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Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning

Published online by Cambridge University Press:  12 February 2009

Mike Oaksford  and
Nick Chater
Mike Oaksford
Affiliation:
School of Psychology, Birkbeck College London, London, WC1E 7HX, United [email protected]/psyc/staff/academic/moaksford
Nick Chater
Affiliation:
Division of Psychology and Language Sciences, and ESRC Centre for Economic Learning and Social Evolution, University College London, London, WC1E 6BT, United [email protected]/people/profiles/chater_nick.htm
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Abstract

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards.

Bayesian Rationality argues that rationality is defined instead by the ability to reason about uncertainty. Although people are typically poor at numerical reasoning about probability, human thought is sensitive to subtle patterns of qualitative Bayesian, probabilistic reasoning. In Chapters 1–4 of Bayesian Rationality (Oaksford & Chater 2007), the case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems. In Chapters 5–7 the psychology of “deductive” reasoning is tackled head-on: It is argued that purportedly “logical” reasoning problems, revealing apparently irrational behaviour, are better understood from a probabilistic point of view. Data from conditional reasoning, Wason's selection task, and syllogistic inference are captured by recasting these problems probabilistically. The probabilistic approach makes a variety of novel predictions which have been experimentally confirmed. The book considers the implications of this work, and the wider “probabilistic turn” in cognitive science and artificial intelligence, for understanding human rationality.

Keywords

Bayes' theoremconditional inferencelogicnon-monotonic reasoningprobabilityrational analysisrationalityreasoningselection tasksyllogisms
Type
Main Articles
Information
Behavioral and Brain Sciences , Volume 32 , Issue 1 , February 2009 , pp. 69 - 84
Copyright
Copyright © Cambridge University Press 2009

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