Black is angry, White is scared: Evaluation of pain expressions in White and Black faces
Bibliographic record
Abstract
Detecting pain in others is a social skill of utmost importance (Williams, 2002). In countries where the racial majority is of White-European descent, pain experienced by Black individuals is underestimated. This tendency may in part take root in perceptual processes involved in pain facial expression recognition (Mende-Siedlecki et al., 2019). In the present study, we verified how people represent the appearance of pain expressions in Black and White faces. We extracted the mental representations of 30 White-Canadian and 30 Black-African participants using Reverse Correlation (Mangini & Biederman). Participants rated perceived pain in White and Black faces embedded in white sinusoidal noise. The average mental representations obtained in each ethnic group with each face ethnicity were then rated by independent participants on the degree to which they expressed five basic emotions and pain. Two main results were obtained. First, the overall emotional intensity of the mental representations extracted in Black-African participants was lower than the one of White-Canadians (F(1, 52)=5.02, p=.03). Second, the mental representation of pain, when expressed in a Black face, was perceived as less in pain (t(54)=8.3, p<.001) and more angry (t(54)=-3.6, p=.001) than when expressed in a White face. Moreover, when pain was expressed in a White face, it was perceived as more sad (t(53)=2.4, p=.02) and scared (t(54)=3.4, p=.001). These results suggest that at least two perceptual factors may be linked with the underestimation, by White individuals, of the pain experienced by Black individuals. First, White-Canadians expect pain expressions to be more intense than Black-Africans. These higher expectations may lead them to erroneously assume that pain experienced by Black individuals is of lower intensity. Second, while pain expressions in White faces include other emotions associated with approachability, pain expression in Black faces appear angrier, an emotion that may discourage helping behavior.
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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 | 0.001 | 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".