Facial expression of pain: Sex differences in the discrimination of varying intensities.
Bibliographic record
Abstract
It has been proposed that women are better than men at recognizing emotions and pain experienced by others. They have also been shown to be more sensitive to variations in pain expressions. The objective of the present study was to explore the perceptual basis of these sexual differences by comparing the visual information used by men and women to discriminate between different intensities of pain facial expressions. Using the data-driven Bubbles method, we were able to corroborate the woman advantage in the discrimination of pain intensities that did not appear to be explained by variations in empathic tendencies. In terms of visual strategies, our results do not indicate any qualitative differences in the facial regions used by men and women. However, they suggest that women rely on larger regions of the face that seems to completely mediate their advantage. This utilization of larger clusters could indicate either that women integrate simultaneously and more efficiently information coming from different areas of the face or that they are more flexible in the utilization of the information present in these clusters. Women would then opt for a more holistic or flexible processing of the facial information, while men would rely on a specific yet rigid integration strategy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".