Experience in sports and music influences implicit motor imagery
Bibliographic record
Abstract
Background. Motor imagery (MI) can be an effective strategy for learning and enhancing movement or as an alternative training modality when physical practice is compromised. Individual differences in MI ability are widely documented but the role of experience in different activities in influencing MI is not well understood. The present study examined how experience in activities associated with the use of MI influences implicit and explicit MI. Methods. Participants completed a hand laterality judgement task (N=125) and a MI questionnaire (MIQ-3; N=89) online. These implicit and explicit measures of MI were analysed in relation to frequent experience in individual sports/exercise, team sports, dance, and playing a musical instrument. Results. The majority of participants reported using both visual and kinesthetic MI within their activities. Across activities, frequent experience was associated with more accurate hand laterality judgement, as well as increased biomechanical constraint effects, particularly for hands viewed from the palm. In relation to the different activity types, significant effects were found for individual and team sports and music. No effects of experience were found for explicit MI (MIQ-3). Conclusion. Experience in activities that utilise MI influences implicit MI strategies more than explicit MI ability. Activity-specific effects on MI may reflect differences in kinesthetic and visual experience of the different hand surfaces.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".