Confidence as Bayesian Probability: From Neural Origins to Behavior

Florent Meyniel; Mariano Sigman; Zachary F Mainen

doi:10.1016/j.neuron.2015.09.039

Confidence as Bayesian Probability: From Neural Origins to Behavior

Neuron. 2015 Oct 7;88(1):78-92. doi: 10.1016/j.neuron.2015.09.039.

Authors

Florent Meyniel¹, Mariano Sigman², Zachary F Mainen³

Affiliations

¹ Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, F-91191, Gif sur Yvette Cedex, France. Electronic address: florent.meyniel@gmail.com.
² Departamento de Física, FCEN, UBA and IFIBA; Universidad Torcuato Di Tella, Buenos Aires, 1428 CABA, Argentina.
³ Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, 1400-038, Lisbon, Portugal.

PMID: 26447574
DOI: 10.1016/j.neuron.2015.09.039

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Animals
Bayes Theorem
Brain / physiology*
Cognition / physiology*
Decision Making
Humans
Models, Psychological
Probability