Brain mechanisms associated with facial encoding of affective states
Bibliographic record
Abstract
Affective states are typically accompanied by facial expressions, but these behavioral manifestations are highly variable. Even highly arousing and negative valent experiences, such as pain, show great instability in facial affect encoding. The present study investigated which neural mechanisms are associated with variations in facial affect encoding by focusing on facial encoding of sustained pain experiences. Facial expressions, pain ratings, and brain activity (BOLD-fMRI) during tonic heat pain were recorded in 27 healthy participants. We analyzed facial expressions by using the Facial Action Coding System (FACS) and examined brain activations during epochs of painful stimulation that were accompanied by facial expressions of pain. Epochs of facial expressions of pain were coupled with activity increase in motor areas (M1, premotor and SMA) as well as in areas involved in nociceptive processing, including primary and secondary somatosensory cortex, posterior and anterior insula, and the anterior part of the mid-cingulate cortex. In contrast, prefrontal structures (ventrolateral and medial prefrontal) were less activated during incidences of facial expressions, consistent with a role in down-regulating facial displays. These results indicate that incidences of facial encoding of pain reflect activity within nociceptive pathways interacting or possibly competing with prefrontal inhibitory systems that gate the level of expressiveness.
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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 | 0.002 |
| 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) | 0.000 | 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".