Visual feedback modulates BOLD activity in the posterior parietal cortex more so for visually-guided grasping than for visually-guided reaching
Bibliographic record
Abstract
When we reach out to grasp an object, the visuomotor system uses vision to direct our hand to the object's location and scale our grip aperture to the object's size. Several lines of evidence from human and non-human primate studies have implicated a network of structures in the posterior parietal cortex (PPC) in the programming and updating of visually guided grasping. The present study was designed to examine whether the availability of visual feedback during movement execution would modulate patterns of brain activation in the PPC for visually-guided reach-to-grasp movements. Participants were asked to either reach out and touch or reach out and grasp novel 3-D objects with or without visual feedback throughout the movement. A voxel-wise analysis was carried out using a 2 (task) x 2 (feedback) ANOVA. Not surprisingly, the availability of visual feedback was found to increase activation in many visual areas in both the dorsal and ventral streams. In addition, grasping (as compared to reaching) invoked activity in motor areas (premotor and primary motor cortex), early visual areas (striate and extra-striate cortex), and areas in both the dorsal [e.g., anterior intraparietal sulcus (aIPS) and superior parietal lobule (SPL)] and ventral (lateral occipital complex, fusiform gyrus, and inferior temporal cortex) streams of visual processing. Importantly, however, task by feedback interactions were observed in several dorsal stream regions. In the right SPL, the left aIPS, and the precuneus in both hemispheres, visual feedback increased the level of activation associated with reach-to-grasp movements relative to those made without visual feedback, a difference that was not apparent in the levels of activation associated with reach-to-touch movements. Taken together, these results add to a growing body of evidence that implicates the PPC in the programming, online monitoring, and updating of visually guided grasping.
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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 | 0.002 |
| 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".