Effects of mental fatigue on exercise decision-making
Bibliographic record
Abstract
People's decisions regarding effort-based tasks such as engaging in physical activity depend on the subjective value of the activity: weighing the costs against the benefits. Exerting cognitive effort while performing one task negatively biases people's decisions to exert effort on subsequent cognitive tasks, suggesting a shift in their subjective valuation of the task due to mental fatigue. The present study investigated the effect of mental fatigue on people's decisions to engage in an acute bout of exercise and whether the effect of mental fatigue on decision-making was mediated by a cost-benefit analysis. Participants (N = 55) completed either a 10-minute, high cognitive demand (Stroop) task or low cognitive demand (documentary viewing) task and then made a choice between engaging in a 20-minute self-paced moderate-to-vigorous intensity exercise task or a 20-minute sedentary task. Prior to choosing, participants rated their mental fatigue and their perceived benefits and costs of the exercise task. The cognitive task had a strong effect on mental fatigue (Cohen's d = 1.40). The mediation analysis showed no direct effect of mental fatigue on choice; however, there was a significant indirect effect indicating the cost-benefit score mediated the effect of mental fatigue on choice (95% C.I = -.02 to -.0004). Higher levels of mental fatigue were associated with greater costs of exercising (r = .35), which in turn were associated with a decreased likelihood of choosing the exercise task (r squared = .13). Findings provide insight into the effects of mental fatigue on people's exercise decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".