Mental Representations of Pain: the Effect of the Sex of the Perceiver
Bibliographic record
Abstract
Humans rely on facial expressions to assess emotions in others. Of all the negative-valued emotions, pain remains the least accurately recognized, and this deficiency is even greater when the observer is a man (Wingenbach et al., 2018). Moreover, pain is often confused with emotions like anger and disgust (Kappesser & Williams, 2002). This study verifies whether patterns of similarity between mental representations of pain and other negative emotions vary as a function of the observer's sex. We first used the Reverse Correlation method (Mangini & Biederman, 2004) to reveal mental representations of pain facial expressions in 89 participants (42 males). We then presented all representations to a sample of 16 independent judges who rated how intensely they perceived the following emotions: anger, disgust, fear, happiness, sadness, surprise and pain. We calculated the average rating per emotion across the 16 judges for each of the 89 mental representations. A 2 (sex) by 7 (emotions) repeated measures ANOVA revealed a main effect of emotion (F(1,163.9)=167.1, p=<0.001), but no main effect of sex (p=0.75) or interaction was found (p=0.63). Post-hoc t-tests on each combination of emotions revealed significant differences in ratings of emotions except for disgust-anger and disgust-pain. We found anger and disgust rated as the most salient emotions, even more than pain. Lastly, a cluster analysis on the average ratings for each emotion (pooled across sexes) revealed 3 clusters, where the dominant emotion in the mental representation was 1) anger, 2) sadness and 3) anger and disgust equally. Our results suggest that emotions perceived in proxies of mental representations of pain extracted from men and women do not differ significantly. Interestingly however, our results reveal significant individual variations in the dominant emotions that are part of the mental representation of pain facial expressions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".