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Optimal coding and neuronal adaptation in economic decisions

Author

Listed:
  • Aldo Rustichini

    (University of Minnesota)

  • Katherine E. Conen

    (Washington University in St Louis)

  • Xinying Cai

    (Washington University in St Louis
    NYU Shanghai)

  • Camillo Padoa-Schioppa

    (Washington University in St Louis
    Washington University in St Louis
    Washington University in St Louis)

Abstract

During economic decisions, offer value cells in orbitofrontal cortex (OFC) encode the values of offered goods. Furthermore, their tuning functions adapt to the range of values available in any given context. A fundamental and open question is whether range adaptation is behaviorally advantageous. Here we present a theory of optimal coding for economic decisions. We propose that the representation of offer values is optimal if it ensures maximal expected payoff. In this framework, we examine offer value cells in non-human primates. We show that their responses are quasi-linear even when optimal tuning functions are highly non-linear. Most importantly, we demonstrate that for linear tuning functions range adaptation maximizes the expected payoff. Thus value coding in OFC is functionally rigid (linear tuning) but parametrically plastic (range adaptation with optimal gain). Importantly, the benefit of range adaptation outweighs the cost of functional rigidity. While generally suboptimal, linear tuning may facilitate transitive choices.

Suggested Citation

  • Aldo Rustichini & Katherine E. Conen & Xinying Cai & Camillo Padoa-Schioppa, 2017. "Optimal coding and neuronal adaptation in economic decisions," Nature Communications, Nature, vol. 8(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01373-y
    DOI: 10.1038/s41467-017-01373-y
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    Cited by:

    1. Katarzyna Jurewicz & Brianna J. Sleezer & Priyanka S. Mehta & Benjamin Y. Hayden & R. Becket Ebitz, 2024. "Irrational choices via a curvilinear representational geometry for value," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Landry, Peter & Webb, Ryan, 2021. "Pairwise normalization: A neuroeconomic theory of multi-attribute choice," Journal of Economic Theory, Elsevier, vol. 193(C).
    4. Hertel, Johanna & Igan, Deniz & Smith, John, 2023. "On the dynamics of the responses in Frydman and Jin (2022): Nullius in verba," MPRA Paper 117788, University Library of Munich, Germany.
    5. Mehran Spitmaan & Oihane Horno & Emily Chu & Alireza Soltani, 2019. "Combinations of low-level and high-level neural processes account for distinct patterns of context-dependent choice," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-31, October.
    6. Jonathan Schaffner & Sherry Dongqi Bao & Philippe N. Tobler & Todd A. Hare & Rafael Polania, 2023. "Sensory perception relies on fitness-maximizing codes," Nature Human Behaviour, Nature, vol. 7(7), pages 1135-1151, July.
    7. Aldo Rustichini, 2023. "Economics with a biological foundation," Indian Economic Review, Springer, vol. 58(1), pages 1-40, June.
    8. Payzan-LeNestour, Elise & Woodford, Michael, 2022. "Outlier blindness: A neurobiological foundation for neglect of financial risk," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1316-1343.
    9. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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