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Bayesian RationalityThe probabilistic approach to human reasoning$
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Mike Oaksford and Nick Chater

Print publication date: 2007

Print ISBN-13: 9780198524496

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780198524496.001.0001

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An uncertain quantity: how people reason with syllogisms

An uncertain quantity: how people reason with syllogisms

Chapter:
(p.213) Chapter 7 An uncertain quantity: how people reason with syllogisms
Source:
Bayesian Rationality
Author(s):

Mike Oaksford

Nick Chater

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198524496.003.0007

Chater and Oaksford developed both computational- and algorithmic-level analyses of syllogistic reasoning, based on a probabilistic approach. The resulting model was called the probability heuristics model (PHM). This chapter first outlines PHM, then shows how it can account for the existing data, and compares it with alternative theories. Finally, it looks at the empirical results that have emerged since the model’s appearance and addresses some of the arguments that have been levelled against it. An important feature of PHM is that it extends directly to syllogisms involving generalized quantifiers such as most and few. The crucial feature of these syllogisms is that they cannot be explained logically, and hence they fall outside the scope of theories like mental logic and mental models that assume standard logic as their computational-level theory.

Keywords:   syllogistic reasoning, probability heuristics model, syllogisms

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