Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift

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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, background): “g: perhaps people are more likely to use rare words mistakenly in novel ways, a mechanism formalizable by Bayesian models of word learning and corresponding to the biological notion of genetic drift (Reali and Grifﬁths, 2010). Or perhaps a sociocultural conformity bias makes people less likely to ac”
Iterated learning and the evolution of language. (2014, background): “Subsequent mathematical analyses have begun to link these results to broader questions about cultural and biological evolution, exploring transmission in more complex populations [35, 36], the effects of the structure of the environment on the structure of language [37], formal relationships between”
Diffusion of Lexical Change in Social Media (2012, background): “A number of abstract models have been proposed for language change, including dynamical systems [18], Nash equilibria [19], Bayesian learners [20], agent-based simulations [21], and population genetics [22], among others.”
Three decades of cultural evolution in Savannah sparrow songs (2013, background): “Even weak biases that favour regularity in human language can give rise to strong systematic rules favouring one grammatical form rather than two (Kirby et al. 2007; Reali & Griffiths 2010).”