title: The link between statistical segmentation and word learning in adults.
abstract: Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this possible link in adults by following a statistical segmentation exposure phase with an artificial lexicon learning phase. Participants were able to learn all novel object-label pairings, but pairings were learned faster when labels contained high probability (word-like) or non-occurring syllable transitions from the statistical segmentation phase than when they contained low probability (boundary-straddling) syllable transitions. This suggests that, for adults, labels inconsistent with expectations based on statistical learning are harder to learn than consistent or neutral labels. In contrast, a previous study found that infants learn consistent labels, but not inconsistent or neutral labels.
authors: Department of Psychology, University of Connecticut, 406 Babbidge Road, Unit 1020, Storrs, CT 06269-1020, USA; Haskins Laboratories, New Haven, CT 06511, USA. daniel.mirman@uconn.edu
authors: Daniel
authors: D
authors: Mirman
authors: James S
authors: JS
authors: Magnuson
authors: Katharine Graf
authors: KG
authors: Estes
authors: James A
authors: JA
authors: Dixon
mesh_terms: D000328:Adult; D003627:Data Interpretation, Statistical; D006801:Humans; D014706:Verbal Learning; D014825:Vocabulary