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Intertemporal Choice as Discounted Value Accumulation

Author

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  • Christian A Rodriguez
  • Brandon M Turner
  • Samuel M McClure

Abstract

Two separate cognitive processes are involved in choosing between rewards available at different points in time. The first is temporal discounting, which consists of combining information about the size and delay of prospective rewards to represent subjective values. The second involves a comparison of available rewards to enable an eventual choice on the basis of these subjective values. While several mathematical models of temporal discounting have been developed, the reward selection process has been largely unexplored. To address this limitation, we evaluated the applicability of the Linear Ballistic Accumulator (LBA) model as a theory of the selection process in intertemporal choice. The LBA model formalizes the selection process as a sequential sampling algorithm in which information about different choice options is integrated until a decision criterion is reached. We compared several versions of the LBA model to demonstrate that choice outcomes and response times in intertemporal choice are well captured by the LBA process. The relationship between choice outcomes and response times that derives from the LBA model cannot be explained by temporal discounting alone. Moreover, the drift rates that drive evidence accumulation in the best-fitting LBA model are related to independently estimated subjective values derived from various temporal discounting models. These findings provide a quantitative framework for predicting dynamics of choice-related activity during the reward selection process in intertemporal choice and link intertemporal choice to other classes of decisions in which the LBA model has been applied.

Suggested Citation

  • Christian A Rodriguez & Brandon M Turner & Samuel M McClure, 2014. "Intertemporal Choice as Discounted Value Accumulation," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
  • Handle: RePEc:plo:pone00:0090138
    DOI: 10.1371/journal.pone.0090138
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    Citations

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    Cited by:

    1. 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.
    2. Stefan Scherbaum & Simon Frisch & Susanne Leiberg & Steven J. Lade & Thomas Goschke & Maja Dshemuchadse, 2016. "Process dynamics in delay discounting decisions: An attractor dynamics approach," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 11(5), pages 472-495, September.
    3. repec:cup:judgdm:v:16:y:2021:i:4:p:1010-1038 is not listed on IDEAS
    4. Cary Frydman & Ian Krajbich, 2022. "Using Response Times to Infer Others’ Private Information: An Application to Information Cascades," Management Science, INFORMS, vol. 68(4), pages 2970-2986, April.
    5. repec:cup:judgdm:v:14:y:2019:i:4:p:381-394 is not listed on IDEAS
    6. 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.
    7. Fadong Chen & Jiehui Zheng & Lei Wang & Ian Krajbich, 2024. "Attribute latencies causally shape intertemporal decisions," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    9. repec:cup:judgdm:v:11:y:2016:i:5:p:472-495 is not listed on IDEAS
    10. Yan-Bang Zhou & Qiang Li & Hong-Zhi Liu, 2021. "Visual attention and time preference reversals," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(4), pages 1010-1038, July.
    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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