Specificity of Human Parietal Saccade and Reach Regions during Transcranial Magnetic Stimulation
Bibliographic record
Abstract
Single-unit recordings in macaque monkeys have identified effector-specific regions in posterior parietal cortex (PPC), but functional neuroimaging in the human has yielded controversial results. Here we used on-line repetitive transcranial magnetic stimulation (rTMS) to determine saccade and reach specificity in human PPC. A short train of three TMS pulses (separated by an interval of 100 ms) was delivered to superior parieto-occipital cortex (SPOC), a region over the midposterior intraparietal sulcus (mIPS), and a site close to caudal IPS situated over the angular gyrus (AG) during a brief memory interval while subjects planned either a saccade or reach with the left or right hand. Behavioral measures then were compared to controls without rTMS. Stimulation of mIPS and AG produced similar patterns: increased end-point variability for reaches and decreased saccade accuracy for contralateral targets. In contrast, stimulation of SPOC deviated reach end points toward visual fixation and had no effect on saccades. Contralateral-limb specificity was highest for AG and lowest for SPOC. Visual feedback of the hand negated rTMS-induced disruptions of the reach plan for mIPS and AG, but not SPOC. These results suggest that human SPOC is specialized for encoding retinally peripheral reach goals, whereas more anterior-lateral regions (mIPS and AG) along the IPS possess overlapping maps for saccade and reach planning and are more closely involved in motor details (i.e., planning the reach vector for a specific hand). This work provides the first causal evidence for functional specificity of these parietal regions in healthy humans.
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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) | 0.001 | 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".