Perceived fatigue does not alter effort-based decision making but does undermine confidence in the ability to perform physical actions
Bibliographic record
Abstract
Fatigue may affect the decision to deploy effort (cost) for a given rewarded outcome (benefit). However, it remains unclear whether these fatigue-associated changes can be attributed to simply feeling fatigued. To investigate this question, twenty-two healthy males made a series of choices between two rewarded options: a fixed, no effort option, where no physical effort was required to obtain a set, low reward vs. a variable, effortful option, in which both the physical effort requirements (i.e. different durations of a sustained contraction performed in the lower limbs) and rewards (i.e. greater monetary incentives) was varied. Effort-based choices were made under two conditions: 1) a rested (control) state, (2) a pre-induced state of perceived fatigue, evoked through physical exertion in the upper-limbs, thus controlling for the physical manifestation of fatigue (i.e. decline in muscular force production) on the decision process. Though prior physical exertion increased the perception of fatigue, participants choice behaviour did not significantly differ between control and fatigue conditions. Across both conditions, participants demonstrated an anticipated aversion to effort, with greater effort requirements reducing the decision to engage in actions associated with higher rewards. However, in the fatigue state only, decision time was prolonged and self-reported confidence in individuals’ ability to perform high effort actions was reduced. The findings suggest that a perceived state of fatigue does not necessarily alter cost/benefit comparisons within effort-based decisions, but may introduce greater uncertainty within choice and reduce self-confidence. These findings evidence altered evaluative processes during decision making under conditions of fatigue.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".