Rapid Word Learning under Uncertainty via Cross-Situational Statistics

• Published in: Psychological science Vol. 18; no. 5; pp. 414 - 420
• Main Authors: Yu, Chen, Smith, Linda B.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA Blackwell Publishing 01.05.2007; SAGE Publications; SAGE PUBLICATIONS, INC
• Subjects: Adult education; Adults; Ambiguity; Association Learning - physiology; Collective representation; Cues; Educational environment; Experimentation; Fidelity; Grammar; Humans; Learning; Learning environment; Learning Processes; Learning rate; Learning Strategies; Lexicon; Linguistics; Mapping; Probability; Psychology; Psychology of learning; Referents; Social representations; Spoken language; Statistical Analysis; Statistics; Statistics as Topic - methods; Students - psychology; Task Performance and Analysis; Time Factors; Training; Uncertainty; Verbal Learning; Verbal Learning - physiology; Visual Media; Vocabulary learning; Words
• ISSN: 0956-7976; 1467-9280
• DOI: 10.1111/j.1467-9280.2007.01915.x

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.