Flexible social inference facilitates targeted social learning when rewards are not observable.


Journal

Nature human behaviour
ISSN: 2397-3374
Titre abrégé: Nat Hum Behav
Pays: England
ID NLM: 101697750

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 25 09 2020
accepted: 20 07 2023
pubmed: 18 8 2023
medline: 18 8 2023
entrez: 17 8 2023
Statut: ppublish

Résumé

Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behaviour. We compared our social inference model against simpler heuristics in three studies of human behaviour in a collective-sensing task. Experiment 1 demonstrated that average performance improved as a function of group size at a rate greater than predicted by heuristic models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behaviour.

Identifiants

pubmed: 37591983
doi: 10.1038/s41562-023-01682-x
pii: 10.1038/s41562-023-01682-x
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1767-1776

Subventions

Organisme : National Science Foundation (NSF)
ID : 14747
Organisme : National Science Foundation (NSF)
ID : 1122374

Informations de copyright

© 2023. The Author(s), under exclusive licence to Springer Nature Limited.

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Auteurs

Robert D Hawkins (RD)

Department of Psychology, Stanford University, Stanford, CA, USA. rdhawkins@wisc.edu.
Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA. rdhawkins@wisc.edu.

Andrew M Berdahl (AM)

School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA.

Alex 'Sandy' Pentland (A')

MIT Media Lab, MIT, Cambridge, MA, USA.

Joshua B Tenenbaum (JB)

Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.

Noah D Goodman (ND)

Department of Psychology, Stanford University, Stanford, CA, USA.
Department of Computer Science, Stanford University, Stanford, CA, USA.

P M Krafft (PM)

Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
Creative Computing Institute, University of Arts London, London, UK.

Classifications MeSH