title: 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 attentional constraints at a single moment. This article discusses 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 multiple spoken words and multiple pictures of individual objects; no information about word-picture correspondences was given within a trial. Nonetheless, over trials, subjects learned the word-picture mappings through cross-trial statistical relations. Different learning conditions varied the degree of within-trial reference uncertainty, the number of trials, and the length of trials. Overall, the remarkable performance of learners in various learning conditions suggests that they calculate cross-trial statistics with sufficient fidelity and by doing so rapidly learn word-referent pairs even in highly ambiguous learning contexts.
authors.affiliation: Department of Psychological and Brain Sciences and Program in Cognitive Science, Indiana University, IN 47405, USA. chenyu@indiana.edu
authors.forename: Chen
authors.initials: C
authors.lastname: Yu
authors.forename: Linda B
authors.initials: LB
authors.lastname: Smith
mesh_terms: D001245:Association Learning / Q000502:physiology; D003463:Cues; D006801:Humans; D011336:Probability; D013223:Statistics as Topic / Q000379:methods*; D013334:Students / Q000523:psychology; D013647:Task Performance and Analysis; D013997:Time Factors; D035501:Uncertainty; D014706:Verbal Learning / Q000502:physiology*