What Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning

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TitreWhat Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning
Type de publicationJournal Article
Year of Publication2019
AuteursPerruchet P
JournalTOPICS IN COGNITIVE SCIENCE
Volume11
Pagination520-535
Date PublishedJUL
Type of ArticleArticle
ISSN1756-8757
Mots-clésBayesian inference, Chunk, Computation, Computational modeling, Implicit learning, segmentation, Statistical learning, Transitional probability
Résumé

In a prior review, Perrruchet and Pacton (2006) noted that the literature on implicit learning and the more recent studies on statistical learning focused on the same phenomena, namely the domain-general learning mechanisms acting in incidental, unsupervised learning situations. However, they also noted that implicit learning and statistical learning research favored different interpretations, focusing on the selection of chunks and the computation of transitional probabilities aimed at discovering chunk boundaries, respectively. This paper examines the state of the debate 12 years later. The link between contrasting theories and their historical roots has disappeared, but a number of studies were aimed at contrasting the predictions of these two approaches. Overall, these studies strongly question the still prevalent account based on the statistical computation of pairwise associations. Various chunk-based models provide much better predictions in a number of experimental situations. However, these models rely on very different conceptual frameworks, as illustrated by a comparison between Bayesian models of word segmentation, PARSER, and a connectionist model (TRACX).

DOI10.1111/tops.12403