Evidence for the dependence of visual and kinesthetic motor imagery on isolated visual and motor practice
Bibliographic record
Abstract
Motor imagery (MI) is a cognitive process believed to rely on the representation developed through task-specific experience. Despite ideas about the equivalence between MI and execution, the relationship between visual-motor experiences and MI ability is unclear. Here we evaluated how distinct experiences (i.e., no-vision physical and observational practice) impact visual and kinesthetic MI ability. Participants (N = 66) were randomized into three groups; no-vision physical practice, observational practice and no-practice control. Participants practiced and then visually and kinesthetically imagined two hand gesture sequences. Mental chronometry, a movement time (MT) congruency measure, and MI quality ratings were used to assess MI. As predicted, physical practice produced higher ratings for kinesthetic MI and observational practice elicited higher ratings for visual MI. However, physical practice did not result in greater temporal congruency between imagined/executed MTs in comparison to other groups. We conclude that MI is only partially tied to the motor representation as physical practice was not essential for enhancing MI quality. The motor representation developed with no-vision practice improved perceptions of kinesthetic MI, but without the expected congruence in timing, questioning the equivalence between execution and MI.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".