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    Restructuring partitioned knowledge: The role of recoordination in category learning

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    Author
    Sewell, DK; Lewandowsky, S
    Date
    2011-03-01
    Source Title
    COGNITIVE PSYCHOLOGY
    Publisher
    ACADEMIC PRESS INC ELSEVIER SCIENCE
    University of Melbourne Author/s
    Sewell, David
    Affiliation
    Melbourne School Of Psychological Sciences
    Metadata
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    Document Type
    Journal Article
    Citations
    Sewell, D. K. & Lewandowsky, S. (2011). Restructuring partitioned knowledge: The role of recoordination in category learning. COGNITIVE PSYCHOLOGY, 62 (2), pp.81-122. https://doi.org/10.1016/j.cogpsych.2010.09.003.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/31533
    DOI
    10.1016/j.cogpsych.2010.09.003
    Description

    C1 - Journal Articles Refereed

    Abstract
    Knowledge restructuring refers to changes in the strategy with which people solve a given problem. Two types of knowledge restructuring are supported by existing category learning models. The first is a relearning process, which involves incremental updating of knowledge as learning progresses. The second is a recoordination process, which involves novel changes in the way existing knowledge is applied to the task. Whereas relearning is supported by both single- and multiple-module models of category learning, only multiple-module models support recoordination. To date, only relearning has been directly supported empirically. We report two category learning experiments that provide direct evidence of recoordination. People can fluidly alternate between different categorization strategies, and moreover, can reinstate an old strategy even after prolonged use of an alternative. The knowledge restructuring data are not well fit by a single-module model (ALCOVE). By contrast, a multiple-module model (ATRIUM) quantitatively accounts for recoordination. Low-level changes in the distribution of dimensional attention are shown to subsequently affect how ATRIUM coordinates its modular knowledge. We argue that learning about complex tasks occurs at the level of the partial knowledge elements used to generate a response strategy.
    Keywords
    Computer Perception; Memory and Attention; Decision Making; Knowledge Representation and Machine Learning; Expanding Knowledge in Psychology and Cognitive Sciences

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