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A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal
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Culbertson, Jennifer; Smolensky, Paul – Cognitive Science, 2012
In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…
Descriptors: Models, Bayesian Statistics, Artificial Languages, Language Acquisition
Substantive Learning Bias or an Effect of Familiarity? Comment on Culbertson, Smolensky, and Legendre (2012)
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Goldberg, Adele E. – Cognition, 2013
Typologists have long observed that there are certain distributional patterns that are not evenly distributed among the world's languages. This discussion note revisits a recent experimental investigation of one such intriguing case, so-called "universal 18", by Culbertson, Smolensky, and Legendre (2012). The authors find that adult learners are…
Descriptors: Language Classification, Adult Students, Grammar, Artificial Languages