Perception of effort and the allocation of physical resources: A generalization to upper-limb motor tasks
Bibliographic record
Abstract
Purpose: The perception of effort (PE) is widely used to prescribe and monitor exercise during locomotor and resistance tasks. The present study examines the validity of PE to prescribe and monitor exercise during upper-limb motor tasks under various loads and speed requirements. Methods: Forty participants volunteered in two experiments. In experiment 1, we used four PE intensities to prescribe exercise on a modified version of the box and block test (BBT) and a pointing task. We investigated the possibility of monitoring the exercise intensity by tracking changes in PE rating in response to three different tempos or additional weights. Experiment 2 replicated the possibility of prescribing the exercise with the PE intensity during the BBT and explored the impact of additional weights on performance and PE during the standardized version of the BBT. Muscle activation, heart rate, and respiratory frequencies were recorded. Results: In experiment 1, increasing the PE intensity to prescribe exercise induced an increased performance between each intensity. Increasing task difficulty with faster movement tempo and adding weight on the forearm increased the rating of PE. Experiment 2 replicated the possibility to use PE intensity for exercise prescription during the BBT. When completing the BBT with an additional weight on the forearm, participants maintained performance at the cost of a higher PE. In both experiments, changes in PE were associated with changes in muscle activation. Conclusion: Our results suggest that PE is a valid tool to prescribe and monitor exercise during upper-limb motor tasks.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".