Effects of mental fatigue and attentional focus on muscular endurance performance
Bibliographic record
Abstract
Mental fatigue shows consistent detrimental effects on physically-demanding tasks including running and muscular endurance tasks. Several strategies to mitigate these effects have been assessed; however, it is also important to explore factors that may combine with mental fatigue to amplify performance decrements. The present study investigated potential moderating effects of attentional focus on the mental fatigue – endurance performance relationship. Undergraduates (N = 78) completed two wall-sit tasks to volitional failure prior to, and following, a cognitive task. Using a 2 X 2 randomised factorial design, half the participants completed a mentally fatiguing cognitive task (incongruent Stroop; HF), while the other half performed a non-mentally fatiguing task (documentary viewing; LF). Prior to the second wall-sit task, half the participants in each of the HF and LF groups were given attentional focus instructions to focus internally (INT) and the other half to focus externally (EXT). Both the mental fatigue (p < .001) and attentional focus (p < .001) manipulations were found to be successful. Planned comparisons showed no significant effects of attentional focus or mental fatigue on wall-sit endurance performance. However, a large effect size was observed between the HF/INT group and the other three groups (d = −1.56). Overall, data indicate a potential moderating effect of attentional focus such that internal focus and high mental fatigue may combine to have detrimental effects on physical endurance performance.
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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".