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DC Field | Value | Language |
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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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Fulltext.pdf | 55.03 kB | Adobe PDF | View/Open |
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