Abstract

Most theories of word learning fall into one of two classes: hypothesis elimination or associationist. We propose a new approach to word learning within a Bayesian framework. Tenenbaum and Xu (2000) presented a Bayesian model of adults learning words for hierarchically structured categories. We report two experiments with 3- and 4-year-old children, providing evidence that the basic principles of Bayesian inference are employed when children acquire new words at different hierarchical levels. Implications for theories of word learning are discussed.

Citations

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