Leveraging the effector independent nature of motor imagery when it is paired with physical practice
Bibliographic record
Abstract
While considered analogous to physical practice, the nature of imagery-based skill acquisition-specifically whether or not both effector independent and dependent encoding occurs through motor imagery-is not well understood. Here, motor imagery-based training was applied prior to or after physical practice-based training to probe the nature of imagery-based skill acquisition. Three groups of participants (N = 38) engaged in 10 days of training of a dart throwing task: 5 days of motor imagery prior to physical practice (MIP-PP), motor imagery following physical practice (PP-MIP), or physical practice only (PP-PP). Performance-related outcomes were assessed throughout. Brain activity was measured at three time points using fMRI (pre/mid/post-training; MIP-PP and PP-MIP groups). In contrast with physical practice, motor imagery led to changes in global versus specific aspects of the movement. Following 10 days of training, performance was greater when motor imagery preceded physical practice, although remained inferior to performance resulting from physical practice alone. Greater activation of regions that support effector dependent encoding was observed mid-, but not post-training for the PP-MIP group. Findings indicate that changes driven by motor imagery reflect effector independent encoding, providing new information regarding how motor imagery may be leveraged for skill acquisition.
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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 | 0.001 |
| 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) | 0.002 | 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".