Motor Imagery Ability is Associated with Action Prediction in a Volleyball-Based Task
Bibliographic record
Abstract
Despite evidence that motor imagery (MI) facilitates motor experience and refinement of a motor representation, the association between MI and creating anticipatory representations of a motor skill is unknown. Here, our aims were two-fold: 1) we sought to examine the effect of both kinesthetic and visual MI on action prediction (AP) in a sports context, when motor skills are observed from both the 1st- and 3rd-person perspective, and 2) we sought to explore whether individual differences in volleyball experience and MI ability are associated with action prediction. Participants (N = 116, 83 female, M = 2.60±3.70 years volleyball experience) were randomized into two groups (Control, MI). Both groups engaged in an online AP task which involved watching temporally occluded videos of volleyball serves (two Video Conditions: 1st- and 3rd-person perspective) and predicting where the ball would land. The MI Group additionally performed MI of the serve before making their prediction. Accuracy (proportion of correct responses) on the AP task was calculated across participants and Video Condition. Linear mixed effects modelling and associated effect sizes revealed that AP performance was greater when videos were viewed from the 3rd person perspective overall, and further enhanced for longer Occlusion Time Points. A PCA revealed three components (“Imagery Ability”, “Volleyball Specific Experience”, and “Non-sport Specific Experience”) explaining 61.6% of the variance. AP performance at longer Occlusion Time Points was found to be associated with the Imagery Ability component. Overall, MI ability was associated with greater AP performance in a sport-specific context when performed during the task. Further work is required to understand how expertise may modulate the association between MI and action prediction.
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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) | 0.003 | 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".