Cerebral Regulation of Facial Expressions of Pain
Bibliographic record
Abstract
Facial expression of affective states plays a key role in social interactions. Interestingly, however, individuals differ substantially in their level of expressiveness, ranging from high expressive to stoic individuals. Here, we investigate which brain mechanisms underlie the regulation of facial expressiveness to acute pain. Facial responses, pain ratings, and brain activity (BOLD-fMRI) evoked by noxious heat and warm (control) stimuli were recorded in 34 human volunteers with different degrees of facial expressiveness. Within-subject and between-subject variations in blood oxygenation level-dependent (BOLD) responses were examined specifically in relation to facial responses. Pain expression was inversely related to frontostriatal activity, consistent with a role in downregulating facial displays. More detailed analyses of the peak activity in medial prefrontal cortex revealed negative BOLD responses to thermal stimuli, an effect generally associated with the default mode network. Given that this negative BOLD response was weaker in low expressive individuals during pain, it could reflect stronger engagement in, or reduced disengagement from, self-reflective processes in stoic individuals. The occurrence of facial expressions during pain was coupled with stronger primary motor activity in the face area and-interestingly-in areas involved in pain processing. In conclusion, these results indicate that spontaneous pain expression reflects activity within nociceptive pathways while stoicism involves the active suppression of expression, a manifestation of learned display rules governing emotional communication and possibly related to an increased self-reflective or introspective focus.
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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 | 0.001 |
| 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".