Neural correlates of object size and object location during grasping actions
Bibliographic record
Abstract
The visuo-motor channel hypothesis (Jeannerod, 1981) postulates that grasping movements consist of a grip and a transport component differing in their reliance on intrinsic vs. extrinsic object properties (e.g. size vs. location, respectively). While recent neuroimaging studies have revealed separate brain areas implicated in grip and transport components within the parietal lobe, less is known about the neural processing of extrinsic and intrinsic properties of objects for grasping actions. We used functional magnetic resonance imaging adaptation to examine the cortical areas involved in processing object size, object location or both. Participants grasped (using the dominant right hand) or passively viewed sequential pairs of objects that could differ in size, location or both. We hypothesized that if intrinsic and extrinsic object properties are processed separately, as suggested by the visuo-motor channel hypothesis, we would observe adaptation to object size in areas that code the grip and adaptation to location in areas that code the transport component. On the other hand, if intrinsic and extrinsic object properties are not processed separately, brain areas involved in grasping may show adaptation to both object size and location. We found adaptation to object size for grasping movements in the left anterior intraparietal sulcus (aIPS), in agreement with the idea that object size is processed separately from location. In addition, the left superior parietal occipital sulcus (SPOC), primary somatosensory and motor area (S1/M1), precuneus, dorsal premotor cortex (PMd), and supplementary motor area (SMA) showed non-additive adaptation to both object size and location. We propose different roles for the aIPS as compared with the SPOC, S1/M1, precuneus, PMd and SMA. In particular, while the aIPS codes intrinsic object properties, which are relevant for hand preshaping and force scaling, area SPOC, S1/M1, precuneus, PMd and SMA code intrinsic as well as extrinsic object properties, both of which are relevant for digit positioning during grasping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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) | 0.000 | 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 teacher head, 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".