A Mathematical Framework for Statistical Decision Confidence.
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| Title: | A Mathematical Framework for Statistical Decision Confidence. |
|---|---|
| Authors: | Hangya, Balázs, Sanders, Joshua I., Kepecs, Adam |
| Source: | Neural Computation. 2016, Vol. 28 Issue 9, p1840-1858. 19p. 1 Diagram, 2 Graphs. |
| Subjects: | Statistical decision making, Probability theory, Animal behavior, Algorithms, Bayesian analysis |
| Abstract: | Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here, we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice, and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press 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: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 117805969 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Mathematical Framework for Statistical Decision Confidence. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hangya%2C+Balázs%22">Hangya, Balázs</searchLink><br /><searchLink fieldCode="AR" term="%22Sanders%2C+Joshua+I%2E%22">Sanders, Joshua I.</searchLink><br /><searchLink fieldCode="AR" term="%22Kepecs%2C+Adam%22">Kepecs, Adam</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. 2016, Vol. 28 Issue 9, p1840-1858. 19p. 1 Diagram, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+behavior%22">Animal behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the evidence contributing to it. Here, we used this formal definition as a starting point to develop a normative statistical framework for decision confidence. Our goal was to make general predictions that do not depend on the structure of the noise or a specific algorithm for estimating confidence. We analytically proved several interrelations between statistical decision confidence and observable decision measures, such as evidence discriminability, choice, and accuracy. These interrelationships specify necessary signatures of decision confidence in terms of externally quantifiable variables that can be empirically tested. Our results lay the foundations for a mathematically rigorous treatment of decision confidence that can lead to a common framework for understanding confidence across different research domains, from human and animal behavior to neural representations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=117805969 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/NECO_a_00864 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1840 Subjects: – SubjectFull: Statistical decision making Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Animal behavior Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Bayesian analysis Type: general Titles: – TitleFull: A Mathematical Framework for Statistical Decision Confidence. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hangya, Balázs – PersonEntity: Name: NameFull: Sanders, Joshua I. – PersonEntity: Name: NameFull: Kepecs, Adam IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 28 – Type: issue Value: 9 Titles: – TitleFull: Neural Computation Type: main |
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