Brain responses to dynamic facial expressions of pain
Bibliographic record
Abstract
The facial expression of pain is a prominent non-verbal pain behaviour, unique and distinct from the expression of basic emotions. Yet, little is known about the neurobiological basis for the communication of pain. Here, subjects performed a sex-discrimination task while we investigated neural responses to implicit processing of dynamic visual stimuli of male or female faces displaying pain or angry expressions, matched on expression intensity and compared to neutral expression. Stimuli were presented in a mixed blocked/event-related design while blood oxygenation level dependent (BOLD) signal was acquired using whole-brain functional magnetic resonance imaging (fMRI) at 1.5 Tesla. Comparable sustained responses to pain and angry faces were found in the superior temporal sulcus (STS). Stronger transient activation was also observed to male expression of pain (Vs neutral and anger) in high-order visual areas (STS and fusiform face area) and in emotion-related areas including the amygdala (highest peak t-value=10.8), perigenual anterior cingulate cortex (ACC), and SI. Male pain compared to anger expression also activated the ventromedial prefrontal cortex, SII/posterior insula and anterior insula. This is consistent with the hypothesis that the implicit processing of male pain expression triggers an emotional reaction characterized by a threat-related response. Unexpectedly, several areas responsive to male expression, including the amygdala, perigenual ACC, and somatosensory areas, showed a decrease in activation to female pain faces (Vs neutral). This sharp contrast in the response to male and female faces suggests potential differences in the socio-functional role of pain expression in males and females.
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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".