Is the juice worth the squeeze? Learning the marginal value of mental effort over time
Bibliographic record
Abstract
In keeping with the view that individuals invest cognitive effort in accordance with its relative costs and benefits, reward incentives typically improve performance in tasks that require cognitive effort. At the same time, increasing effort investment may confer larger or smaller performance benefits—i.e., the marginal value of effort—depending on the situation, or context. On this view, we hypothesize that the magnitude of reward-induced effort modulations should depend critically on the marginal value of effort for the given context, and furthermore, the marginal value of effort of a context should be learned over time as a function of direct experience in the context. Using two well-characterized cognitive control tasks and simple computational models, we demonstrate that individuals appear to learn the marginal value of effort for different contexts. In a task-switching paradigm (Experiment 1), we found that participants initially exhibited reward-induced switch cost reductions across contexts—here, task switch rates—but over time learned to only increase effort in contexts with a comparatively larger marginal utility of effort. Likewise, in a Flanker task (Experiment 2), we observed a similar learning effect across contexts defined by the proportion of incongruent trials. Together, these results enrich theories of cost-benefit effort decision-making by highlighting the importance of the (learned) marginal utility of cognitive effort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".