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Reminders of past choices bias decisions for reward in humans

Author

Listed:
  • Aaron M. Bornstein

    (Neuroscience Institute, Princeton University)

  • Mel W. Khaw

    (Columbia University)

  • Daphna Shohamy

    (Brain, Behavior Institute
    Kavli Center for Brain Science, Columbia University)

  • Nathaniel D. Daw

    (Neuroscience Institute, Princeton University
    Princeton University)

Abstract

We provide evidence that decisions are made by consulting memories for individual past experiences, and that this process can be biased in favour of past choices using incidental reminders. First, in a standard rewarded choice task, we show that a model that estimates value at decision-time using individual samples of past outcomes fits choices and decision-related neural activity better than a canonical incremental learning model. In a second experiment, we bias this sampling process by incidentally reminding participants of individual past decisions. The next decision after a reminder shows a strong influence of the action taken and value received on the reminded trial. These results provide new empirical support for a decision architecture that relies on samples of individual past choice episodes rather than incrementally averaged rewards in evaluating options and has suggestive implications for the underlying cognitive and neural mechanisms.

Suggested Citation

  • Aaron M. Bornstein & Mel W. Khaw & Daphna Shohamy & Nathaniel D. Daw, 2017. "Reminders of past choices bias decisions for reward in humans," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15958
    DOI: 10.1038/ncomms15958
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    Citations

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    Cited by:

    1. Jingwei Sun & Jian Li & Hang Zhang, 2019. "Human representation of multimodal distributions as clusters of samples," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-29, May.
    2. Ronayne, David & Brown, Gordon D.A., 2016. "Multi-Attribute Decision By Sampling : An Account Of The Attraction, Compromise And Similarity Effects," Economic Research Papers 269322, University of Warwick - Department of Economics.
    3. Gloria W. Feng & Robb B. Rutledge, 2024. "Surprising sounds influence risky decision making," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. repec:cup:judgdm:v:16:y:2021:i:1:p:201-237 is not listed on IDEAS
    5. 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.
    6. Zhang Chen & Rob W. Holland & Julian Quandt & Ap Dijksterhuis & Harm Veling, 2021. "How preference change induced by mere action versus inaction persists over time," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(1), pages 201-237, January.
    7. Mel W Khaw & Luminita Stevens & Michael Woodford, 2021. "Individual differences in the perception of probability," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-25, April.
    8. repec:cup:judgdm:v:16:y:2021:i:1:p:114-130 is not listed on IDEAS
    9. Rosen Valchev & Cosmin Ilut, 2017. "Economic Agents as Imperfect Problem Solvers," 2017 Meeting Papers 1285, Society for Economic Dynamics.
    10. Vincent Moens & Alexandre Zénon, 2019. "Learning and forgetting using reinforced Bayesian change detection," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-41, April.
    11. Mikhail S. Spektor & Dirk U. Wulff, 2021. "Myopia drives reckless behavior in response to over-taxation," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(1), pages 114-130, January.
    12. 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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