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Heuristic and optimal policy computations in the human brain during sequential decision-making

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  • Christoph W. Korn

    (University of Zurich
    University of Zurich
    University Medical Center Hamburg-Eppendorf)

  • Dominik R. Bach

    (University of Zurich
    University of Zurich
    University College London)

Abstract

Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.

Suggested Citation

  • Christoph W. Korn & Dominik R. Bach, 2018. "Heuristic and optimal policy computations in the human brain during sequential decision-making," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02750-3
    DOI: 10.1038/s41467-017-02750-3
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    Cited by:

    1. Longbing Cao & Chengzhang Zhu, 2022. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
    2. 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.
    3. Florian Ott & Dimitrije Marković & Alexander Strobel & Stefan J Kiebel, 2020. "Dynamic integration of forward planning and heuristic preferences during multiple goal pursuit," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-27, February.
    4. Jacqueline Scholl & Hailey A Trier & Matthew F S Rushworth & Nils Kolling, 2022. "The effect of apathy and compulsivity on planning and stopping in sequential decision-making," PLOS Biology, Public Library of Science, vol. 20(3), pages 1-38, March.

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