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An opportunity cost model of subjective effort and task performance
Open accessAbstract
Why does performing certain tasks cause the aversive experience of mental effort and concomitant deterioration in task performance? One explanation posits a physical resource that is depleted over time. We propose an alternative explanation that centers on mental representations of the costs and benefits associated with task performance. Specifically, certain computational mechanisms, especially those associated with executive function, can be deployed for only a limited number of simultaneous tasks at any given moment. Consequently, the deployment of these computational mechanisms carries an opportunity cost – that is, the next-best use to which these systems might be put. We argue that the phenomenology of effort can be understood as the felt output of these cost/benefit computations. In turn, the subjective experience of effort motivates reduced deployment of these computational mechanisms in the service of the present task. These opportunity cost representations, then, together with other cost/benefit calculations, determine effort expended and, everything else equal, result in performance reductions. In making our case for this position, we review alternative explanations for both the phenomenology of effort associated with these tasks and for performance reductions over time. Likewise, we review the broad range of relevant empirical results from across sub-disciplines, especially psychology and neuroscience. We hope that our proposal will help to build links among the diverse fields that have been addressing similar questions from different perspectives, and we emphasize ways in which alternative models might be empirically distinguished.
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“According to the opportunity cost model (Kurzban et al., 2013), the experienced costs of effort invested in a task are weighed against alternative opportunities.”
From Worth the Effort: the Start and Stick to Desirable Difficulties (S2D2) Framework (2023) · cites this paper as Kurzban et al., 2013
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Metadata
- Authors
- Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Behavioral and Brain Sciences
- Publisher
- Cambridge University Press (CUP)
- Published
- 2013-12
- Volume
- 36
- Issue
- 6
- Pages
- 661-679
- ISSN
- 0140-525X (ISSN-L)
- 1469-1825
- PMC
- PMC3856320
- OpenAlex
- W2164682760
- Cited by
- 1,381
- References
- 489
- Citations by year
- 2026: 99 · 2025: 132 · 2024: 118 · 2023: 119 · 2022: 108 · 2021: 138 · 2020: 133 · 2019: 128 · 2018: 107 · 2017: 97 · 2016: 87 · 2015: 68 · 2014: 34 · 2013: 8
- FWCI
- 36.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Behavioral and Brain Sciences (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ScholarlyCommons (University of Pennsylvania) (submittedVersion)
- Funders
- National Institute on Aging (K01AG033182)
- National Institute of Mental Health (R01MH101111)
- National Institute on Drug Abuse (DA029149, R01DA029149)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
- Keywords
- subjective effort, cognitive effort, task performance, opportunity cost, executive function, cognitive resource allocation, effort aversion
- MeSH terms
- Cost-Benefit Analysis, Mental Fatigue, Humans, Models, Psychological, Psychomotor Performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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