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    Probabilities and polarity biases in conditional inference

    Oaksford, Michael and Chater, N. and Larkin, J. (2000) Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology: Learning, Memory, and Cognition 26 (4), pp. 883-899. ISSN 0278-7393.

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    Abstract

    A probabilistic computational level model of conditional inference is proposed that can explain polarity biases in conditional inference (e.g., J. St.B. T. Evans, 1993). These biases are observed when J. St.B. T. Evans's (1972) negations paradigm is used in the conditional inference task. The model assumes that negations define higher probability categories than their affirmative counterparts (M. Oaksford & K. Stenning, 1992); for example, P(not-dog) > P(dog). This identification suggests that polarity biases are really a rational effect of high-probability categories. Three experiments revealed that, consistent with this probabilistic account, when high-probability categories are used instead of negations, a high-probability conclusion effect is observed. The relationships between the probabilistic model and other phenomena and other theories in conditional reasoning are discussed.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Depositing User: Administrator
    Date Deposited: 15 Sep 2016 17:01
    Last Modified: 15 Sep 2016 17:01
    URI: http://eprints.bbk.ac.uk/id/eprint/16073

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