Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift. Proc R Soc B. 277: 429-436

Department of Psychology, 3210 Tolman Hall, MC 1650, University of California at Berkeley, Berkeley, CA 94720-1650, USA.
Proceedings of the Royal Society B: Biological Sciences (Impact Factor: 5.05). 10/2009; 277(1680):429-36. DOI: 10.1098/rspb.2009.1513
Source: PubMed


Scientists studying how languages change over time often make an analogy between biological and cultural evolution, with words or grammars behaving like traits subject to natural selection. Recent work has exploited this analogy by using models of biological evolution to explain the properties of languages and other cultural artefacts. However, the mechanisms of biological and cultural evolution are very different: biological traits are passed between generations by genes, while languages and concepts are transmitted through learning. Here we show that these different mechanisms can have the same results, demonstrating that the transmission of frequency distributions over variants of linguistic forms by Bayesian learners is equivalent to the Wright-Fisher model of genetic drift. This simple learning mechanism thus provides a justification for the use of models of genetic drift in studying language evolution. In addition to providing an explicit connection between biological and cultural evolution, this allows us to define a 'neutral' model that indicates how languages can change in the absence of selection at the level of linguistic variants. We demonstrate that this neutral model can account for three phenomena: the s-shaped curve of language change, the distribution of word frequencies, and the relationship between word frequencies and extinction rates.

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    • "A model of undirected copying was used, for example, to identify social-network arrangements favorable to selection versus random drift in the sorting of variation (Lieberman et al. 2005), and it was also used to show how highly clustered social networks favor the evolution of cooperation (Ohtsuki et al. 2006). Practical applications, with favorable comparison to real-world data, include baby names (Hahn and Bentley 2003), English words (Bentley 2008; Reali and Griffiths 2009), and prehistoric pottery designs (Neiman 1995). "
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    ABSTRACT: We propose using a bi-axial map as a heuristic for categorizing different dynamics involved in the relationship between quality and popularity. The east–west axis represents the degree to which an agent’s decision is influenced by those of other agents. This ranges from the extreme western edge, where an agent learns individually (no outside influence), to the extreme eastern edge, where an agent is influenced by a large number of other agents. The vertical axis represents how easy or difficult it is for an agent to discern the relative quality of available choices. When a case study is located on the map, it becomes easier to select the range of tools to use for understanding and predicting the relation between quality and popularity. KeywordsCultural transmission–Evolution–Pattern formation–Popularity–Social learning
    Mind & Society 12/2011; 10(2):181-191. DOI:10.1007/s11299-011-0087-4
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    • "Although there were free parameters in the model used for this analysis, these conclusions were found to be independent over the range of values they could reasonably take (see [3] for full details). On the other hand, the quantitative support for neutral evolution provided by Reali and Griffiths [38], namely compatibility with wordfrequency distributions and lexical replacement rates, involved parameter fitting in both cases. According to Gotelli and McGill [24] parameter fitting is problematic because it biases the null hypothesis in favour of being accepted. "
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    ABSTRACT: We review the task of aligning simple models for language dynamics with relevant empirical data, motivated by the fact that this is rarely attempted in practice despite an abundance of abstract models. We propose that one way to meet this challenge is through the careful construction of null models. We argue in particular that rejection of a null model must have important consequences for theories about language dynamics if modelling is truly to be worthwhile. Our main claim is that the stochastic process of neutral evolution (also known as genetic drift or random copying) is a viable null model for language dynamics. We survey empirical evidence in favour and against neutral evolution as a mechanism behind historical language changes, highlighting the theoretical implications in each case.
    Advances in Complex Systems 08/2011; 15(3-4). DOI:10.1142/S0219525911003414 · 0.97 Impact Factor
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    ABSTRACT: Recent research on computational models of language change and cultural evolution in general has focused on the analytical study of languages as dynamic systems, thus avoiding the difficulties of analysing the complex multi-agent interactions underlying numerical simulations of cultural transmission. The same is true for the examination of the effects of inductive biases on language distributions within the Bayesian Iterated Learning Framework. The aim of this work is to test whether the strong results obtained through analytical methods in this framework also extend to finite populations of Bayesian learners, and to investigate what other effects richer population dynamics have on the results. Small world networks are introduced as a tool to model social structures which are shown to play an important role in the outcome of cultural transmission processes. The assumptions behind a Bayesian approach to language learning and its implications will be studied and compared to previous models of language change. While studying the effects of populations on convergence rates in the Bayesian model, the role of more complex population settings for the future of Iterated
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