The impact of face ethnicity on the detection of pain facial expressions
Bibliographic record
Abstract
Many studies have revealed that the pain expressed by Black people is underestimated. Moreover, a series of studies have shown that White perceivers have a more stringent threshold for detecting pain on Black than on White faces. However, those studies systematically relied on a yes/no task, which is sensitive to one’s decisional criterion. Thus, it is not possible to know if the different thresholds come from alterations in sensitivity or from different decisional criteria. This research assessed whether those disparities remain when using a task controlling for decisional criteria. Experiment 1 aimed at replicating the aforementioned studies. We used a yes/no task where participants (N=50) saw either Black or White faces depicting a level of pain expression ranging from neutral to 100%. Participants indicated whether the face displayed pain or not. Results support previous findings with an effect of face ethnicity on participant’s proportions of pain detection, F(1, 49)=58.5, p<.001, η2p=.54. More precisely, participants detected pain less frequently on Black than on White faces between 35% and 55% of pain intensity (p<.001). In Experiment 2 (N=50), a 2-IFC task, considered to be mostly criterion-free, was used. Two faces appeared subsequently, and participants indicated which face displayed the higher pain expression. One face was neutral, and the other displayed a pain expression ranging from 5% to 70% intensity. Results showed a significant effect of ethnicity on participant’s proportions of pain detection, F(1, 49)=16.9, p<.001, η2p =.26. However, the lower frequency of pain detection in Black than in White faces was only observed at a 10% of pain intensity (p<.001). When controlling for decisional criteria, effect of face ethnicity subsists, yet seems weaker and constrained to a narrower range of pain intensities. Subsequent researches should simultaneously measure the respective contributions of sensitivity and decisional criteria in face ethnicity effect on pain detection.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".