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A Common Mechanism Underlying Food Choice and Social Decisions

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  • Ian Krajbich
  • Todd Hare
  • Björn Bartling
  • Yosuke Morishima
  • Ernst Fehr

Abstract

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others’ benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making.Author Summary: One critical question that concerns all disciplines involved in the study of human decision-making is whether different types of decisions are made in different ways, or whether there exists a common decision mechanism that underlies human choices. If the latter, what are the properties of that mechanism? Here we characterize a dynamical model of decision making that was initially fit to subjects making food choices but was later able to accurately predict choices and reaction times of separate groups of subjects making social decisions. The robustness of the model across different subjects, tasks, and environments supports the idea that the brain uses a consistent process for making decisions.

Suggested Citation

  • Ian Krajbich & Todd Hare & Björn Bartling & Yosuke Morishima & Ernst Fehr, 2015. "A Common Mechanism Underlying Food Choice and Social Decisions," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-24, October.
  • Handle: RePEc:plo:pcbi00:1004371
    DOI: 10.1371/journal.pcbi.1004371
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    Cited by:

    1. Fadong Chen & Urs Fischbacher, 2020. "Cognitive processes underlying distributional preferences: a response time study," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 421-446, June.
    2. Daniel Serra, 2019. "La neuroéconomie en question : débats et controverses," Working Papers halshs-02160911, HAL.
    3. Clithero, John A., 2018. "Response times in economics: Looking through the lens of sequential sampling models," Journal of Economic Psychology, Elsevier, vol. 69(C), pages 61-86.
    4. repec:cup:judgdm:v:14:y:2019:i:4:p:381-394 is not listed on IDEAS
    5. Arkady Konovalov & Ian Krajbich, 2019. "Revealed strength of preference: Inference from response times," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 381-394, July.
    6. Pirrone, Angelo & Gobet, Fernand, 2021. "Is attentional discounting in value-based decision making magnitude sensitive?," LSE Research Online Documents on Economics 108608, London School of Economics and Political Science, LSE Library.
    7. J. Tyler Boyd-Meredith & Alex T. Piet & Emily Jane Dennis & Ahmed El Hady & Carlos D. Brody, 2022. "Stable choice coding in rat frontal orienting fields across model-predicted changes of mind," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Daniel Serra, 2021. "Decision-making: from neuroscience to neuroeconomics—an overview," Theory and Decision, Springer, vol. 91(1), pages 1-80, July.
    9. Amasino, Dianna R. & Dolgin, Jack & Huettel, Scott A., 2023. "Eyes on the account size: Interactions between attention and budget in consumer choice," Journal of Economic Psychology, Elsevier, vol. 97(C).
    10. Daniel Serra, 2019. "Neuroeconomics and modern neuroscience," CEE-M Working Papers halshs-02160907, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    11. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.

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