The Role of Parietal Cortex in Awareness of Self-generated Movements: a Transcranial Magnetic Stimulation Study
Bibliographic record
Abstract
Awareness of self-generated movements arises from comparing motor plans, and the accompanying (hypothetical) efference copy, with the visual and proprioceptive consequences of movement. Here we used repetitive transcranial magnetic stimulation (rTMS) to investigate the role of a posterior region in the superior parietal lobule (SPL) in this process. Nine healthy volunteers performed a finger extension actively and passively while wearing a CyberGlove; the glove recorded these (actual) finger movements and used this information in real time to move a virtual hand displayed on a computer screen. To assess the participant's awareness of movement onset, we introduced a delay between the onset of the actual and virtual movement (60-270 ms, 30 ms increments); the task was to judge whether the virtual hand movements were delayed relative to the actual hand movements. Low-frequency rTMS (15 min, 0.6 Hz) was applied either over the left SPL or the left temporal cortex (control site) to decrease excitability of these regions and, in turn, test their role in the awareness of self-generated movement. Following the SPL stimulation, participants' assessments of asynchrony were impaired for active but not passive movements. No significant changes were observed after rTMS applied over the control site. We suggest that these findings are consistent with the role of the SPL in evaluating the temporal congruency of peripheral (visual) and central (efference copy) signals associated with self-generated movements. As such, this region may contribute to the sense of 'agency' and its disturbances in disorders such as apraxia and schizophrenia.
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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".