Efficient stabilization of imprecise statistical inference through conditional belief updating.
Journal
Nature human behaviour
ISSN: 2397-3374
Titre abrégé: Nat Hum Behav
Pays: England
ID NLM: 101697750
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
19
07
2021
accepted:
11
08
2022
pubmed:
23
9
2022
medline:
20
12
2022
entrez:
22
9
2022
Statut:
ppublish
Résumé
Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.
Identifiants
pubmed: 36138224
doi: 10.1038/s41562-022-01445-0
pii: 10.1038/s41562-022-01445-0
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1691-1704Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : 1R01MH115554-01
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.
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