Are both the sensory and the affective dimensions of pain encoded in the face?
Bibliographic record
Abstract
The facial expression of pain plays a crucial role in pain communication and pain diagnostics. Despite its importance, it has remained unknown which dimensions of pain (sensory and/or affective) are encoded in the face. To answer this question, we used a well-established cognitive strategy (suggestions) to differentially modulate the sensory and affective dimensions of pain and investigate the effect of this manipulation on facial responses to experimental pain. Twenty-two subjects participated in the study. Their facial expressions, pain intensity, and unpleasantness ratings as well as skin conductance responses to tonic and phasic heat pain were assessed before and after suggestions directed toward increase in affective and sensory qualities of pain, respectively, were provided. Facial expressions were analyzed with the Facial Action Coding system. As expected, suggestions designed to increase the sensory dimension produced a selective increase in pain intensity ratings, whereas suggestions designed to increase pain affect produced increased unpleasantness ratings and elevated skin conductance responses. Furthermore, suggestions for either increased pain affect or pain sensation produced selective modulations in facial response patterns, with facial movements around the eyes mostly encoding sensory aspects, whereas movements of the eyebrows and of the upper lip were closely associated with the affective pain dimension. The facial expression of pain is a multidimensional response system that differentially encodes affective and sensory pain qualities. This differential encoding might have evolved to guarantee that the specific characteristics of one's pain experience are facially communicated, thereby ensuring adequate help and support from others.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".