Disentangling Representations of Object and Grasp Properties in the Human Brain
Bibliographic record
Abstract
UNLABELLED: The properties of objects, such as shape, influence the way we grasp them. To quantify the role of different brain regions during grasping, it is necessary to disentangle the processing of visual dimensions related to object properties from the motor aspects related to the specific hand configuration. We orthogonally varied object properties (shape, size, and elongation) and task (passive viewing, precision grip with two or five digits, or coarse grip with five digits) and used representational similarity analysis of functional magnetic resonance imaging data to infer the representation of object properties and hand configuration in the human brain. We found that object elongation is the most strongly represented object feature during grasping and is coded preferentially in the primary visual cortex as well as the anterior and posterior superior-parieto-occipital cortex. By contrast, primary somatosensory, motor, and ventral premotor cortices coded preferentially the number of digits while ventral-stream and dorsal-stream regions coded a mix of visual and motor dimensions. The representation of object features varied with task modality, as object elongation was less relevant during passive viewing than grasping. To summarize, this study shows that elongation is a particularly relevant property of the object to grasp, which along with the number of digits used, is represented within both ventral-stream and parietal regions, suggesting that communication between the two streams about these specific visual and motor dimensions might be relevant to the execution of efficient grasping actions. SIGNIFICANCE STATEMENT: To grasp something, the visual properties of an object guide preshaping of the hand into the appropriate configuration. Different grips can be used, and different objects require different hand configurations. However, in natural actions, grip and object type are often confounded, and the few experiments that have attempted to separate them have produced conflicting results. As such, it is unclear how visual and motor properties are represented across brain regions during grasping. Here we orthogonally manipulated object properties and grip, and revealed the visual dimension (object elongation) and the motor dimension (number of digits) that are more strongly coded in ventral and dorsal streams. These results suggest that both streams play a role in the visuomotor coding essential for 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".