ERIC Number: EJ766468
Record Type: Journal
Publication Date: 2007-Apr
Word Learning as Bayesian Inference
Xu, Fei; Tenenbaum, Joshua B.
Psychological Review, v114 n2 p245-272 Apr 2007
The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.
Descriptors: Prior Learning, Inferences, Associative Learning, Vocabulary Development, Bayesian Statistics, Logical Thinking, Semantics
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
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