Optimizing Motor Learning: Difficulty Manipulation Combined with Feedback- Frequency Enhance Under-Time-Pressure Fine-Motor-Coordination Skill Acquisition and Retention
Bibliographic record
Abstract
Improving acquisition and retention of new motor skills is of great importance. This study investigated the effects of progressive task difficulty manipulation (TD), combined with varying knowledge of results frequencies (KR) on performance accuracy and consistency when learning novel fine motor coordination tasks, and examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical-education students (age = 10.72 ± 0.89 years) participated; participants were separated into three groups, receiving varying KR frequency (100%KR, 50%KR, and 33%KR). For each group, distance to the target was increased progressively (2 m, 2.37 m, and 3.56 m) to obtain three difficulty levels. We assessed performance during test sessions (pretest, post-test, Retention1 and Retention2) under free (FC) and time pressure (TPC) conditions. Results revealed that under FC, 100%KR improved significantly. Results revealed significant linear improvements in accuracy for 50%KR and 33%KR under TPC. New findings indicate that the association between TD and KR (50%KR) may provide more appropriate cognitive loads compared to 33%KR and 100%KR groups. These have implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be based on the characteristics of the learners.
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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 | 0.001 |
| 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) | 0.001 | 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".