The Probabilistic Analysis of Language Acquisition: Theoretical, Computational, and Experimental Analysis
Cognition, in Press
27 Pages Posted: 4 Apr 2011
Date Written: March 31, 2011
There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.
Keywords: child language acquisition, poverty of the stimulus, no negative evidence, minimum description length, simplicity principle, natural language, probabilistic models, identification in the limit, Bayesian models
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