Which gender do we perceive in a painful face?
Bibliographic record
Abstract
Some studies have found an association between our representations of gender and emotions, where male gender is more associated with negative emotions (Becker et al., 2007; Hess et al., 2009; Wardle et al., 2022). This association is still not well understood with regards to pain expressions, and it could have the consequence that pain is less easily perceived in a female face (Riva et al., 2011). In this study, we measured the association between gender and the pain facial expression, from two different angles. 64 observers (32 men) took part in two tasks where they were asked to categorize the gender of faces displaying either neutral or pain facial expressions (Exp.1) and the presence of pain in male and female faces (Exp.2). Stimuli consisted of 8 facial identities (4 men) taken from the Delaware Pain Database (Mende-Siedlecki et al., 2020). Using a digital morphing software, a continuum of 8 levels of intensity of gender (Exp.1 from 100% female to 100% men face) and emotional state (Exp.2 from 100% neutral to 100% pain) were created. On each trial, one face was presented to participants who had to identify whether the face represented a man or a woman (Exp.1) or a person in physical pain or not (Exp.2). Our results suggest that observers perceive significantly more masculine features in a face expressing pain (Exp.1) and they attribute less pain to a feminine face (Exp.2). These results reinforce the association between gender and emotion. Most importantly, they reveal a symmetry in this association, where the male gender is more easily perceived in a pain expression, and the pain expression is more easily perceived in a male face.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".