Iterated learning in populations of Bayesian agents

Abstract

Previous analytic results (Griffiths & Kalish, 2007) show that repeated learning and transmission of languages in populations of Bayesian learners results in distributions of languages which directly reflect the biases of learners. This result potentially has profound implications for our understanding of the link between the human language learning apparatus and the distribution of languages in the world. It is shown here that a variation on these models (such that learners learn from the linguistic behaviour of multiple individuals, rather than a single individual) changes this transparent relationship between learning bias and typology. This suggests that inferring learning bias from typology (or population behaviour from laboratory diffusion chains) is potentially unsafe.


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