What Mechanisms Underlie Implicit Statistical Learning? Transitional Probabilities Versus Chunks in Language Learning
| Autor: | Pierre Perruchet |
|---|---|
| Přispěvatelé: | Laboratoire d'Etude de l'Apprentissage et du Développement [Dijon] (LEAD), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2018 |
| Předmět: |
Linguistics and Language
Implicit learning Transitional probability Computer science Cognitive Neuroscience Bayesian inference Experimental and Cognitive Psychology 050105 experimental psychology 03 medical and health sciences Segmentation 0302 clinical medicine Connectionism Artificial Intelligence Selection (linguistics) Humans 0501 psychology and cognitive sciences Language 05 social sciences Text segmentation Computational modeling Models Theoretical Chunk Language acquisition Statistical learning Human-Computer Interaction Computation [SCCO.PSYC]Cognitive science/Psychology Unsupervised learning Pairwise comparison Probability Learning 030217 neurology & neurosurgery Cognitive psychology |
| Zdroj: | Topics in cognitive science Topics in cognitive science, Wiley, 2019, 11 (3), pp.520-535. ⟨10.1111/tops.12403⟩ |
| ISSN: | 1756-8757 1756-8765 |
| DOI: | 10.1111/tops.12403 |
| Popis: | International audience; 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text