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      Rapid word learning under uncertainty via cross-situational statistics.

      1 ,
      Psychological science
      Wiley

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          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.

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          Author and article information

          Journal
          Psychol Sci
          Psychological science
          Wiley
          0956-7976
          0956-7976
          May 2007
          : 18
          : 5
          Affiliations
          [1 ] Department of Psychological and Brain Sciences and Program in Cognitive Science, Indiana University, IN 47405, USA. chenyu@indiana.edu
          Article
          PSCI1915
          10.1111/j.1467-9280.2007.01915.x
          17576281
          f4c15fae-f065-4a5e-a782-f02a8d5f7c07
          History

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