Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2125
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dc.contributor.authorLane, PCR-
dc.contributor.authorGobet, F-
dc.contributor.authorCheng, PCH-
dc.coverage.spatial6en
dc.date.accessioned2008-05-01T13:10:41Z-
dc.date.available2008-05-01T13:10:41Z-
dc.date.issued2000-
dc.identifier.citationLane, P. C. R., Gobet, F. , & Cheng, P. C-H. (2000). Learning-based constraints on schemata. Proceedings of the 22nd Meeting of the Cognitive Science Society, pp. 776-781. Mahwah, NJ: Erlbaum.en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/2125-
dc.description.abstractSchemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models.en
dc.format.extent232939 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherErlbaumen
dc.subjectschemaen
dc.subjectknowledgeen
dc.subjectconstraintsen
dc.subjectlearningen
dc.subjectneural networksen
dc.subjectproduction systemsen
dc.subjectchunking networksen
dc.subjectCHRESTen
dc.titleLearning-based constraints on schemataen
dc.typeResearch Paperen
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

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