Are observed effects of movement simulated during motor imagery performance?
Bibliographic record
Abstract
Motor learning relies on adjusting the performance of movements via error detection and correction. How motor learning proceeds via motor imagery, the imagination of movement, is not understood. Motor imagery-based learning is thought to rely on comparing the predicted effect of movement, resulting from the forward model, against its intended effect. Whether motor imagery-based learning uses the observed effect of movement, simulated in motor imagery, to make comparisons to the intended effect to permit error detection and correction, is an open question. To address this, transcranial magnetic stimulation was used to inhibit the left inferior parietal lobe (L_IPL) after each trial of a task requiring participants to reproduce complex trajectories via motor imagery. From past work, we speculated the L_IPL was a candidate for integrating simulated feedback about task performance (simulated observed effects), hypothesizing inhibition of the L_IPL would impair learning, suggesting simulated observed effects of movement are used in motor imagery-based learning. Participants received stimulation to the L_IPL or over the vertex of the head after each trial. Learning was defined as reduced error on a repeated trajectory in comparison to randomly generated trajectories. Regardless of group participants learned, a finding countering our hypothesis, suggesting (a) observed effects of movement are not simulated in motor imagery; (b) the L_IPL is not involved in integrating simulated observed effects of movement; or (c) the timing of the stimulation did not align with the speculated role of the L_IPL. Results encourage further research probing simulated feedback in motor imagery and its neural correlates. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".