Plans for action in posterior parietal cortex: An rTMS investigation
Bibliographic record
Abstract
Many theories of visuomotor control distinguish between the planning of a movement (i.e., programming the initial kinematic parameters), and the execution of the movement itself (so-called ‘online control’). Evidence from neurological patients and functional brain imaging studies strongly support the notion that the posterior parietal cortex (PPC; especially the left hemisphere) plays a critical role in the planning and execution of goal-directed movements. Importantly, however, there is no clear consensus on how different sub-regions within the PPC contribute to movement planning and execution. Some theories suggest that both planning and execution are carried out primarily within the superior parietal lobe (SPL), whereas others suggest that planning is carried out by inferior parietal lobe (IPL) and execution is carried out by the SPL. In the current study we investigated this question using MRI-image guided repetitive transcranial magnetic stimulation (rTMS; 3 pulses at 10Hz). Specifically, we applied rTMS to different sites within the left IPL (angular and supramarginal gyri) and the left SPL (anterior and posterior SPL) either at target onset (planning), or movement onset (execution), while participants (n=12) made open-loop pointing movements to targets in peripheral vision. Thus, participants had vision of their hand and the target during the planning phase; however, vision of the hand and the target were removed at movement onset. The results revealed a significant interaction between the site of rTMS stimulation and the time of rTMS delivery. This interaction was driven by a significant increase in movement endpoint error when rTMS was applied during movement planning compared to execution in the SPL compared to both the IPL and sham stimulation. In short, these data are consistent with the idea that the SPL plays a crucial role in the planning (i.e., programming) of goal directed movements.
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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".