Subjective confidence reflects representation of Bayesian probability in cortex


Author: Laura S Geurts1, James R H Cooke1, Ruben S van Bergen1,2, Janneke F M Jehee3

Affiliation: <sup>1</sup> Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands.

<sup>2</sup> Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.

<sup>3</sup> Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands. janneke.jehee@donders.ru.nl.
Conference/Journal: Nat Hum Behav
Date published: 2022 Feb 1
Other:
 Volume ID: 6   ,  Issue ID: 2   ,  Pages: 294-305   ,  Special Notes: doi: 10.1038/s41562-021-01247-w.   ,  Word Count: 164  
    What gives rise to the human sense of confidence? Here we tested the Bayesian hypothesis that confidence is based on a probability distribution represented in neural population activity. We implemented several computational models of confidence and tested their predictions using psychophysics and functional magnetic resonance imaging. Using a generative model-based decoding technique, we extracted probability distributions from neural population activity in human visual cortex. We found that subjective confidence tracks the shape of the decoded distribution. That is, when sensory evidence was more precise, as indicated by the decoded distribution, observers reported higher levels of confidence. We furthermore found that neural activity in the insula, anterior cingulate and prefrontal cortex was linked to both the shape of the decoded distribution and reported confidence, in ways consistent with the Bayesian model. Altogether, our findings support recent statistical theories of confidence and suggest that probabilistic information guides the computation of one's sense of confidence.





PMID: 35058641 PMCID: PMC7612428 (available on  2022-07-20) DOI: 10.1038/s41562-021-01247-w