Theories of artificial grammar learning.

Full Abstract

Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together with relevant human experimental and neuroscience data. The author concludes that satisfactory understanding of AGL requires (a) an understanding of implicit knowledge as knowledge that is not consciously activated at the time of a cognitive operation; this could be because the corresponding representations are impoverished or they cannot be concurrently supported in working memory with other representations or operations, and (b) adopting a frequency-independent view of rule knowledge and contrasting rule knowledge with specific similarity and associative learning (co-occurrence) knowledge.

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Author information

Author/s: Pothos, Emmanuel M (EM);

Affiliation: Department of Psychology, Swansea University, Swansea, United Kingdom. e.m.pothos(-atsign-)swansea.ac.uk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Psychological bulletin (Psychol Bull), published in United States. (Language: eng)

Reference: 2007-Mar; vol 133 (issue 2) : pp 227-44

Dates: Created 2007/03/06; Completed 2007/05/24;

PMID: 17338598, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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