The unrealized promise of infant statistical word–referent learning

Linda B. Smith, Sumarga H. Suanda, Chen Yu

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)


Recent theory and experiments offer a new solution regarding how infant learners may break into word learning by using cross-situational statistics to find the underlying word–referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word–referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics.
Original languageEnglish
Pages (from-to)251-258
JournalTrends in Cognitive Sciences
Issue number5
Publication statusPublished - May 2014
Externally publishedYes

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