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Neuro-computational account of arbitration between imitation and emulation during human observational learning

Charpentier, Caroline C. and Iigaya, Kiyohito and O'Doherty, John P. (2019) Neuro-computational account of arbitration between imitation and emulation during human observational learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191104-131139153

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Abstract

In observational learning (OL), organisms learn from observing the behavior of others. There are at least two distinct strategies for OL. Imitation involves learning to repeat the previous actions of other agents, while in emulation, learning proceeds from inferring the goals and intentions of others. While putative neural correlates for these forms of learning have been identified, a fundamental question remains unaddressed: how does the brain decides which strategy to use in a given situation? Here we developed a novel computational model in which arbitration between the strategies is determined by the predictive reliability, such that control over behavior is adaptively weighted toward the strategy with the most reliable prediction. To test the theory, we designed a novel behavioral task in which our experimental manipulations produced dissociable effects on the reliability of the two strategies. Participants performed this task while undergoing fMRI in two independent studies (the second a pre-registered replication of the first). Behavior manifested patterns consistent with both emulation and imitation and flexibly changed between the two strategies as expected from the theory. Computational modelling revealed that behavior was best described by an arbitration model, in which the reliability of the emulation strategy determined the relative weights allocated to behavior for each strategy. Emulation reliability - the model's arbitration signal - was encoded in the ventrolateral prefrontal cortex, temporoparietal junction and rostral cingulate cortex. Being replicated across two fMRI studies, these findings suggest a neuro-computational mechanism for allocating control between emulation and imitation during observational learning.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/828723DOIDiscussion Paper
https://www.biorxiv.org/content/10.1101/828723v1OrganizationDiscussion Paper
ORCID:
AuthorORCID
Charpentier, Caroline C.0000-0002-7283-0738
Iigaya, Kiyohito0000-0002-4748-8432
O'Doherty, John P.0000-0003-0016-3531
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. bioRxiv preprint first posted online Nov. 4, 2019. 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.C. and J.O. were responsible for conceptualization and methodology. C.C. carried out the investigations. C.C. and K.I. performed the analyses. C.C. and J.O. wrote the original manuscript draft. C.C., K.I. and J.O. reviewed and edited the manuscript. J.O. supervised the study and acquired funding. The authors declare no competing interests.
Funders:
Funding AgencyGrant Number
Caltech Conte Center for the Neurobiology of Social Decision MakingUNSPECIFIED
Japan Society for the Promotion of Science (JSPS)UNSPECIFIED
Swartz FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20191104-131139153
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191104-131139153
Official Citation:Neuro-computational account of arbitration between imitation and emulation during human observational learning. Caroline J Charpentier, Kiyohito Iigaya, John P O'Doherty. bioRxiv 828723; doi: https://doi.org/10.1101/828723
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99655
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Nov 2019 21:21
Last Modified:28 May 2020 19:59

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