Word learning as bayesian inference: evidence from preschoolers

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