Neural and computational underpinnings of biased confidence in human reinforcement learning
Chih-Chung Ting
(1)
,
Nahuel Salem-Garcia
(2)
,
Stefano Palminteri
(3)
,
Jan Engelmann
(4)
,
Maël Lebreton
(2, 5, 6)
Chih-Chung Ting
- Fonction : Auteur
- PersonId : 1339372
- ORCID : 0000-0002-0620-128X
Nahuel Salem-Garcia
- Fonction : Auteur
- PersonId : 1286646
- ORCID : 0000-0003-2332-3104
Stefano Palminteri
- Fonction : Auteur
- PersonId : 1189601
- ORCID : 0000-0001-5768-6646
Jan Engelmann
- Fonction : Auteur
- PersonId : 814037
- ORCID : 0000-0001-6493-8792
Maël Lebreton
- Fonction : Auteur
- PersonId : 1286627
- IdHAL : mael-lebreton
- ORCID : 0000-0002-2071-4890
Résumé
While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.
Domaines
Economies et financesFormat du dépôt | Notice |
---|---|
Type de dépôt | Article dans une revue |
Titre |
en
Neural and computational underpinnings of biased confidence in human reinforcement learning
|
Résumé |
en
While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.
|
Auteur(s) |
Chih-Chung Ting
1
, Nahuel Salem-Garcia
2
, Stefano Palminteri
3
, Jan Engelmann
4
, Maël Lebreton
2, 5, 6
1
UHH -
Universität Hamburg
( 300676 )
- Allemagne
2
CISA -
Swiss Center for Affective Sciences
( 127909 )
- UniGE 7, rue des Battoirs 1205 Geneva Switzerland Tel: +41 22 379 98 01 Fax: +41 22 379 98 44
- Suisse
3
ENS-PSL -
École normale supérieure - Paris
( 59704 )
- 45, Rue d'Ulm - 75230 Paris cedex 05
- France
4
ASE -
Amsterdam School of Economics
( 104737 )
- Roetersstraat 11, 1018 WB Amsterdam
- Pays-Bas
5
PSE -
Paris School of Economics
( 301309 )
- 48 boulevard Jourdan 75014 Paris
- France
6
PJSE -
Paris Jourdan Sciences Economiques
( 578027 )
- 48 boulevard Jourdan 75014 Paris
- France
|
Page/Identifiant |
6896
|
Vulgarisation |
Non
|
Comité de lecture |
Oui
|
Audience |
Internationale
|
Langue du document |
Anglais
|
Nom de la revue |
|
Date de publication |
2023
|
Volume |
14
|
Licence |
Paternité
|
Public visé |
Scientifique
|
Projet(s) ANR |
|
Domaine(s) |
|
Mots-clés |
en
Decision, Decision making, Human behaviour, Learning algorithms
|
DOI | 10.1038/s41467-023-42589-5 |
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