Effects of External Pacing Type on the Cross-Education of Motor Skill
Bibliographic record
Abstract
Cross-education (CE) is a phenomenon whereby motor training of one limb leads to improved performance in the opposite untrained limb. External pacing of a motor task can enhance CE; however, the influence of different pacing methods is poorly understood. This study explored how motor training with auditory (AP) and visual pacing (VP) impacts CE with a visuomotor force target task. Sixty-one participants performed a unimanual motor task. Participants were randomized into a visual (n = 31) or auditory (n = 30) pacing stimuli condition. The primary outcome was cumulative error scores for each hand, before and after visuomotor training. Pacing type did not yield different magnitudes of CE. However, after adjusting for baseline differences, a significant hand (trained vs. untrained) × practice side (dominant or non-dominant) interaction (p = .013, ηp2 = .106) and a group main effect (p = .036, ηp2 = .165) were observed. Visual pacing resulted in greater improvements in task performance compared to auditory pacing regardless of hand or practice side, while training the dominant limb resulting in a greater interlimb asymmetry regardless of pacing stimulus. These findings have implications for applying pacing strategies during rehabilitation from unilateral injury or neurological impairment.
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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) | 0.003 | 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".