Ethnicity and Pain Recognition: Unraveling Confusion Patterns in Facial Expressions
Bibliographic record
Abstract
Ethnic minorities expressing pain are often under-diagnosed and under-treated (Cintron et al., 2006). The misinterpretation of pain signals conveyed through facial expressions across different ethnicities might be a key factor contributing to these variations (Dildine et al., 2023). Current theories of social perception suggest that such misclassifications could be due to ethnic stereotype knowledge that could amplify confusions between pain and similar-looking facial expressions, like anger or disgust (Hugenberg et al., 2004; Kunz et al., 2019; Roy et al., 2015). However, no study has explicitly examined the interaction between these factors. Here, we addressed this question by first examining whether face ethnicity influences the interpretation of facial expressions of pain as other negative emotions. Using a generative model of the human face, we displayed 40 facial expressions of pain, sadness, anger, fear and disgust on each of 40 face identities varying in ethnicity (Black, East Asian, White) and sex (male, female). Participants (30 White Western; sex-balanced; 18-35 years) classified each stimulus in an alternative forced choice task (5AFC). Combining a within-subject bootstrap analysis (10,000 resamples) with the Bayesian estimate of population prevalence (Ince et al., 2021), we investigated the effect of ethnicity on accuracy and confusion patterns between pain and other negative emotions. Preliminary results (n = 22) show that, across face ethnicities, facial expressions of pain are the least accurately identified and generate more systematic confusions with disgust for black compared to white faces. To explore these confusions further, we plan to conduct a second complementary discrimination task where participants will detect the presence of target emotions (measured using d-prime, n = 30). By investigating the role of face ethnicity in interpreting facial expressions of pain, our study aims to shed light on how and why disparities in pain perception arise and provide potential insights into mitigating these effects.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".