Skill acquisition via motor imagery relies on both motor and perceptual learning.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it