Learning whom to cooperate with: neurocomputational mechanisms for choosing cooperative partners

Tao Jin, Shen Zhang, Patricia Lockwood, Iris Vilares, Haiyan Wu, Chao Liu, Yina Ma

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Cooperation is fundamental for survival and a functioning society. With substantial individual variability in cooperativeness, we must learn whom to cooperate with, and often make these decisions on behalf of others. Understanding how people learn about the cooperativeness of others, and the neurocomputational mechanisms supporting this learning, is therefore essential. During functional magnetic resonance imaging scanning, participants completed a novel cooperation-partner-choice task where they learned to choose between cooperative and uncooperative partners through trial-and-error both for themselves and vicariously for another person. Interestingly, when choosing for themselves, participants made faster and more exploitative choices than when choosing for another person. Activity in the ventral striatum preferentially responded to prediction errors (PEs) during self-learning, whereas activity in the perigenual anterior cingulate cortex (ACC) signaled both personal and vicarious PEs. Multivariate pattern analyses showed distinct coding of personal and vicarious choice-making and outcome processing in the temporoparietal junction (TPJ), dorsal ACC, and striatum. Moreover, in right TPJ the activity pattern that differentiated self and other outcomes was associated with individual differences in exploitation tendency. We reveal neurocomputational mechanisms supporting cooperative learning and show that this learning is ref lected in trial-by-trial univariate signals and multivariate patterns that can distinguish personal and vicarious choices.

Original languageEnglish (US)
Pages (from-to)4612-4625
Number of pages14
JournalCerebral Cortex
Volume33
Issue number8
DOIs
StatePublished - Apr 15 2023

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (NSFC) (32271092 and 32130045 to C. L.; 32125019 and 31771204 to Y. M.), the Major Project of National Social Science Foundation (19ZDA363 to C. L.), the National Key Research and Development Program of China (2022ZD0211000 to Y. M.), the Beijing Municipal Science and Technology Commission (Z151100003915122 to C. L.), and the National Program for Support of Top-notch Young Professionals to C. L.

Publisher Copyright:
© The Author(s) 2022. Published by Oxford University Press. All rights reserved.

Keywords

  • computational modeling
  • cooperation-partner selection
  • multivariate pattern
  • prediction errors
  • vicarious learning

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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