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Bibliographic record
Abstract
Objectives Previous studies have shown the relationship between recognition of facial expressions and psychiatric symptoms. This study investigated how healthy young adults recognize neutral faces and which psychological distresses and symptoms relate to their recognition of neutral faces.\n\nMethods One hundred forty-three healthy volunteers participated in this study. We used neutral facial pictures, selected from the Japanese and Caucasian Facial Expressions of Emotion (JACFEE) photo set, to evaluate participants' facial expression recognition and the State-Trait Anxiety Inventory, Beck Depression Inventory, Toronto Alexithymia Scale, Conner-Davidson Resilience Scale, and Temperament and Character Inventory (TCI) to measure and examine their psychological characteristics.\n\nResults There were significant positive correlations between the recognition rate of neutral expressions as contempt and trait-anxiety level (r=0.21 ; p=0.01) and depression (r=0.20 ; p=0.02). This contempt-recognition was significantly negatively correlated with resilience score (r=-0.22 ; p=0.01) and the TCI self-directedness subscale (r=-0.29 ; p=0.00). Also, the TCI's harm avoidance subscale score was significantly positively correlated with the contempt recognition rate (r=0.21 ; p=0.01).\n\nConclusion These finding suggests recognition of neutral faces as contempt may be related to psychological distress, including trait and temperament characteristics. This negative bias toward neutral emotion (expressions) may affect interpersonal relations and social functioning in a healthy population
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".