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Observational learning computations in neurons of the human anterior cingulate cortex

Author

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  • Michael R. Hill

    (California Institute of Technology
    Computation and Neural Systems, California Institute of Technology
    University of California (UCLA)
    Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech H4)

  • Erie D. Boorman

    (Computation and Neural Systems, California Institute of Technology
    California Institute of Technology
    Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital
    Present address: Center for Mind & Brain, University of California Davis, Davis, California 95618, USA)

  • Itzhak Fried

    (University of California (UCLA)
    Functional Neurosurgery Unit, Tel-Aviv Medical Centre and Sackler Faculty of Medicine, Tel-Aviv University)

Abstract

When learning from direct experience, neurons in the primate brain have been shown to encode a teaching signal used by algorithms in artificial intelligence: the reward prediction error (PE)—the difference between how rewarding an event is, and how rewarding it was expected to be. However, in humans and other species learning often takes place by observing other individuals. Here, we show that, when humans observe other players in a card game, neurons in their rostral anterior cingulate cortex (rACC) encode both the expected value of an observed choice, and the PE after the outcome was revealed. Notably, during the same task neurons recorded in the amygdala (AMY) and the rostromedial prefrontal cortex (rmPFC) do not exhibit this type of encoding. Our results suggest that humans learn by observing others, at least in part through the encoding of observational PEs in single neurons in the rACC.

Suggested Citation

  • Michael R. Hill & Erie D. Boorman & Itzhak Fried, 2016. "Observational learning computations in neurons of the human anterior cingulate cortex," Nature Communications, Nature, vol. 7(1), pages 1-12, November.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12722
    DOI: 10.1038/ncomms12722
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    Cited by:

    1. Koen M. M. Frolichs & Gabriela Rosenblau & Christoph W. Korn, 2022. "Incorporating social knowledge structures into computational models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    2. M. A. Pisauro & E. F. Fouragnan & D. H. Arabadzhiyska & M. A. J. Apps & M. G. Philiastides, 2022. "Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    3. Sam Ereira & Raymond J Dolan & Zeb Kurth-Nelson, 2018. "Agent-specific learning signals for self–other distinction during mentalising," PLOS Biology, Public Library of Science, vol. 16(4), pages 1-32, April.

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