Prolonged cognitive activity increases perception of fatigue but does not influence perception of effort, affective valence, or performance during subsequent isometric endurance exercise.
Bibliographic record
Abstract
Mental fatigue has previously been reported to impair subsequent endurance performance. However, uncertainty regarding the veracity of this effect remains. Moreover, the effect of heightened perception of fatigue on common perceptual (i.e. effort) and affective modulators of endurance performance also requires further study. The present study therefore examined two hypotheses: (1) that mental fatigue impairs muscular endurance performance and (2) that the perception of fatigue evoked through sustained cognitive exertion is related to the degree of effort and affect reported during a subsequent endurance task. Eighteen participants performed an exhaustive, submaximal (20% maximal voluntary contraction, MVC) isometric contraction of the right knee extensor muscles, following both a protracted high and low cognitively demanding task. The high cognitive demand task (HIGH) evoked greater perceptions of fatigue and poorer cognitive performance. However, both endurance performance and perceptions of effort and affect recorded during the endurance task were not different between cognitive tasks. Significant correlations were found between perceptions of fatigue and both perceived effort and affective responses. In conclusion, mental fatigue did not impair muscular endurance performance nor influence perceived effort and affective responses. The observed relationships offer some support to the idea that fatigue perception reflects an overarching controller of performance regulatory processes. However, the absence of direct performance effects may indicate that fatigue perception following cognitive vs. physical exertion may emerge through distinct processes.
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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 | 0.000 | 0.001 |
| 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".