Rapid word learning under uncertainty via cross-situational statistics

Psychol Sci. 2007 May;18(5):414-20. 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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Association Learning / physiology
  • Cues
  • Humans
  • Probability
  • Statistics as Topic / methods*
  • Students / psychology
  • Task Performance and Analysis
  • Time Factors
  • Uncertainty*
  • Verbal Learning / physiology*