Effects of cognitive effort exertion on physical sense of effort and force production
Bibliographic record
Abstract
Engaging in cognitively demanding tasks can lead to decreased physical performance and increased perceived effort. These negative carryover effects suggest there are common neurological determinants of effort sensation during cognitive and physical tasks. The purpose of this study was to explore the aftereffects of cognitive effort exertion on perceptions of effort, force production, and muscle activation during a physically-demanding task. Using a crossover design, participants (N=21) completed a 10-minute high-effort cognitive task and a low-effort cognitive task on separate days. On both days, the cognitive tasks were followed by an endurance handgrip trial in which participants squeezed a dynamometer at 15% of their maximum voluntary contraction (MVC) with their dominant hand until exhaustion. Additionally, participants intermittently (30-sec intervals) squeezed another dynamometer with their non-dominant hand to match the perceived effort required to sustain the endurance squeeze. Surface EMG measured forearm muscle activation in both arms and ratings of perceived exertion (RPE) were recorded prior to each effort-matching trial. Participants performed equally on the endurance trials (p=0.93, Cohen's d=0.02) with similar RPE throughout both trials (p=0.91, ES
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".