thank members of the UBC Baby Cognition Lab for their help with data collection, and Paul Bloom, Geoff Hall, and Terry Regier for helpful discussion. We owe a particular debt to Liz Bonawitz, for discussions and pilot work on an earlier version of this work.

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.

All students of cognitive development agree that the central questions in development are 1) specifying the initial state of a human infant, 2) specifying the final state of development for a human adult, and 3) specifying how to get from the initial state to the final state. Then academic disputes ensue. Cognitive developmental psychologists are roughly divided into two camps: those who are more or less nativists and those who are more or less empiricists. Some psychologists do not like these terms, and some alternatives are “those who believe in innate knowledge ” and “those who believe in learning, ” or “those who believed in initial conceptual knowledge ” and “those who believe in initial perceptual capabilities. ” This division is also correlated with whether a researcher believes in domain specificity or not: nativists tend to argue for domain-specific knowledge (even at the beginning of development) and domain-specific learning mechanisms; empiricists tend to argue for domain-general learning mechanisms that may result in domain-specific knowledge some years into development (for some representative explications of these views, see Carey &