Research Article Rapid Word Learning Under Uncertainty via Cross-Situational Statistics

ABSTRACT—There are an infinite number of possible word-to-word pairings in naturalistic learning environments. Previous proposals to solve this mapping problem have focused on linguistic, social, representational, and at-tentional constraints at a single moment. This article dis-cusses a cross-situational learning strategy based on computing distributional statistics across words, across referents, and, most important, across the co-occurrences of words and referents at multiple moments. We briefly exposed adults to a set of trials that each contained mul-tiple spoken words and multiple pictures of individual objects; no information about word-picture correspon-dences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning condi