Bayesian Brains without Probabilities.
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| Title: | Bayesian Brains without Probabilities. |
|---|---|
| Authors: | Sanborn, Adam N.1 a.n.sanborn@warwick.ac.uk, Chater, Nick2 |
| Source: | Trends in Cognitive Sciences. Dec2016, Vol. 20 Issue 12, p883-893. 11p. |
| Subjects: | Bayesian analysis, Probability theory, Neurosciences, Nervous system, Neurophysiologic monitoring, Base rate fallacies |
| Abstract: | Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy. [ABSTRACT FROM AUTHOR] |
| Copyright of Trends in Cognitive Sciences is the property of Elsevier B.V. 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 119511033 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian Brains without Probabilities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sanborn%2C+Adam+N%2E%22">Sanborn, Adam N.</searchLink><relatesTo>1</relatesTo><i> a.n.sanborn@warwick.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Chater%2C+Nick%22">Chater, Nick</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Trends+in+Cognitive+Sciences%22">Trends in Cognitive Sciences</searchLink>. Dec2016, Vol. 20 Issue 12, p883-893. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Nervous+system%22">Nervous system</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiologic+monitoring%22">Neurophysiologic monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Base+rate+fallacies%22">Base rate fallacies</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Trends in Cognitive Sciences is the property of Elsevier B.V. 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: 10.1016/j.tics.2016.10.003 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 883 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Neurosciences Type: general – SubjectFull: Nervous system Type: general – SubjectFull: Neurophysiologic monitoring Type: general – SubjectFull: Base rate fallacies Type: general Titles: – TitleFull: Bayesian Brains without Probabilities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sanborn, Adam N. – PersonEntity: Name: NameFull: Chater, Nick IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 13646613 Numbering: – Type: volume Value: 20 – Type: issue Value: 12 Titles: – TitleFull: Trends in Cognitive Sciences Type: main |
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