Natural statistics support a rational account of confidence biases.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
06 07 2023
06 07 2023
Historique:
received:
06
01
2022
accepted:
09
06
2023
medline:
10
7
2023
pubmed:
7
7
2023
entrez:
6
7
2023
Statut:
epublish
Résumé
Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional models, necessitating strong assumptions about the representations over which confidence is computed. To address this, we used deep neural networks to develop a model of decision confidence that operates directly over high-dimensional, naturalistic stimuli. The model accounts for a number of puzzling dissociations between decisions and confidence, reveals a rational explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.
Identifiants
pubmed: 37414780
doi: 10.1038/s41467-023-39737-2
pii: 10.1038/s41467-023-39737-2
pmc: PMC10326055
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
3992Subventions
Organisme : NIMH NIH HHS
ID : F32 MH117972
Pays : United States
Informations de copyright
© 2023. The Author(s).
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