Fine-grained sensitivity to statistical information in adult word learning

A language learner trying to acquire a new word must often sift through many potential relations between particular words and their possible meanings. In principle, statistical information about the distribution of those mappings could serve as one important source of data, but little is known about whether learners can in fact track multiple word–referent mappings, and, if they do, the precision with which they can represent those statistics. To test this, two experiments contrasted a pair of possibilities: that learners encode the fine-grained statistics of mappings in the input – both high- and low-frequency mappings – or, alternatively, that only high frequency mappings are represented. Participants were briefly trained on novel word–novel object pairs combined with varying frequencies: some objects were paired with one word, other objects with multiple words with differing frequencies (ranging from 10% to 80%). Results showed that participants were exquisitely sensitive to very small statistical differences in mappings. The second experiment showed that word learners’ representation of low frequency mappings is modulated as a function of the variability in the environment. Implications for Mutual Exclusivity and Bayesian accounts of word learning are discussed.