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Nested sets theory, full stop: Explaining performance on Bayesian inference tasks without dual-systems assumptions1

Published online by Cambridge University Press:  29 October 2007

David R. Mandel
David R. Mandel
Affiliation:
Defence Research and Development Canada (Toronto), Toronto, ON M3M 3B9, Canada. david.mandel@drdc-rddc.gc.cahttp://mandel.socialpsychology.org/
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Abstract

Consistent with Barbey & Sloman (B&S), it is proposed that performance on Bayesian inference tasks is well explained by nested sets theory (NST). However, contrary to those authors' view, it is proposed that NST does better by dispelling with dual-systems assumptions. This article examines why, and sketches out a series of NST's core principles, which were not previously defined.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 30 , Issue 3 , June 2007 , pp. 275 - 276
Copyright
Copyright © Cambridge University Press 2007

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