2007/05/01 by Chen Yu, Linda B. Smith · 12 citations
Computer Science · Psychology · #Child and Animal Learning Development #Language Development and Disorders #Speech and dialogue systems
paper · doi:10.1111/j.1467-9280.2007.01915.x
openalex publication_date 2007/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.