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Toward a Rational and Mechanistic Account of Mental Effort
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In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits. This review explores recent advances in computational modeling and empirical research aimed at addressing these questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how we manage those limited cognitive resources. We conclude by identifying remaining challenges for theoretical accounts of mental effort as well as possible applications of the available findings to understanding the causes of and potential solutions for apparent failures to exert the mental effort required of us.
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“Consequently, modern theories of motivation increasingly describe motivational states as the output of a computation (calculation), where the expected value of an action is weighed against anticipated costs. These include energetic costs, as all goal pursuit will require the expenditure of metabolic energy, opportunity costs, as the pursuit of one goal might block progress towards another, hedonic”
From Motivation in the Service of Allostasis: The Role of Anterior Mid-Cingulate Cortex (2019) · cites this paper as Shenhav et al., 2017
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Metadata
- Authors
- Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M.
- Affiliations
- Brown University (US)
- Princeton University (US)
- University of California, Berkeley (US)
- Harvard University (US)
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Shenhav, Amitai
- ORCID
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2017-07-25
- Volume
- 40
- Issue
- 1
- Pages
- 99-124
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2600886565
- Cited by
- 1,077
- References
- 196
- Citations by year
- 2026: 124 · 2025: 122 · 2024: 149 · 2023: 117 · 2022: 123 · 2021: 124 · 2020: 112 · 2019: 117 · 2018: 60 · 2017: 23
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Social Sciences › Arts and Humanities › Philosophy › Free Will and Agency
- Keywords
- cognitive effort, cognitive control, effort cost, cognitive resource allocation, aversive experience, cognitive limitations
- MeSH terms
- Cognition, Decision Making, Humans, Motivation, Reward, Prefrontal Cortex, Executive Function
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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