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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Controlling the temporal parameters of both the cognitive and physical effort tasks eliminated delay discounting as an explanation of choice behaviour.
“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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Neurocomputational mechanisms underlying subjective valuation of effort costs
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A Representation of Effort in Decision-Making and Motor Control
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Klein-Flügge, M. C., Kennerley, S. W., Friston, K. J., & Bestmann, S. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0292-16.2016 · Cited by 247 · see citing works
Self-citationWhy not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases
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Prosocial apathy for helping others when effort is required
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Anhedonia in depression: biological mechanisms and computational models
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Reward Processing, Neuroeconomics, and Psychopathology
Zald, D. H., & Treadway, M. T. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044957 · Cited by 181 · see citing works
The role of cognitive effort in subjective reward devaluation and risky decision-making
Apps, M. A. J., Grima, L. L., Manohar, S., & Husain, M. (2015) · Scientific Reports · doi:10.1038/srep16880 · Cited by 145 · see citing works
Separate mesocortical and mesolimbic pathways encode effort and reward learning signals
Hauser, T. U., Eldar, E., & Dolan, R. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705643114 · Cited by 135 · see citing works
Quantifying motivation with effort-based decision-making paradigms in health and disease
Chong, T. T. ‐., Bonnelle, V., & Husain, M. (2016) · Progress in brain research · doi:10.1016/bs.pbr.2016.05.002 · Cited by 131 · see citing works
Computational modelling of social cognition and behaviour—a reinforcement learning primer
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Self-citationCan’t or Won’t? Immunometabolic Constraints on Dopaminergic Drive
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Aging Increases Prosocial Motivation for Effort
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Dopamine restores cognitive motivation in Parkinson’s disease
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Advances in modeling learning and decision-making in neuroscience
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Neural mechanisms for learning self and other ownership
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Self-citationSubjective value representations during effort, probability and time discounting across adulthood
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Sleep deprivation increases the costs of attentional effort: Performance, preference and pupil size
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Cognitive and behavioral training interventions to promote self-control.
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Why Don't We Move Slower? The Value of Time in the Neural Control of Action
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Cognitive Control as Cost‐Benefit Decision Making
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Forced choices reveal a trade-off between cognitive effort and physical pain
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Distinct neural representations for prosocial and self-benefiting effort
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Quantifying the Motivational Effects of Cognitive Fatigue Through Effort-Based Decision Making
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The effort-doors task: Examining the temporal dynamics of effort-based reward processing using ERPs
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Fatigue increases the perception of future effort during decision making
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The Neurobiology of Pathological Fatigue: New Models, New Questions
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Cognitive effort exertion enhances electrophysiological responses to rewarding outcomes
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Computational mechanisms underlying the dynamics of physical and cognitive fatigue
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Pharmacological evidence for the implication of noradrenaline in effort
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Sleep deprivation, effort allocation and performance
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The role of dopamine in dynamic effort-reward integration
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Heightened effort discounting is a common feature of both apathy and fatigue
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Dopamine-Dependent Loss Aversion during Effort-Based Decision-Making
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Disordered Decision Making: A Cognitive Framework for Apathy and Impulsivity in Huntington’s Disease
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A Neurocomputational Model for Intrinsic Reward
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Need for Cognition is associated with a preference for higher task load in effort discounting
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The Neurobiology of Cognitive Fatigue and Its Influence on Effort-Based Choice
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Methylphenidate increases willingness to perform effort in adults with ADHD
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Neurocomputational mechanisms underlying the subjective value of information
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Body dynamics of gait affect value-based decisions
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Different depression: motivational anhedonia governs antidepressant efficacy in Huntington’s disease
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Dopamine and acetylcholine have distinct roles in delay- and effort-based decision-making in humans
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Impulsivity and Active Inference
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Shielding working memory from distraction is more effortful than flexible updating
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A computational account of why more valuable goals seem to require more effortful actions
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Expending effort may share neural responses with reward and evokes high subjective satisfaction
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Reward processing and reinforcement learning: from adolescence to agingPreprint
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Strengths of social ties modulate brain computations for third-party punishment
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Effort-reward balance and work motivation in rats: Effects of context and order of experience
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Understanding Motivation with the Progressive Ratio Task: a Hierarchical Bayesian Model
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Effort cost of harvest affects decisions and movement vigor of marmosets during foraging
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Value Analysis determines when and how to strive
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Asymmetric valuation of gains and losses in effort-based decision making
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A common alteration in effort-based decision-making in apathy, anhedonia, and late circadian rhythmPreprint
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Effort and boredom shape our experience of time
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Effects of Procedure and Effort Type on Data Systematicity and the Rate of Effort Discounting
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Effort-based decision making varies by smoking status
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Effort cost of harvest affects decisions and movement vigor of marmosets during foraging
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Modeling Dynamic Allocation of Effort in a Sequential Task Using Discounting Models
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Does decision confidence reflect effort?
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Risk inherent in delay accounts for magnitude effects in intertemporal decision making
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Ageing increases prosocial motivation for effortPreprint
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Explicit effort may not influence perceptuomotor decision-making
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- 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
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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