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    The probability heuristics model of syllogistic reasoning

    Chater, N. and Oaksford, Michael (1999) The probability heuristics model of syllogistic reasoning. Cognitive Psychology 38 (2), pp. 191-258. ISSN 0010-0285.

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    Abstract

    A probability heuristic model (PHM) for syllogistic reasoning is proposed. An informational ordering over quantified statements suggests simple probability based heuristics for syllogistic reasoning. The most important is the “min-heuristic”: choose the type of the least informative premise as the type of the conclusion. The rationality of this heuristic is confirmed by an analysis of the probabilistic validity of syllogistic reasoning which treats logical inference as a limiting case of probabilistic inference. A meta-analysis of past experiments reveals close fits with PHM. PHM also compares favorably with alternative accounts, including mental logics, mental models, and deduction as verbal reasoning. Crucially, PHM extends naturally to generalized quantifiers, such asMostandFew,which have not been characterized logically and are, consequently, beyond the scope of current mental logic and mental model theories. Two experiments confirm the novel predictions of PHM when generalized quantifiers are used in syllogistic arguments. PHM suggests that syllogistic reasoning performance may be determined by simple but rational informational strategies justified by probability theory rather than by logic.

    Metadata

    Item Type: Article
    School: School of Science > Psychological Sciences
    Depositing User: Administrator
    Date Deposited: 20 Sep 2016 16:20
    Last Modified: 20 Sep 2016 16:20
    URI: https://eprints.bbk.ac.uk/id/eprint/16137

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