Effects of deliberate control on verbal and facial expressions of pain
Bibliographic record
Abstract
The 'facial feedback hypothesis' suggests that inhibiting or exaggerating pain displays produces parallel effects on subjective experience. Research on the regulation of emotional expressions suggests that the act of self-regulation may be detectable in the properties of facial behavior. Both issues were examined in this study. Healthy young volunteers were videotaped while they were exposed to electric shocks varying in intensity. Participants in the Augment group were instructed to exaggerate their facial reactions to the shocks. Participants in the Attenuate group were instructed to inhibit their reactions. Controls simply responded to the shocks. All groups rated the pain of each shock on numeric, sensory and affective scales. In subsequent phases, judges rated the intensity of pain displays for all participants, and facial reactions were measured with the Facial Action Coding System. Results provided no support for the facial feedback hypothesis. Judges' ratings of participants' pain indicated that the augment instructions produced distinct alterations in pain expression. The control and inhibit groups showed linear increases in pain expression with increasing pain intensity, which did not differ significantly. Fine-grained analysis of participants' facial behavior provided evidence that pain augmentation was accompanied by topographic changes in pain expression. Parallels with existing studies, methodological issues and practical implications of the findings are discussed.
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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 | 0.008 |
| 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 | 0.000 | 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".