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  What is learned about fragments in artificial grammar learning? A transitional probabilities approach

Poletiek, F. H., & Wolters, G. (2009). What is learned about fragments in artificial grammar learning? A transitional probabilities approach. Quarterly Journal of Experimental Psychology, 62(5), 868-876. doi:10.1080/17470210802511188.

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 Creators:
Poletiek, Fenna H.1, Author           
Wolters, Gezinus, Author
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1Department of Cognitive Psychology, Faculty of Social and Behavioural Sciences, Leiden University, NL, ou_persistent22              

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 Abstract: Learning local regularities in sequentially structured materials is typically assumed to be based on encoding of the frequencies of these regularities. We explore the view that transitional probabilities between elements of chunks, rather than frequencies of chunks, may be the primary factor in artificial grammar learning (AGL). The transitional probability model (TPM) that we propose is argued to provide an adaptive and parsimonious strategy for encoding local regularities in order to induce sequential structure from an input set of exemplars of the grammar. In a variant of the AGL procedure, in which participants estimated the frequencies of bigrams occurring in a set of exemplars they had been exposed to previously, participants were shown to be more sensitive to local transitional probability information than to mere pattern frequencies.

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Language(s): eng - English
 Dates: 2009
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000266301700005
DOI: 10.1080/17470210802511188
 Degree: -

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Title: Quarterly Journal of Experimental Psychology
Source Genre: Journal
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Publ. Info: Colchester, East Sussex, UK : Psychology Press
Pages: - Volume / Issue: 62 (5) Sequence Number: - Start / End Page: 868 - 876 Identifier: ISSN: 1747-0218
CoNE: https://pure.mpg.de/cone/journals/resource/954925255152