The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments.
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| Title: | The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments. |
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| Authors: | Zhu, Jian-Qiao1 (AUTHOR) Jianqiao.Zhu@warwick.ac.uk, Sanborn, Adam N.1 (AUTHOR), Chater, Nick2 (AUTHOR) |
| Source: | Psychological Review. Oct2020, Vol. 127 Issue 5, p719-748. 30p. |
| Subject Terms: | Probability theory, Samplers, Conditional probability, Forecasting |
| Abstract: | Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of "noise" in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample. [ABSTRACT FROM AUTHOR] |
| Copyright of Psychological Review is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 146494115 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Jian-Qiao%22">Zhu, Jian-Qiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Jianqiao.Zhu@warwick.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Sanborn%2C+Adam+N%2E%22">Sanborn, Adam N.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chater%2C+Nick%22">Chater, Nick</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Psychological+Review%22">Psychological Review</searchLink>. Oct2020, Vol. 127 Issue 5, p719-748. 30p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Samplers%22">Samplers</searchLink><br /><searchLink fieldCode="DE" term="%22Conditional+probability%22">Conditional probability</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of "noise" in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Psychological Review is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1037/rev0000190 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 719 Subjects: – SubjectFull: Probability theory Type: general – SubjectFull: Samplers Type: general – SubjectFull: Conditional probability Type: general – SubjectFull: Forecasting Type: general Titles: – TitleFull: The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhu, Jian-Qiao – PersonEntity: Name: NameFull: Sanborn, Adam N. – PersonEntity: Name: NameFull: Chater, Nick IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 0033295X Numbering: – Type: volume Value: 127 – Type: issue Value: 5 Titles: – TitleFull: Psychological Review Type: main |
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