FMRI Reveals a Dissociation between Grasping and Perceiving the Size of Real 3D Objects
Bibliographic record
Abstract
BACKGROUND: Almost 15 years after its formulation, evidence for the neuro-functional dissociation between a dorsal action stream and a ventral perception stream in the human cerebral cortex is still based largely on neuropsychological case studies. To date, there is no unequivocal evidence for separate visual computations of object features for performance of goal-directed actions versus perceptual tasks in the neurologically intact human brain. We used functional magnetic resonance imaging to test explicitly whether or not brain areas mediating size computation for grasping are distinct from those mediating size computation for perception. METHODOLOGY/PRINCIPAL FINDINGS: Subjects were presented with the same real graspable 3D objects and were required to perform a number of different tasks: grasping, reaching, size discrimination, pattern discrimination or passive viewing. As in prior studies, the anterior intraparietal area (AIP) in the dorsal stream was more active during grasping, when object size was relevant for planning the grasp, than during reaching, when object properties were irrelevant for movement planning (grasping>reaching). Activity in AIP showed no modulation, however, when size was computed in the context of a purely perceptual task (size = pattern discrimination). Conversely, the lateral occipital (LO) cortex in the ventral stream was modulated when size was computed for perception (size>pattern discrimination) but not for action (grasping = reaching). CONCLUSIONS/SIGNIFICANCE: While areas in both the dorsal and ventral streams responded to the simple presentation of 3D objects (passive viewing), these areas were differentially activated depending on whether the task was grasping or perceptual discrimination, respectively. The demonstration of dual coding of an object for the purposes of action on the one hand and perception on the other in the same healthy brains offers a substantial contribution to the current debate about the nature of the neural coding that takes place in the dorsal and ventral streams.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".