Face selective patches in marmoset frontal cortex
Bibliographic record
Abstract
Abstract Primates have evolved the ability transmit important social information through facial expression. In humans and macaque monkeys, socially relevant face processing is accomplished via a distributed cortical and subcortical functional network that includes specialized patches in anterior cingulate cortex and lateral prefrontal cortex, regions usually associated with high-level cognition. It is unclear whether a similar network exists in New World primates, who diverged ~35 million years from Old World primates and have a less elaborated frontal cortex. The common marmoset ( Callithrix jacchus ) is a small New World primate that is ideally placed to address this question given the complex social repertoire inherent to this species (e.g., observational social learning; imitation; cooperative antiphonal calling). Here, we investigated the existence of a putative high-level face processing network in marmosets by employing ultra-high field (9.4 Tesla) task-based functional MRI (fMRI). We demonstrated that, like Old World primates, marmosets show differential activation in anterior cingulate cortex and lateral prefrontal cortex while they view socially relevant videos of marmoset faces. We corroborate the locations of these frontal regions by demonstrating both functional (via resting-state fMRI) and structural (via cellular-level tracing) connectivity between these regions and temporal lobe face patches. Given the evolutionary separation between macaques and marmosets, our results suggest this frontal network specialized for social face processing predates the separation between Platyrrhini and Catarrhine. These results give further credence to the marmoset as a viable preclinical modelling species for studying human social disorders.
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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) | 0.002 | 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".