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

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Oaksford, M. (Mike) and Chater, Nick (2014) Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning. Thinking & Reasoning, Volume 20 (Number 2). pp. 269-295. doi:10.1080/13546783.2013.877401 ISSN 1354-6783.

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Official URL: http://dx.doi.org/10.1080/13546783.2013.877401

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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.

Item Type: Journal Article
Divisions: Faculty of Social Sciences > Warwick Business School > Behavioural Science
Faculty of Social Sciences > Warwick Business School
Journal or Publication Title: Thinking & Reasoning
Publisher: Psychology Press
ISSN: 1354-6783
Official Date: 31 January 2014
Dates:
DateEvent
31 January 2014Published
Volume: Volume 20
Number: Number 2
Page Range: pp. 269-295
DOI: 10.1080/13546783.2013.877401
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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