Experience modulates motor imagery‐based brain activity
Bibliographic record
Abstract
Whether or not brain activation during motor imagery (MI), the mental rehearsal of movement, is modulated by experience (i.e. skilled performance, achieved through long-term practice) remains unclear. Specifically, MI is generally associated with diffuse activation patterns that closely resemble novice physical performance, which may be attributable to a lack of experience with the task being imagined vs. being a distinguishing feature of MI. We sought to examine how experience modulates brain activity driven via MI, implementing a within- and between-group design to manipulate experience across tasks as well as expertise of the participants. Two groups of 'experts' (basketball/volleyball athletes) and 'novices' (recreational controls) underwent magnetoencephalography (MEG) while performing MI of four multi-articular tasks, selected to ensure that the degree of experience that participants had with each task varied. Source-level analysis was applied to MEG data and linear mixed effects modelling was conducted to examine task-related changes in activity. Within- and between-group comparisons were completed post hoc and difference maps were plotted. Brain activation patterns observed during MI of tasks for which participants had a low degree of experience were more widespread and bilateral (i.e. within-groups), with limited differences observed during MI of tasks for which participants had similar experience (i.e. between-groups). Thus, we show that brain activity during MI is modulated by experience; specifically, that novice performance is associated with the additional recruitment of regions across both hemispheres. Future investigations of the neural correlates of MI should consider prior experience when selecting the task to be performed.
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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".