Confidence as Bayesian Probability: From Neural Origins to Behavior
Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in th...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_08966273_v88_n1_p78_Meyniel |
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todo:paper_08966273_v88_n1_p78_Meyniel2023-10-03T15:43:46Z Confidence as Bayesian Probability: From Neural Origins to Behavior Meyniel, F. Sigman, M. Mainen, Z.F. accuracy association Bayes theorem Bayesian probability behavior cognition confusion (uncertainty) decision making forced choice method human information seeking learning nonhuman orbital cortex orientation perceptive discrimination positive feedback priority journal probability Review reward sensorimotor integration transcranial magnetic stimulation animal Bayes theorem brain physiology probability psychological model Animals Bayes Theorem Brain Cognition Decision Making Humans Models, Psychological Probability Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or "read out" from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions. © 2015 Elsevier Inc. Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_08966273_v88_n1_p78_Meyniel |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
accuracy association Bayes theorem Bayesian probability behavior cognition confusion (uncertainty) decision making forced choice method human information seeking learning nonhuman orbital cortex orientation perceptive discrimination positive feedback priority journal probability Review reward sensorimotor integration transcranial magnetic stimulation animal Bayes theorem brain physiology probability psychological model Animals Bayes Theorem Brain Cognition Decision Making Humans Models, Psychological Probability |
| spellingShingle |
accuracy association Bayes theorem Bayesian probability behavior cognition confusion (uncertainty) decision making forced choice method human information seeking learning nonhuman orbital cortex orientation perceptive discrimination positive feedback priority journal probability Review reward sensorimotor integration transcranial magnetic stimulation animal Bayes theorem brain physiology probability psychological model Animals Bayes Theorem Brain Cognition Decision Making Humans Models, Psychological Probability Meyniel, F. Sigman, M. Mainen, Z.F. Confidence as Bayesian Probability: From Neural Origins to Behavior |
| topic_facet |
accuracy association Bayes theorem Bayesian probability behavior cognition confusion (uncertainty) decision making forced choice method human information seeking learning nonhuman orbital cortex orientation perceptive discrimination positive feedback priority journal probability Review reward sensorimotor integration transcranial magnetic stimulation animal Bayes theorem brain physiology probability psychological model Animals Bayes Theorem Brain Cognition Decision Making Humans Models, Psychological Probability |
| description |
Research on confidence spreads across several sub-fields of psychology and neuroscience. Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability distributions, and summary confidence, pertaining to scalar summaries of those distributions. Summary confidence is, normatively, derived or "read out" from distributional confidence. Neural implementations of readout will trade off optimality versus flexibility of routing across brain systems, allowing confidence to serve diverse cognitive functions. © 2015 Elsevier Inc. |
| format |
JOUR |
| author |
Meyniel, F. Sigman, M. Mainen, Z.F. |
| author_facet |
Meyniel, F. Sigman, M. Mainen, Z.F. |
| author_sort |
Meyniel, F. |
| title |
Confidence as Bayesian Probability: From Neural Origins to Behavior |
| title_short |
Confidence as Bayesian Probability: From Neural Origins to Behavior |
| title_full |
Confidence as Bayesian Probability: From Neural Origins to Behavior |
| title_fullStr |
Confidence as Bayesian Probability: From Neural Origins to Behavior |
| title_full_unstemmed |
Confidence as Bayesian Probability: From Neural Origins to Behavior |
| title_sort |
confidence as bayesian probability: from neural origins to behavior |
| url |
http://hdl.handle.net/20.500.12110/paper_08966273_v88_n1_p78_Meyniel |
| work_keys_str_mv |
AT meynielf confidenceasbayesianprobabilityfromneuraloriginstobehavior AT sigmanm confidenceasbayesianprobabilityfromneuraloriginstobehavior AT mainenzf confidenceasbayesianprobabilityfromneuraloriginstobehavior |
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1807321550294089728 |