Research Article Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning

ABSTRACT—Word learning is a ‘‘chicken and egg’ ’ prob-lem. If a child could understand speakers ’ utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers ’ intended meanings. To the beginning learner, however, both individual word meanings and speakers ’ intentions are unknown. We describe a com-putational model of word learning that solves these two inference problems in parallel, rather than relying exclu-sively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speak