Stimulation of the Posterior Parietal Cortex Interferes with Arm Trajectory Adjustments during the Learning of New Dynamics
Bibliographic record
Abstract
Substantial neurophysiological evidence points to the posterior parietal cortex (PPC) as playing a key role in the coordinate transformation necessary for visually guided reaching. Our goal was to examine the role of PPC in the context of learning new dynamics of arm movements. We assessed this possibility by stimulating PPC with transcranial magnetic stimulation (TMS) while subjects learned to make reaching movements with their right hand in a velocity-dependent force field. We reasoned that, if PPC is necessary to adjust the trajectory of the arm as it interacts with a novel mechanical system, interfering with the functioning of PPC would impair adaptation. Single pulses of TMS were applied over the left PPC 40 msec after the onset of movement during adaptation. As a control, another group of subjects was stimulated over the visual cortex. During early stages of learning, the magnitude of the error (measured as the deviation of the hand paths) was similar across groups. By the end of the learning period, however, error magnitudes decreased to baseline levels for controls but remained significantly larger for the group stimulated over PPC. Our findings are consistent with a role of PPC in the adjustment of motor commands necessary for adapting to a novel mechanical environment.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".