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What Is the Subjective Cost of Cognitive Effort? Load, Trait, and Aging Effects Revealed by Economic Preference

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  • Andrew Westbrook
  • Daria Kester
  • Todd S Braver

Abstract

It has long been assumed that people treat cognitive effort as costly, but also that such effort costs may vary greatly across individuals. Individual differences in subjective effort could present a major and pervasive confound in behavioral and neuroscience assessments, by conflating cognitive ability with cognitive motivation. Self-report cognitive effort scales have been developed, but objective measures are lacking. In this study, we use the behavioral economic approach of revealed preferences to quantify subjective effort. Specifically, we adapted a well-established discounting paradigm to measure the extent to which cognitive effort causes participants to discount monetary rewards. The resulting metrics are sensitive to both within-individual factors, including objective load and reward amount, and between-individual factors, including age and trait cognitive engagement. We further validate cognitive effort discounting by benchmarking it against well-established measures of delay discounting. The results highlight the promise and utility of behavioral economic tools for assessing trait and state influences on cognitive motivation.

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  • Andrew Westbrook & Daria Kester & Todd S Braver, 2013. "What Is the Subjective Cost of Cognitive Effort? Load, Trait, and Aging Effects Revealed by Economic Preference," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-8, July.
    • Handle: RePEc:plo:pone00:0068210
    DOI: 10.1371/journal.pone.0068210
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    References listed on IDEAS

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    1. Thaler, Richard, 1981. "Some empirical evidence on dynamic inconsistency," Economics Letters, Elsevier, vol. 8(3), pages 201-207.
    2. Liane Schmidt & Maël Lebreton & Marie-Laure Cléry-Melin & Jean Daunizeau & Mathias Pessiglione, 2012. "Neural Mechanisms Underlying Motivation of Mental Versus Physical Effort," PLOS Biology, Public Library of Science, vol. 10(2), pages 1-13, February.
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    Cited by:

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    2. Wouter Kool & Fiery A Cushman & Samuel J Gershman, 2016. "When Does Model-Based Control Pay Off?," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-34, August.
    3. Elizabeth A. Kensinger & Angela H. Gutchess, 2017. "Cognitive Aging in a Social and Affective Context: Advances Over the Past 50 Years," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 72(1), pages 61-70.
    4. repec:cup:judgdm:v:15:y:2020:i:4:p:586-599 is not listed on IDEAS
    5. Arian Aflaki & Pnina Feldman & Robert Swinney, 2019. "Becoming Strategic: Endogenous Consumer Time Preferences and Multiperiod Pricing," Operations Research, INFORMS, vol. 68(4), pages 1116-1131, July.
    6. Zohar Rusou & Moty Amar & Shahar Ayal, 2020. "The psychology of task management: The smaller tasks trap," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(4), pages 586-599, July.
    7. Oberholzer, Yvonne & Olschewski, Sebastian & Scheibehenne, Benjamin, 2024. "Complexity aversion in risky choices and valuations: Moderators and possible causes," Journal of Economic Psychology, Elsevier, vol. 100(C).
    8. Vanda Viola & Annalisa Tosoni & Ambra Brizi & Ilaria Salvato & Arie W Kruglanski & Gaspare Galati & Lucia Mannetti, 2015. "Need for Cognitive Closure Modulates How Perceptual Decisions Are Affected by Task Difficulty and Outcome Relevance," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-13, December.

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