The Effects of Self-Control Fatigue on Dynamic Task Performance
Bibliographic record
Abstract
Mental fatigue can lead to decreases in subsequent physical performance (e.g. Pageaux et al., 2018; Van Cutsem et al., 2017). The aim of this study was to assess the effects of self-control fatigue on displacement and upper extremity muscle activity during three different cyclic push-pull tasks. A randomized crossover design over 2 sessions was used. In each session, 22 participants completed either 12 minutes of the Stroop task or watched a documentary before performing the cyclic bimanual push-pull tasks (with the other condition in the next session). The physical tasks include (1) bimanual push-pull, (2) bimanual reciprocal push-pull, and (3) bimanual push-pull with 30% grip force on the right side. Each task was performed for 60 s and repeated 3 times. A metronome was set at 60 beats per minute to ensure that each cycle was 2 s (1 s push, 1 s pull). A target distance of 80% of standing reach was used. Two potentiometers and a dynamometer on the right side was used to measure displacement and grip force respectively. Surface electromyography was sampled bilaterally from the anterior deltoid, posterior deltoid, biceps brachii, triceps brachii, wrist flexors, and wrist extensors. Ratings of perceived exertion and self-control fatigue levels were collected at baseline and following each trial. The results revealed significantly higher levels of self-control fatigue following the Stroop condition compared to the control condition. Participants only reported higher levels of perceived exertion in the third task following the Stroop condition. Peak displacement was consistent across tasks and conditions. A significant increase in the right posterior deltoid (1.8%) and a decrease in the left triceps brachii (-0.8%) muscle activity was observed in task 1. Increases in the right posterior deltoid (4.9%), left anterior deltoid (0.6%), and right wrist extensors (3.1%) muscle activity was observed in task 2. Increased muscle activity in the right wrist extensors (16.7%), left anterior deltoid (1.4%) and right posterior deltoid (5.7%) muscle activity was observed in task 3. Despite no apparent differences in task performance detected by the potentiometers, there were significant changes in muscle activity across the upper extremity, especially in the wrist extensors. Monitoring performance may not be the most efficient way to detect mental fatigue in ergonomic analyses, as masked changes in muscle activity could be a risk factor for occupational injuries.
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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 | 0.002 |
| 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".