Functional Mapping of Face-Selective Regions in the Extrastriate Visual Cortex of the Marmoset
Bibliographic record
Abstract
The cerebral cortex of humans and macaques has specialized regions for processing faces and other visual stimulus categories. It is unknown whether a similar functional organization exists in New World monkeys, such as the common marmoset (Callithrix jacchus), a species of growing interest as a primate model in neuroscience. To address this question, we measured selective neural responses in the brain of four awake marmosets trained to fix their gaze upon images of faces, bodies, objects, and control patterns. In two of the subjects, we measured high gamma-range field potentials from electrocorticography arrays implanted over a large portion of the occipital and inferotemporal cortex. In the other two subjects, we measured BOLD fMRI responses across the entire brain. Both techniques revealed robust, regionally specific patterns of category-selective neural responses. We report that at least six face-selective patches mark the occipitotemporal pathway of the marmoset, with the most anterior patches showing the strongest preference for faces over other stimuli. The similar appearance of these patches to previous findings in macaques and humans, including their apparent arrangement in two parallel pathways, suggests that core elements of the face processing network were present in the common anthropoid primate ancestor living ∼35 million years ago. The findings also identify the marmoset as a viable animal model system for studying specialized neural mechanisms related to high-level social visual perception in humans.
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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 | 0.000 |
| 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) | 0.001 | 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".