Rational arbitration between statistics and rules in human sequence processing.
Journal
Nature human behaviour
ISSN: 2397-3374
Titre abrégé: Nat Hum Behav
Pays: England
ID NLM: 101697750
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
06
02
2020
accepted:
17
11
2021
pubmed:
3
5
2022
medline:
24
8
2022
entrez:
2
5
2022
Statut:
ppublish
Résumé
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.
Identifiants
pubmed: 35501360
doi: 10.1038/s41562-021-01259-6
pii: 10.1038/s41562-021-01259-6
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1087-1103Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.
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