Facial emotion recognition in adolescent depression: The role of childhood traumas, emotion regulation difficulties, alexithymia and empathy
Bibliographic record
Abstract
Introduction: Facial emotion recognition (FER) is crucial for effective social competency, and problems in this skill are linked depression during adolescence. In this study, we aimed to find the rates of FER accuracy for negative (fearful, sad, angry, disgusted), positive (happy, surprised), and neutral emotions, and the possible predictors of FER skill for most confusing emotions. Subjects and Methods: A total of 67 drug-naive adolescents with depression (11 boys, 56 girls; 11-17 years) were recruited for the study. The facial emotion recognition test, childhood trauma questionnaire and basic empathy, difficulty of emotion regulation, and Toronto alexithymia scales were used. Results: The analysis demonstrated that adolescents have more difficulties in recognizing negative emotions when compared the positive ones. The most confusing emotion is fear (39.8% of fear was recognized as surprise). Boys have lower fear recognition skill than girls and higher childhood emotional abuse, physical abuse, emotional neglect, and difficulty in describing feelings to predict lower fear recognition skill. For sadness recognition skill, emotional neglect, difficulty in describing feelings, and depression severity were the negative predictors. Emotional empathy has a positive effect on disgust recognition skill. Conclusion: Our findings demonstrated that impairment of FER skill for negative emotions is associated with childhood traumas, emotion regulation difficulties, alexithymia, and empathy symptoms in adolescent depression.
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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 | 0.000 |
| 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".