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Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation
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There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.
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“Third, by controlling the temporal parameters of both the cognitive and physical effort tasks, it was possible to eliminate delay discounting as an explanation of choice behaviour [5, 47, 48].”
From Neurocomputational mechanisms underlying subjective valuation of effort costs (2017) · cites this paper as [5]
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Gill, H., Badulescu, S., Vincenzo, J. D. D., Tabassum, A., Mckenzie, A., Shah, H., Amin, M., Llach, C.-D., Rosenblat, J. D., McIntyre, R. S., et al. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.12.090 · Cited by 12
Reward processing and reinforcement learning: from adolescence to agingPreprint
Cutler, J., Apps, M. A. J., & Lockwood, P. L. (2022) · doi:10.31234/osf.io/pnuk8 · Cited by 11
Greenhouse‐Tucknott, A., Wrightson, J. G., Berens, S. C., Dekerle, J., & Harrison, N. A. (2021) · PsyArXiv (OSF Preprints) · doi:10.1016/j.physbeh.2024.114792 · Cited by 12
Byrne, A., Hewitt, D., Henderson, J. R., Newton‐Fenner, A., Roberts, H., Tyson‐Carr, J., Fallon, N., Giesbrecht, T., & Stančák, A. (2021) · Psychophysiology · doi:10.1111/psyp.13897 · Cited by 10
Using metabolic energy to quantify the subjective value of physical effort
Summerside, E. M., & Ahmed, A. A. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0387 · Cited by 13
Strengths of social ties modulate brain computations for third-party punishment
Tang, Z., Qu, C., Hu, Y., Bénistant, J., Moisan, F., Derrington, E. A., & Dreher, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-37286-8 · Cited by 11
Effort-reward balance and work motivation in rats: Effects of context and order of experience
Knauss, Z. T., Filipovic, M., Smith, K. A., Queener, M. M., Lubera, J. A., Bolden-Hall, N. M., Smith, J. P., Goldsmith, R. S., Bischoff, J. E., Miller, M. K., et al. (2020) · Behavioural Processes · doi:10.1016/j.beproc.2020.104239 · Cited by 11
Understanding Motivation with the Progressive Ratio Task: a Hierarchical Bayesian Model
Chen, Y., Breitborde, N. J. K., Peruggia, M., & Zandt, T. V. (2022) · Computational Brain & Behavior · doi:10.1007/s42113-021-00114-1 · Cited by 9
Effort cost of harvest affects decisions and movement vigor of marmosets during foraging
Hage, P., Jang, I. K., Looi, V., Fakharian, M. A., Orozco, S. P., Pi, J. S., Sedaghat-Nejad, E., & Shadmehr, R. (2023) · eLife · doi:10.7554/elife.87238.3 · Cited by 9
Damme, K. S. F., Kelley, N. J., Quinn, M. E., Glazer, J. E., Chat, I. K.-Y., Young, K. S., Nusslock, R., Zinbarg, R. E., Bookheimer, S. Y., & Craske, M. G. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00712-4 · Cited by 13
“when working for reward becomes tiring (Gatzke-Kopp et al., 2018; Klein-Flügge et al., 2015).”
BackgroundValue Analysis determines when and how to strive
Cao, S., Chen-Chen, T. A. N. G., Wu, H., & Liu, X. (2022) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2022.00877 · Cited by 8
Asymmetric valuation of gains and losses in effort-based decision making
O’Brien, M. K., & Ahmed, A. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223268 · Cited by 10
“Furthermore, recent work in decision making has demonstrated the presence of effort discounting and nonlinear sensitivity to increasing effort [19, 28, 29], and subjective effort costs are not exclusively related to energy expenditure [30, 31].”
