Emotional faces alter pain perception
Bibliographic record
Abstract
Abstract Background Although emotional faces might be particularly suited for the investigation of emotional pain modulation, they have thus far rarely been used. In particular, previous studies using emotional faces for pain modulation did not assess modulation of mood, did not differentiate pain intensity and unpleasantness, and did not investigate the interaction with attentional state. Here, we assessed how viewing emotional faces impacts the perceived intensity and unpleasantness of experimentally induced pain as well as subjects' mood. Methods Healthy subjects viewed sad, happy or neutral faces, and short painful thermal stimuli were simultaneously applied to the volar forearm. Subjects provided ratings of pain intensity, pain unpleasantness and mood after blocks consisting of eight pairs of thermal stimuli and eight pairs of faces. Each subject viewed six blocks in total (two of each emotion). Perceptual discrimination tasks ensured that subjects either focused on the pain or on the emotional faces. Results Subjects reported higher pain unpleasantness and higher pain intensity as well as worse mood when they viewed blocks of sad faces compared with blocks of happy or neutral faces. Changes in mood correlated with modulation of pain intensity, but not unpleasantness. No interaction was observed between emotional pain modulation and attentional state. Conclusions These results provide evidence that viewing emotional faces modulates perceived pain intensity and unpleasantness and that this pain modulation is related to mood changes, at least for intensity. Faces might be a reliable and socially relevant tool to study the impact of discrete emotions on pain perception.
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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) | 0.008 | 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".