Perception and Action Selection Dissociate Human Ventral and Dorsal Cortex
Bibliographic record
Abstract
We test theories about the functional organization of the human cortex by correlating brain activity with demands on perception versus action selection. Subjects covertly searched for a target among an array of 4, 8, or 12 items (perceptual manipulation) and then, depending on the color of the array, made a saccade toward, away from, or at a right angle from the target (action manipulation). First, choice response times increased linearly as the demands increased for each factor, and brain activity in several cortical areas increased with increasing choice response times. Second, we found a double dissociation in posterior cortex: Activity in ventral regions (occipito-temporal cortex) increased linearly with perceptual, but not action, selection demands; conversely, activity in dorsal regions (parietal cortex) increased linearly with action, but not perceptual, selection demands. This result provides the clearest support of the theory that posterior cortex is segregated into two distinct streams of visual processing for perception and action. Third, despite segregated anatomical projections from posterior ventral and dorsal streams to lateral pFC, we did not find evidence for a functional dissociation between perception and action selection in pFC. Increasing action, but not perceptual, selection demands evoked increased activation along both the dorsal and the ventral lateral pFC. Although most previous studies have focused on perceptual variables (e.g., space vs. object), these data suggest that understanding the computations underlying action selection will be key to understanding the functional organization of pFC.
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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) | 0.002 | 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".