended learners’ – birds who do not acquire novel song elements after an initial learning period, but who still demonstrate song variability in adulthood. Experimental studies have shown that in such species, vocal learning is more prolonged and fundamental to song production than originally thought. For example, Okanoya and Yamaguchi (1997) showed that afflicted deafening in adult Bengalese Finches resulted in the production of abnormal song syntax in a matter of days. This is parallel to the human condition whereby linguistic fidelity, particularly with regards to prosodic aspects such as pitch and intensity, gradually degrades in human adults with postlinguistically acquired auditory impairments.
There have been some very interesting discussions of the relationship between language and thought recently, including for example, Sean’s absolutely fascinating series of posts about the evolution of colour terms, a great post on descriptions of motion in different languages over at the lousy linguist (here), Guy Deutscher’s article “Does Your Language Shape How You Think?” (for discussions, see e.h. here and here), a slightly less recent piece by Lera Boroditsky in the Wall Street Journal, and an excellent recent discussion of her article by Mark Liberman (here). (see also James’ post, including a great/terrible joke about Whorf).
One of the things that Deutscher wrote in his article was that:
“The area where the most striking evidence for the influence of language on thought has come to light is the language of space — how we describe the orientation of the world around us.”
As I’ve written a bit about this topic on my other blog, Shared Symbolic Storage, I’ll repost a short series of posts over the next couple of days.
As Deutscher said, this is a very fascinating avenue of linguistic research that gives much insight into the nature of language and cognition as well as their relationship. In addition, it also presents us with new facts and considerations we have to take into account when we think about how language and cognition evolved.
It’s long since been established that demography drives evolutionary processes (see Hawks, 2008 for a good overview). Similar attempts are also being made to describe cultural (Shennan, 2000; Henrich, 2004; Richerson & Boyd, 2009) and linguistic (Nettle, 1999a; Wichmann & Homan, 2009; Vogt, 2009) processes by considering the effects of population size and other demographic variables. Even though these ideas are hardly new, until recently, there was a ceiling as to the amount of resources one person could draw upon. In linguistics, this paucity of data is being remedied through the implementation of large-scale projects, such as WALS, Ethnologue and UPSID, that bring together a vast body of linguistic fieldwork from around the world. Providing a solid direction for how this might be utilised is a recent study by Lupyan & Dale (2010). Here, the authors compare the structural properties of more than 2000 languages with three demographic variables: a language’s speaker population, its geographic spread and the number of linguistic neighbours. The salient point being that certain differences in structural features correspond to the underlying demographic conditions.
With that said, a few months ago I found myself wondering about a particular feature, the phoneme inventory size, and its potential relationship to underlying demographic conditions of a speech community. What piqued my interest was that two languages I retain a passing interest in, Kayardild and Pirahã, both contain small phonological inventories and have small speaker communities. The question being: is their a correlation between the population size of a language and its number of phonemes? Despite work suggesting at such a relationship (e.g. Trudgill, 2004), there is little in the way of empirical evidence to support such claims. Hay & Bauer (2007) perhaps represent the most comprehensive attempt at an investigation: reporting a statistical correlation between the number of speakers of a language and its phoneme inventory size.
In it, the authors provide some evidence for the claim that the more speakers a language has, the larger its phoneme inventory. Without going into the sub-divisions of vowels (e.g. separating monophthongs, extra monophtongs and diphthongs) and consonants (e.g. obstruents), as it would extend the post by about 1000 words, the vowel inventory and consonant inventory are both correlated with population size (also ruling out that language families are driving the results). As they note:
That vowel inventory and consonant inventory are both correlated with population size is quite remarkable. This is especially so because consonant inventory and vowel inventory do not correlate with one another at all in this data-set (rho=.01, p=.86). Maddieson (2005) also reports that there is no correlation between vowel and consonant inventory size in his sample of 559 languages. Despite the fact that there is no link between vowel inventory and consonant inventory size, both are significantly correlated with the size of the population of speakers.
Using their paper as a springboard, I decided to look at how other demographic factors might influence the size of the phoneme inventory, namely: population density and the degree of social interconnectedness.
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”
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).