BackgroundA common alteration in effort-based decision-making in apathy, anhedonia, and late circadian rhythmPreprint
Mehrhof, S. Z., & Nord, C. L. (2024) · eLife · doi:10.7554/elife.96803 · Cited by 11
Mehta, M., Butler, G., Ahn, C., Whitaker, Y. I., Bachi, K., Jacob, Y., Treadway, M. T., Murrough, J. W., & Morris, L. S. (2024) · Psychological Medicine · doi:10.1017/s0033291724002022 · Cited by 7
Escobar, G. G., & Mitchell, S. H. (2025) · Judgment and Decision Making · doi:10.1017/jdm.2025.10009 · Cited by 10
Effort and boredom shape our experience of time
Wolff, W., Özay-Otgonbayar, S., Danckert, J. A., Bieleke, M., & Martarelli, C. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106375 · Cited by 10
Toro‐Serey, C., Kane, G. A., & McGuire, J. T. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00972-z · Cited by 7
Effects of Procedure and Effort Type on Data Systematicity and the Rate of Effort Discounting
Macías-Navarrete, R., & dos Santos, C. V. (2023) · The Psychological Record · doi:10.1007/s40732-023-00538-0 · Cited by 7
Effort-based decision making varies by smoking status
Addicott, M. A., Wardle, M. C., & Selig, J. P. (2020) · Psychopharmacology · doi:10.1007/s00213-019-05437-3 · Cited by 8
“Effort expenditure is costly and rewards may become devalued if the effort cost to obtain them is too high (Klein-Flugge et al. 2015; Inzlicht et al. 2018).”
Smith, E. S., & Peters, J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010096 · Cited by 6
“[49, 50] In the present study, the participants applied more force in trials with higher value differences, and in particular a higher subjective value sum of the options.”
Effort cost of harvest affects decisions and movement vigor of marmosets during foraging
Hage, P., Jang, I. K., Looi, V., Fakharian, M. A., Orozco, S. P., Pi, J. S., Sedaghat-Nejad, E., & Shadmehr, R. (2023) · eLife · doi:10.7554/elife.87238 · Cited by 7
Modeling Dynamic Allocation of Effort in a Sequential Task Using Discounting Models
Rivera, D. C., Strobel, A., Goschke, T., & Kiebel, S. J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00242 · Cited by 4
“See also (Talmi and Pine, 2012; Klein-Flügge et al., 2015; Białaszek et al., 2017) for comparisons between these different models.”
BackgroundResultInfluentialDoes decision confidence reflect effort?
Hagura, N., Esmaily, J., & Bahrami, B. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278617 · Cited by 6
Risk inherent in delay accounts for magnitude effects in intertemporal decision making
Białaszek, W., Marcowski, P., & Ostaszewski, P. (2018) · Current Psychology · doi:10.1007/s12144-018-0092-4 · Cited by 6
“Although less frequently used, one solution might be to use other adaptive procedures that allow participants to go beyond the outcome nominal value, such as Parameter Estimation by Sequential Testing (PEST; Klein-Flügge et al. 2015; Taylor and Creelman 1967).”
MethodAgeing increases prosocial motivation for effortPreprint
Lockwood, P. L., Abdurahman, A., Gabay, A. S., Drew, D., Tamm, M., Husain, M., & Apps, M. A. J. (2020) · doi:10.31234/osf.io/8c5ra · Cited by 7
Shah, D., Kames, E., Pelham, J., & Morkos, B. W. (2022) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4053801 · Cited by 6
Explicit effort may not influence perceptuomotor decision-making
Manzone, J. X., & Welsh, T. N. (2023) · Experimental Brain Research · doi:10.1007/s00221-023-06710-w · Cited by 5
“While effort/cost is commonly conceptualized as decreasing the value associated with a given entity (Botvinick et al. 2009; Klein-Flügge et al. 2015), there are instances in which energy minimization is not prioritized and instances in which effort may increase the subjective value of an entity.”
Background
Metadata
- Authors
- Klein-Flügge, M. C., Kennerley, S. W., Saraiva, A. C., Penny, W. D., & Bestmann, S.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- UCL Queen Square Institute of Neurology (GB)
- Corresponding author
- Klein-Flügge, Miriam C.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-03-27
- Volume
- 11
- Issue
- 3
- Pages
- e1004116
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4376637
- OpenAlex
- W2087839453
- Cited by
- 163
- References
- 104
- Citations by year
- 2026: 17 · 2025: 15 · 2024: 12 · 2023: 17 · 2022: 16 · 2021: 22 · 2020: 13 · 2019: 12 · 2018: 15 · 2017: 16 · 2016: 7 · 2015: 1
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Update notices
- correction (2017-02-27)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Wellcome Trust (091593, 091593/Z/10/, 096689/Z/11/Z, /Z/11/Z, 096689, 086120, 086120/Z/08/Z)
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- effort discounting, delay discounting, reward devaluation, hyperbolic discounting, depression, Bayesian model selection, human choice behavior
- MeSH terms
- Adult, Choice Behavior, Female, Humans, Male, Models, Biological, Reward, Task Performance and Analysis, Hand Strength, Computational Biology, Young Adult
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