Generalization of dimension-based statistical learning

Abstract

Recent research demonstrates that the relationship between an acoustic dimension and speech categories is not static. Rather, it is influenced by the evolving distribution of dimensional regularity experienced across time, and specific to experienced individual sounds. Three studies examine the nature of this perceptual, dimension-based statistical learning of artificially accented [b] and [p] speech categories in online word recognition by testing generalization of learning across contexts, and testing the effect of a larger word list across which learning is induced. The results indicate that whereas learning of accented [b] and [p] generalizes across contexts, generalization to contexts not experienced in the accent is weaker even for the same speech categories [b] and [p] spoken by the same speaker. The results support a rich model of speech representation that is sensitive to context-dependent variation in the way the acoustic dimensions are related to speech categories.