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Approximating Bayesian inference through internal sampling
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Sundh, Joakim, Sanborn, Adam N., Zhu, Jian-Qiao, Spicer, Jake, León-Villagrá, Pablo and Chater, Nick (2023) Approximating Bayesian inference through internal sampling. In: Fiedler, Klaus and Juslin, Peter and Denrell, Jerker, (eds.) Sampling in Judgment and Decision Making. Cambridge, United Kingdom: Cambridge University Press, pp. 490-512. ISBN 9781009002042
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Official URL: https://www.cambridge.org/core/books/sampling-in-j...
Abstract
People must often make inferences about, and decisions concerning, a highly complex and unpredictable world, on the basis of sparse evidence. An “ideal” normative approach to such challenges is often modeled in terms of Bayesian probabilistic inference. But for real-world problems of perception, motor control, categorization, language comprehension, or common-sense reasoning, exact probabilistic calculations are computationally intractable. Instead, we suggest that the brain solves these hard probability problems approximately, by considering one, or a few, samples from the relevant distributions. By virtue of being an approximation, the sampling approach inevitably leads to systematic biases. Thus, if we assume that the brain carries over the same sampling approach to easy probability problems, where the “ideal” solution can readily be calculated, then a brain designed for probabilistic inference should be expected to display characteristic errors. We argue that many of the “heuristics and biases” found in human judgment and decision-making research can be reinterpreted as side effects of the sampling approach to probabilistic reasoning.
Item Type: | Book Item | ||||||||
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Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics |
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Divisions: | Faculty of Social Sciences > Warwick Business School > Behavioural Science Faculty of Science, Engineering and Medicine > Science > Psychology Faculty of Social Sciences > Warwick Business School |
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Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Cognitive science, Sampling (Statistics), Reason, Cognition, Selectivity (Psychology) | ||||||||
Publisher: | Cambridge University Press | ||||||||
Place of Publication: | Cambridge, United Kingdom | ||||||||
ISBN: | 9781009002042 | ||||||||
Book Title: | Sampling in Judgment and Decision Making | ||||||||
Editor: | Fiedler, Klaus and Juslin, Peter and Denrell, Jerker | ||||||||
Official Date: | June 2023 | ||||||||
Dates: |
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Page Range: | pp. 490-512 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | This material has been published in Sampling in Judgment and Decision Making edited by Klaus Fiedler, Peter Juslin and Jerker Denrell. This version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University Press | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 5 January 2022 | ||||||||
Date of first compliant Open Access: | 28 August 2023 | ||||||||
RIOXX Funder/Project Grant: |
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