Transcranial Magnetic Stimulation Over Human Dorsal–Lateral Posterior Parietal Cortex Disrupts Integration of Hand Position Signals Into the Reach Plan
Bibliographic record
Abstract
Posterior parietal cortex (PPC) has been implicated in the integration of visual and proprioceptive information for the planning of action. We previously reported that single-pulse transcranial magnetic stimulation (TMS) over dorsal-lateral PPC perturbs the early stages of spatial processing for memory-guided reaching. However, our data did not distinguish whether TMS disrupted the reach goal or the internal estimate of initial hand position needed to calculate the reach vector. To test between these hypotheses, we investigated reaching in six healthy humans during left and right parietal TMS while varying visual feedback of the movement. We reasoned that if TMS were disrupting the internal representation of hand position, visual feedback from the hand might still recalibrate this signal. We tested four viewing conditions: 1) final vision of hand position; 2) full vision of hand position; 3) initial and final vision of hand position; and 4) middle and final vision of hand position. During the final vision condition, left parietal stimulation significantly increased endpoint variability, whereas right parietal stimulation produced a significant leftward shift in both visual fields. However, these errors significantly decreased with visual feedback of the hand during both planning and control stages of the reach movement. These new findings demonstrate that 1) visual feedback of hand position during the planning and early execution of the reach can recalibrate the perturbed signal and, importantly, and 2) TMS over dorsal-lateral PPC does not disrupt the internal representation of the visual goal, but rather the reach vector, or more likely the sense of initial hand position that is used to calculate this vector.
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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 | 0.000 |
| 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 | 0.000 |
| 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".