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Published May 20, 2020 | Supplemental Material
Journal Article Open

A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning


When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents' previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model's arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others.

Additional Information

© 2020 Elsevier Inc. Received 19 November 2019, Revised 18 January 2020, Accepted 25 February 2020, Available online 17 March 2020. This work was funded by the Caltech Conte Center for the Neurobiology of Social Decision-Making. K.I. is supported by the Japan Society for the Promotion of Science and the Swartz Foundation. Author Contributions: C.J.C. and J.P.O. were responsible for conceptualization and methodology. C.J.C. carried out the investigations. C.J.C. and K.I. performed the analyses. C.J.C. and J.P.O. wrote the original manuscript draft. C.J.C., K.I., and J.P.O. reviewed and edited the manuscript. J.P.O. supervised the study and acquired funding. The authors declare no competing interests.

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August 22, 2023
October 19, 2023