Face-Selective Patches in Marmosets Are Involved in Dynamic and Static Facial Expression Processing
Bibliographic record
Abstract
The correct identification of facial expressions is critical for understanding the intention of others during social communication in the daily life of all primates. Here we used ultra-high-field fMRI at 9.4 T to investigate the neural network activated by facial expressions in awake New World common marmosets from both male and female sex, and to determine the effect of facial motions on this network. We further explored how the face-patch network is involved in the processing of facial expressions. Our results show that dynamic and static facial expressions activate face patches in temporal and frontal areas (O, PV, PD, MD, AD, and PL) as well as in the amygdala, with stronger responses for negative faces, also associated with an increase of the respiration rates of the monkey. Processing of dynamic facial expressions involves an extended network recruiting additional regions not known to be part of the face-processing network, suggesting that face motions may facilitate the recognition of facial expressions. We report for the first time in New World marmosets that the perception and identification of changeable facial expressions, vital for social communication, recruit face-selective brain patches also involved in face detection processing and are associated with an increase of arousal. SIGNIFICANCE STATEMENT Recent research in humans and nonhuman primates has highlighted the importance to correctly recognize and process facial expressions to understand others' emotions in social interactions. The current study focuses on the fMRI responses of emotional facial expressions in the common marmoset ( Callithrix jacchus ), a New World primate species sharing several similarities of social behavior with humans. Our results reveal that temporal and frontal face patches are involved in both basic face detection and facial expression processing. The specific recruitment of these patches for negative faces associated with an increase of the arousal level show that marmosets process facial expressions of their congener, vital for social communication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".