Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

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

Abstract : 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).
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02268114
Contributeur : Lead - Université de Bourgogne <>
Soumis le : mardi 20 août 2019 - 13:24:36
Dernière modification le : mercredi 21 août 2019 - 01:07:23

Identifiants

Collections

Citation

Pierre Perruchet. What Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning. Topics in cognitive science, Wiley, 2019, 11 (3), pp.520-535. ⟨10.1111/tops.12403⟩. ⟨hal-02268114⟩

Partager

Métriques

Consultations de la notice

61