Facial emotion recognition accuracy in women with symptoms of polycystic ovary syndrome: Reduced fear and disgust perception
Bibliographic record
Abstract
BACKGROUND: Research suggests that women with polycystic ovary syndrome (PCOS) are more likely to suffer from mental health disorders, emotional distress, and have altered hormone profiles (e.g., higher androgens). Past research suggests facial emotion processing is affected by hormones (e.g., androgens), mental health-related disorders, and may be altered in PCOS. OBJECTIVES: The present study examined whether facial emotion recognition (FER) differs between women with and without PCOS symptoms. DESIGN: Observational case-control design. METHODS: = 178) completed a FER task that involved identifying emotions (anger, disgust, fear, happiness, sadness, surprise, or neutral) in images of emotional faces. Overall emotion recognition and emotion-specific accuracy were examined. PCOS symptom severity and provisional diagnoses were also assessed in women via self-report measures, including the polycystic ovary syndrome questionnaire. RESULTS: Women with provisional PCOS had significantly lower emotion recognition accuracy than those without PCOS, and emotion-specific differences were found for fear and disgust. A significant linear effect also emerged for overall FER, revealing men as the least accurate, followed by women with provisional PCOS, and then women without PCOS. CONCLUSIONS: The results suggest that women with PCOS may have difficulty with emotion recognition, especially fear and disgust. The sex difference in emotion recognition was in line with previous research. These findings are consistent with the theory that androgens affect emotion recognition and suggest implications for PCOS symptoms on women's emotional well-being and socioemotional functioning.
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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 | 0.004 |
| 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) | 0.002 | 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".