What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/9157

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DC Field	Value	Language
dc.contributor.author	Lane, PCR	-
dc.contributor.author	Gobet, F	-
dc.contributor.author	Cheng, PCH	-
dc.date.accessioned	2014-09-25T13:23:24Z	-
dc.date.available	2014-09-25T13:23:24Z	-
dc.date.issued	2001	-
dc.identifier.citation	Behavioural and Brain Sciences, 24 (1): 128-129, Feb 2001	en_US
dc.identifier.issn	0140-525X	-
dc.identifier.uri	http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=84511&fileId=S0140525X01363926	en
dc.identifier.uri	http://bura.brunel.ac.uk/handle/2438/9157	-
dc.description.abstract	Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.	en_US
dc.language.iso	en	en_US
dc.publisher	Cambridge University Press	en_US
dc.subject	Computational modeling	en_US
dc.subject	Learning	en_US
dc.subject	Chunking	en_US
dc.subject	CHREST	en_US
dc.subject	Magical number	en_US
dc.subject	Short-term memory (STM)	en_US
dc.subject	Visual short-term memory	en_US
dc.subject	Cowan	en_US
dc.title	What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning	en_US
dc.type	Research Paper	en_US
dc.identifier.doi	tp://dx.doi.org/10.1017/S0140525X01363926	-
Appears in Collections:	Psychology Dept of Life Sciences Research Papers

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