Language Evolution by Iterated Learning With Bayesian Agents

Abstract

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a… (More)
DOI: 10.1080/15326900701326576

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Cite this paper

@article{Griffiths2007LanguageEB, title={Language Evolution by Iterated Learning With Bayesian Agents}, author={Thomas L. Griffiths and Michael L. Kalish}, journal={Cognitive science}, year={2007}, volume={31 3}, pages={441-80} }