Increasing the cognitive demand of upper-limb psychomotor tasks increases the perception of effort
Bibliographic record
Abstract
The motivation intensity theory suggests that the perception of effort (PE) reflects the self-monitoring of resources and is used to self-regulate goal-directed actions. These processes are typically studied separately in the motor or cognitive domain. Here, we tested the effect of cognitive demand on PE in two psychomotor tasks (experiment 1) and we examined the effect of prescribing a target level of PE intensity on performance (experiment 2).In experiment 1 (n=20), we used fixed tempo during two upper-limb psychomotor tasks (Box and Block test and pointing task) with PE as dependent variable. In experiment 2 (n=20), we used fixed PE during a self-paced pointing task with PE as independent variable. In both experiments, heart rate, respiratory rate and electromyographic signal of biceps and triceps brachii were monitored. Cognitive demand was manipulated (low, moderate, high) via a Stroop task, determining which block to move or target to reach.Results showed that in experiment 1, PE increased with increased cognitive demand to maintain performance in both tasks. In experiment 2, higher cognitive demand during self-paced pointing at fixed PE led to decreased performance. Heart rate proved most sensitive to cognitive demand changes among physiological variables but failed to reflect the increase in cognitive demand from low to moderate in experiment 1.These results confirm the possibility of using the PE to prescribe and monitor the intensity of a psychomotor task. This study opens perspectives to investigate the unicity of effort and challenge the assumed duality of cognitive vs motor effort.
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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 | 0.002 |
| 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".