Identifying optimal scheduling parameters for the application of motor imagery after physical practice to enhance learning
Bibliographic record
Abstract
While it is thought that motor imagery (MI), the mental rehearsal of a motor task may enhance consolidation when applied after physical practice (PP), little is known about the optimal scheduling of when MI should be applied after PP to improve motor learning. Here, we explore the effects of time between PP and MI sessions within a single day of practice. Participants (N = 15) were randomized into two groups and engaged in PP of a shape-tracing task involving random and repeated shapes. Participants then performed MI either one hour (1HR group) or six hours (6HR group) after PP. Physical test blocks were administered in a pre/post/retention (~24 hours after practice) design, with error (px) used to measure performance. Effect sizes (Cohen’s d) were calculated to quantify motor learning (pre minus retention) for each shape type (random, repeated). Motor learning related to random shapes did not occur in either group, as evidenced by negligible effect sizes (6HR: MPRE = 214.17±50.58, MRET = 216.82±30.16, d =0.05; 1HR: MPRE = 224.74±25.79, MRET = 223.38±24.94, d =0.05). Motor learning related to repeated shapes occurred for both groups, the 6HR group learned to a greater extent as evidenced by a large vs. moderate effect size (6HR: MPRE = 214.63±38.13, MRET = 179.59±41.21, d =0.92; 1HR: MPRE = 211.77±28.54, MRET = 193.58±19.78, d =0.63). Findings suggest that a larger time period between PP and MI (within a single day) enhanced learning, overall informing scheduling of MI and PP to enhance motor learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".