Skill acquisition via motor imagery relies on both motor and perceptual learning.
Bibliographic record
Abstract
Motor imagery (MI), the mental rehearsal of movement, is an effective means for acquiring a novel skill, even in the absence of physical practice (PP). The nature of this learning, be it perceptual, motor, or both, is not well understood. Understanding the mechanisms underlying MI-based skill acquisition has implications for its use in numerous disciplines, including informing best practices regarding its use. Here we used an implicit sequence learning (ISL) task to probe whether MI-based skill acquisition can be attributed to perceptual or motor learning. Participants (n = 60) randomized to 4 groups were trained through MI or PP, and were then tested in either perceptual (altering the sensory cue) or motor (switching the hand) transfer conditions. Control participants (n = 42) that did not perform a transfer condition were utilized from previous work. Learning was quantified through effect sizes for reaction time (RT) differences between implicit and random sequences. Generally, PP-based training led to lower RTs compared with MI-based training for implicit and random sequences. All groups demonstrated learning (p < .05), the magnitude of which was reduced by transfer conditions relative to controls. For MI-based training perceptual transfer disrupted performance more than for PP. Motor transfer disrupted performance equally for MI- and PP-based training. Our results suggest that MI-based training relies on both perceptual and motor learning, while PP-based training relies more on motor processes. These results reveal details regarding the mechanisms underlying MI, and inform its use as a modality for skill acquisition. (PsycINFO Database Record
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".