Statistical learning is the process of identifying patterns of probabilistic co-occurrence among stimulus features, essential to our ability to perceive the world as predictable and stable. Research on auditory statistical learning has revealed that infants use statistical properties of linguistic input to discover structure--including sound patterns, words, and the beginnings of grammar--that may facilitate language acquisition. Research on visual statistical learning has revealed abilities to discriminate, learn, and generalize probabilities in visual patterns, but the mechanisms (including developmental mechanisms) underlying infant performance remain unclear. This talk will present new work that examines competing models of statistical learning and how learning might be constrained by limits in infants' attention, perception, and memory. Broader implications for theories of cognitive development will be discussed.
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