ArticleCerebral cortex (New York, N.Y. : 1991)2023
Learning when effort matters: neural dynamics underlying updating and adaptation to changes in performance efficacy.
Article in Cerebral cortex (New York, N.Y. : 1991), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Motivation to exert effort dynamically adapts to reward context and environmental controllability.Cognitive, affective & behavioral neuroscience · 2026Article
- A Hitchhiker's Guide to Physical and Cognitive Effort.Sports medicine (Auckland, N.Z.) · 2026Review
- The perils of omitting omissions when modeling evidence accumulation.PLoS computational biology · 2026Article
- Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.Psychological review · 2026Article
- The neurocomputational mechanisms underlying the impact of social comparison on effort investment.Communications biology · 2026Article
- Exerting effort for non-instrumental information under risk.Scientific reports · 2026Article
- Understanding human metacontrol and its pathologies using deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Neurocomputational mechanisms underlying the distinct motivational influences of reward and punishment on cognitive control.bioRxiv : the preprint server for biology · 2025Article
- Time Spent Thinking in Online Chess Reflects the Value of Computation.Cognitive science · 2025Article
- Single-Neuron Encoding of Learnability in the Dorsal Anterior Cingulate Cortex.bioRxiv : the preprint server for biology · 2025Article
- Slower transitions between control states lead to reductions in cognitive flexibility over the lifespan.bioRxiv : the preprint server for biology · 2025Article
- Investigating the Effectiveness of Self-Regulated, Pomodoro, and Flowtime Break-Taking Techniques Among Students.Behavioral sciences (Basel, Switzerland) · 2025Article
- Characterizing age-related change in learning the value of cognitive effort.Journal of experimental psychology. General · 2025Article
- The integration of self-efficacy and response-efficacy in decision making.Scientific reports · 2025Article
- Humans rationally balance detailed and temporally abstract world models.Communications psychology · 2025Article
- Reward and Efficacy Modulate the Rate of Anticipatory Pupil Dilation.Psychophysiology · 2025Article
- Neural and Computational Mechanisms of Motivation and Decision-making.Journal of cognitive neuroscience · 2024Article
- Learning how to reason and deciding when to decide.The Behavioral and brain sciences · 2023Article
- Decomposing the motivation to exert mental effort.Current directions in psychological science · 2021Article
- Expectations of reward and efficacy guide cognitive control allocation.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
To determine how much cognitive control to invest in a task, people need to consider whether exerting control matters for obtaining rewards. In particular, they need to account for the efficacy of their performance-the degree to which rewards are determined by performance or by independent factors. Yet it remains unclear how people learn about their performance efficacy in an environment. Here we combined computational modeling with measures of task performance and EEG, to provide a mechanistic account of how people (i) learn and update efficacy expectations in a changing environment and (ii) proactively adjust control allocation based on current efficacy expectations. Across 2 studies, subjects performed an incentivized cognitive control task while their performance efficacy (the likelihood that rewards are performance-contingent or random) varied over time. We show that people update their efficacy beliefs based on prediction errors-leveraging similar neural and computational substrates as those that underpin reward learning-and adjust how much control they allocate according to these beliefs. Using computational modeling, we show that these control adjustments reflect changes in information processing, rather than the speed-accuracy tradeoff. These findings demonstrate the neurocomputational mechanism through which people learn how worthwhile their cognitive control is.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.