Pain in the eye of the beholder: Variations in pain visual representations as a function of face ethnicity and culture
Bibliographic record
Abstract
Pain experienced by Black individuals is systematically underestimated, and recent studies have shown that part of this bias is rooted in perceptual factors. We used Reverse Correlation to estimate visual representations of the pain expression in Black and White faces, in participants originating from both Western and African countries. Groups of raters were then asked to evaluate the presence of pain and other emotions in these representations. A second group of White raters then evaluated those same representations placed over a neutral background face (50% White; 50% Black). Image-based analyses show significant effects of culture and face ethnicity, but no interaction between the two factors. Western representations were more likely to be judged as expressing pain than African representations. For both cultural groups, raters also perceived more pain in White face representations than in Black face representations. However, when changing the background stimulus to the neutral background face, this effect of face ethnic profile disappeared. Overall, these results suggest that individuals have different expectations of how pain is expressed by Black and White individuals, and that cultural factors may explain a part of this phenomenon.
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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 | 0.003 |
| 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".