The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Mental labour
Abstract
Mental effort is an elementary notion in our folk psychology and a familiar fixture in everyday introspective experience. However, as an object of scientific study, mental effort has remained rather elusive. Cognitive psychology has provided some tools for understanding how effort impacts performance, by linking effort with cognitive control function. What has remained less clear are the principles that govern the allocation of mental effort. Under what circumstances do people choose to invest mental effort, and when do they decline to do so? And what regulates the intensity of mental effort when it is applied? In new and promising work, these questions are being approached with the tools of behavioural economics. Though still in its infancy, this economic approach to mental effort research has already uncovered important aspects of effort-based decision-making, and points clearly to future lines of inquiry, including some intriguing opportunities presented by recent artificial intelligence research.
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“Our friend Prudence makes us do more work, which people prefer to avoid [19], so we may find that participants prefer less work, but with worse results Buçinca et al. [1].”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [19]
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Cited by
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Integrating Models of Self-Regulation
Inzlicht, M., Werner, K. M., Briskin, J. L., & Roberts, B. W. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-061020-105721 · Cited by 594
Explanations Can Reduce Overreliance on AI Systems During Decision-Making
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BackgroundInfluentialMotivation and cognitive control in depression
Grahek, I., Shenhav, A., Musslick, S., Krebs, R. M., & Koster, E. H. W. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.04.011 · Cited by 389
Inductive biases for deep learning of higher-level cognition
Goyal, A., & Bengio, Y. (2022) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2021.0068 · Cited by 300
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Vu, T., Magis-Weinberg, L., Jansen, B. R. J., van Atteveldt, N., Janssen, T. W. P., Lee, N. C., van der Maas, H. L. J., Raijmakers, M. E. J., Sachisthal, M. S. M., & Meeter, M. (2022) · Educational Psychology Review · doi:10.1007/s10648-021-09616-7 · Cited by 224
Metadata
- Authors
- Kool, W., & Botvinick, M.
- Affiliations
- Harvard University (US)
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Botvinick, Matthew
- Venue
- Nature Human Behaviour
- Published
- 2018-09-03
- Volume
- 2
- Issue
- 12
- Pages
- 899-908
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W4249127163
- Cited by
- 288
- References
- 85
- Citations by year
- 2026: 61 · 2025: 51 · 2024: 33 · 2023: 36 · 2022: 40 · 2021: 34 · 2020: 22 · 2019: 9
- FWCI
- 1.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- cognitive effort, effort allocation, effort-based decision making, behavioral economics, cognitive control, cost-benefit trade-off, effort intensity, artificial intelligence
- MeSH terms
- Cognition, Decision Making, Humans, Reward, Work, Economics, Behavioral
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