Model-free decision making is prioritized when learning to avoid harming others

Patricia L. Lockwood, Miriam C. Klein-Flügge, Ayat Abdurahman, Molly J. Crockett

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Moral behavior requires learning how our actions help or harm others. Theoretical accounts of learning propose a key division between “model-free” algorithms that cache outcome values in actions and “model-based” algorithms that map actions to outcomes. Here, we tested the engagement of these mechanisms and their neural basis as participants learned to avoid painful electric shocks for themselves and a stranger. We found that model-free decision making was prioritized when learning to avoid harming others compared to oneself. Model-free prediction errors for others relative to self were tracked in the thalamus/caudate. At the time of choice, neural activity consistent with model-free moral learning was observed in subgenual anterior cingulate cortex (sgACC), and switching after harming others was associated with stronger connectivity between sgACC and dorsolateral prefrontal cortex. Finally, model-free moral learning varied with individual differences in moral judgment. Our findings suggest moral learning favors efficiency over flexibility and is underpinned by specific neural mechanisms.

Original languageEnglish
Pages (from-to)27719-27730
Number of pages12
JournalProceedings of the National Academy of Sciences
Volume117
Issue number44
Early online date14 Oct 2020
DOIs
Publication statusPublished - 3 Nov 2020

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. This work was supported by a Medical Research Council Fellowship (MR/P014097/1), a Christ Church Junior Research Fellowship, and a Christ Church Research Centre Grant to P.L.L. and a Wellcome Trust grant (106164/A/14/Z) and an Academy of Medical Sciences (SBF001\1008) grant to

Funding Information:
M.J.C. M.C.K.-F. was supported by a Sir Henry Wellcome Fellowship (103184/Z/13/ Z). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). We thank Ms. Eloise Copland and Dr. Hongbo Yu for assistance with data collection; Dr. Marco Wittman, Dr. Peter Smittenaar, Dr. Quentin Huys, Dr. Elsa Fouragnan, and Dr. Mehdi

Publisher Copyright:
© 2020 National Academy of Sciences. All rights reserved.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Learning
  • Model-free
  • Moral
  • Neuroimaging
  • Prediction error

ASJC Scopus subject areas

  • General

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