The role of similarity in artificial grammar learning

Pothos, Emmanuel M. and Bailey, Todd M. 2000. The role of similarity in artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 26 (4) , pp. 847-862. 10.1037//0278-7393.26.4.847

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Abstract

The authors examine the role of similarity in artificial grammar learning (AGL; A. S. Reber, 1989). A standard finite-state language was used to create stimuli that were arrangements of embedded geometric shapes (Experiment 1), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chunk strength, length of items, and edit distance) were observed, thus replicating previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the generalized context model; R. M. Nosofsky, 1989) to fit grammaticality judgments, by nested regression analyses. The results suggest that any explanation of AGL that is based on the existing theories is incomplete without a similarity process as well. Also, the results provide a foundation for further interpreting AGL in the wider context of categorization research.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Publisher:	American Psychological Association
ISSN:	0278-7393
Last Modified:	06 May 2021 01:26
URI:	https://orca.cardiff.ac.uk/id/eprint/11461

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