Perception of dynamic facial expressions activates a cortico-subcortico-cerebellar network in marmosets
Bibliographic record
Abstract
Abstract The perception and identification of faces and facial emotional expressions are critical for social communication in the daily life of all primates. Here, we investigated the neural network activated by dynamic facial expressions in awake New World common marmosets with ultra-high field fMRI at 9.4T. Our results show that dynamic facial expressions activate several areas along the occipitotemporal axis (V2/V3, V4/TEO, FST, caudal TE, rostral TE, TPO), frontal cortex (areas 45/47, 13, 8a and orbital cortex), amygdala, motion-sensitive areas, premotor area, and in the posterior lobe of the cerebellum. Negative faces increased the monkeys’ respiration rates and elicited stronger responses along the occipitotemporal cortical axis, in the amygdala and in the hypothalamus. This cortico-subcortico-cerebellar network may play an important role in the perception of behaviorally relevant facial expressions that are vital for social communication in marmosets. Significance Statement Recent research has highlighted the importance of emotional content of faces in social communication in humans and non-human primates. The current study focuses on the neural responses to dynamic emotional facial expressions in the common marmoset (Callithrix jacchus), a New World primate species sharing several similarities of social behavior with humans. Using ultra-high-field fMRI, we show that negative facial expressions activate a cortico-subcortico-cerebellar network and, critically, negative faces increase the level of arousal of marmosets, possibly relayed through a modulation of the activity in the autonomic nervous system via stress-integrative brain centres in the hypothalamus. Our results reveal the existence of specific neural and physiological responses to negative emotional faces suggesting that behaviorally relevant facial expressions are vital for social communication in New World marmosets.
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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".