Learning to minimize efforts versus maximizing rewards: computational principles and neural correlates
Open accessAbstract
The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scanned healthy human volunteers while they performed a probabilistic instrumental learning task that varied in both the physical effort and the monetary outcome associated with choice options. Behavioral data showed that the same computational rule, using prediction errors to update expectations, could account for both reward maximization and effort minimization. However, these learning-related variables were encoded in partially dissociable brain areas. In line with previous findings, the ventromedial prefrontal cortex was found to positively represent expected and actual rewards, regardless of effort. A separate network, encompassing the anterior insula, the dorsal anterior cingulate, and the posterior parietal cortex, correlated positively with expected and actual efforts. These findings suggest that the same computational rule is applied by distinct brain systems, depending on the choice dimension-cost or benefit-that has to be learned.
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Inactivation of medial prefrontal cortex impairs an animal's ability to overcome effort costs.
“In addition, single-unit studies have shown that dACC/dmPFC neurons signal the net value of rewards associated with effort, and the necessity of this region in cost–benefit valuation has been demonstrated by lesion studies that report that inactivation of medial prefrontal cortex impairs an animal’s ability to overcome effort costs [15, 16, 25, 48, 55].”
From Neurocomputational mechanisms underlying subjective valuation of effort costs (2017) · cites this paper as [15]
dACC/dmPFC neurons signal the net value of rewards associated with effort.
“In addition, single-unit studies have shown that dACC/dmPFC neurons signal the net value of rewards associated with effort, and the necessity of this region in cost–benefit valuation has been demonstrated by lesion studies that report that inactivation of medial prefrontal cortex impairs an animal’s ability to overcome effort costs [15, 16, 25, 48, 55].”
From Neurocomputational mechanisms underlying subjective valuation of effort costs (2017) · cites this paper as [15]
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Neurocomputational mechanisms underlying subjective valuation of effort costs
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Self-citation
- Authors
- Skvortsova, V., Palminteri, S., & Pessiglione, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2014-11-19
- Volume
- 34
- Issue
- 47
- Pages
- 15621-15630
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6608437
- OpenAlex
- W1986178633
- Cited by
- 177
- References
- 84
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 13 · 2023: 10 · 2022: 14 · 2021: 16 · 2020: 27 · 2019: 11 · 2018: 23 · 2017: 13 · 2016: 15 · 2015: 8
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-10-IAIHU06, 10-IAIHU-06, ANR-10)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- effort minimization, reward maximization, prediction error, ventromedial prefrontal cortex, anterior insula, dorsal anterior cingulate cortex, posterior parietal cortex, neural correlates of learning
- MeSH terms
- Adolescent, Adult, Choice Behavior, Computer Simulation, Cues, Physical Exertion, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Models, Neurological, Reward, Young Adult
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