Emotional Experience and Recognition across Menstrual Cycle and in Premenstrual Disorder
Bibliographic record
Abstract
The aim of this study was to determine if differences exist in mood and in the recognition of female and male emotional faces among women in different phases of the menstrual cycle, and in women who suffer from Premenstrual Dysphoric Disorder (PMDD) in the premenstrual phase. Both the emotional states and the recognition of female and male emotional faces were assessed in women in each phase of the menstrual cycle: post-menstrual, ovulatory, post-ovulatory and premenstrual. Also evaluated was a group of women who presented symptoms of PMDD during the premenstrual phase. Only the women with PMDD showed significant changes in levels of unpleasant emotions and anxiety. Regardless of group, the highest accuracy was observed for recognition of happiness and disgust, followed by surprise and sadness. The lowest level of recognition was seen for fear and anger. In addition, expressions of happiness and surprise were recognized better on female faces, while fearful and angry expressions were recognized better on male faces. Finally, women in the ovulatory phase and those with PMDD showed higher accuracy when recognizing sadness on male faces. These results suggest that only women with PMDD presented important differences in their emotional experience compared to the other groups. Finally, the gender of the emotion emitter was a factor that affected the recognition of emotions, an effect that was seen to interact slightly with the menstrual cycle phase.
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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) | 0.001 | 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".