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    Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

    Oaksford, Michael and Chater, N. (2014) Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning. Thinking & Reasoning 20 (2), pp. 269-295. ISSN 1354-6783.

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    Abstract

    In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data. LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs. connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the non-monotonicity of human reasoning.

    Metadata

    Item Type: Article
    Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis, available online at the link above.
    Keyword(s) / Subject(s): Non-monotonic reasoning, Conditionals, Probabilities, Minimal models, Local vs. global computation
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Depositing User: Administrator
    Date Deposited: 06 Nov 2014 11:17
    Last Modified: 31 May 2017 11:17
    URI: http://eprints.bbk.ac.uk/id/eprint/10921

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