The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation
Open accessAbstract
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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Cites
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“Similarly, the probability with which reward is received is subjectively distorted such that small probabilities are over-weighted and large probabilities under-weighted [1,13].”
BackgroundInformation theory, inference, and learning algorithms
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“Our two main criteria to define a theoretically grounded model for effort discounting were (1) that it can take initially concave shapes because (a) intuition tells us that small efforts should have a relatively small or no impact on value, but large efforts should lead to steep discounting of rewards; (b) studies on…”
BackgroundResultInfluentialSubgenual prefrontal cortex abnormalities in mood disorders
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Research works printed but not linked: 1
- Meier S, Sprenger C. Present-Biased Preferences and Credit Card Borrowing [Internet]. Rochester, NY: Social Science Research Network; Report No.: ID 1412276. Available: http://papers.ssrn.com/abstract=1412276
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Neurocomputational mechanisms underlying subjective valuation of effort costs
Chong, T. T.-J., Apps, M., Giehl, K., Sillence, A., Grima, L. L., & Husain, M. (2017) · PLOS Biology · doi:10.1371/journal.pbio.1002598 · Cited by 415
“These functions have been used in several previous studies to fit effort discounting in the physical domain [5, 14, 33].”
BackgroundMethodA Representation of Effort in Decision-Making and Motor Control
Shadmehr, R., Huang, H. J., & Ahmed, A. A. (2016) · Current Biology · doi:10.1016/j.cub.2016.05.065 · Cited by 269
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 271
“We performed several behavioral experiments to develop a behavioral model that can formally describe the range of effort discounting behaviors observed in healthy populations (Klein-Flügge et al., 2015).”
BackgroundMethodResultInfluentialSelf-citationWhy not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases
Pessiglione, M., Vinckier, F., Bouret, S., Jean Daunizeau, A., & Bouc, R. L. (2017) · Brain · doi:10.1093/brain/awx278 · Cited by 255
“Indifference points can be determined through an adaptive design, such as a stair-case procedure, that increments effort and/or rewards levels based on observed choices (Westbrook et al., 2013; Klein-Flugge et al., 2015; Le Bouc et al., 2016).”
BackgroundMüller, T., & Apps, M. A. J. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.04.030 · Cited by 265
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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