Perception of effort decreases with motor sequence learning
Bibliographic record
Abstract
Motor learning is proposed to be associated with a minimization or optimization of physical and cognitive resources. Effort involves the voluntary investment of resources for task performance. While these two constructs are intrinsically linked, their relationship has never been empirically examined. This study aimed to investigate how the perception of effort evolves throughout the motor learning process. Thirty young adults volunteered in this study. Each participant had four visits to perform 10 blocks of a continuous tracking task on each visit. The sequences within these blocks were either random (control condition) or repeated (experimental condition). Following each block, participants rated the intensity of the effort invested to perform the task. Sequence-specific motor learning was observed, with the repeated outperforming the random sequence condition at the retention test. Perception of effort decreased only with sequence-specific motor learning, with a repeated measures correlation showing an association between these two variables. Our findings suggest that motor sequence learning reduces perception of effort. Thus, as sequence-specific task proficiency increases, individuals find the task less effortful. This link between learning a motor task and effort perception presents valuable opportunities for future research to investigate the behavioral and neural mechanisms underlying motor learning and 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 | 0.005 |
| 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".