Gender differences in the encoding and decoding of pain facial expressions.
Bibliographic record
Abstract
Facial expressions are crucial for assessing others' affective states. However, pain facial expressions (PFE) are poorly recognized, often confused with other negative affective states (Kappesser & Williams, 2002) and less easily perceived in women's faces (Riva et al., 2011). Studies have revealed various configurations of PFE (Kunz & Lautenbacher, 2014). Yet, it is unclear whether some of these configurations are easier to recognize than others–such differences may in part explain the disparities in perceived pain as a function of face gender. This study explored potential gender differences in the configurations of PFE (encoding) as well as their perception by external observers (decoding). We used the Delaware Pain Database (DPD; Mende-Siedlecki et al., 2020), containing 225 pictures of White individuals posing PFE. To investigate potential differences in PFE encoding between men and women, we used OpenFace to measure the activation levels of 17 action units (AUs) in those pictures. A principal component analysis indicated five main groups of AUs with correlated activations. Most importantly, the first component included AUs typically associated with PFE, and was more prominent in PFE of men than women. To verify if PFE are decoded differently as a function of face gender, we used ratings openly available within the DPD. Each picture in the DPD has indeed been rated on the perceived intensity of six basic emotions and pain. A mixed ANOVA 7 (affective states) by 2 (genders) indicated significant main effects of affective states and gender, as well as an interaction between both factors. T-tests indicated that fear and sadness were perceived as significantly higher in women's PFE, while pain was perceived as significantly higher in men's. These findings emphasize gender-specific disparities in PFE, their potential overlap with other affective states, and underscore the potential contribution of both encoding and decoding in observed gender differences.
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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 | 0.000 | 0.002 |
| 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) | 0.005 | 0.001 |
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".