The Right Anterior Intraparietal Sulcus Is Critical for Bimanual Grasping: A TMS Study
Bibliographic record
Abstract
Grasping with 2 limbs in opposition to one another is older than the hand, yet the neural mechanisms for bimanual grasps remain unclear. Similar to unimanual grasping, bimanual grasping may require regions in the parietal cortex that use visual object-feature information to find matching stable grasp points on the object. The localization of matching points is computationally expensive, so it might make sense for the signals to converge in a single cortical area. To examine this, we use transcranial magnetic stimulation (TMS) to probe the contribution of cortical areas known to be associated with unimanual grasping, while participants performed bimanual grasps. We applied TMS to the anterior and caudal portion of the intra-parietal sulcus (aIPS and cIPS) in each hemisphere during a size-perturbation task using the index fingers of both hands to grasp an object whose orientation might or might not change. We found significant interaction effects between TMS and perturbation of the grasp-relevant object dimension that increased grip aperture only for the right aIPS. These results indicate that the aIPS is involved not only in unimanual, but also bimanual grasping, and the right aIPS is critically involved in bimanual grasps. This suggests that information from both hemispheres converges in the right hemisphere to achieve bimanual grasps.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".