Pervasive Emotion Recognition Deficit Common to Alexithymia and the Repressive Coping Style
Bibliographic record
Abstract
OBJECTIVE: Previous research has demonstrated a deficit in the ability to recognize emotions in alexithymic individuals. The repressive coping style is thought to preferentially impair the detection of unpleasant compared with pleasant emotions, and the degree of deficit is typically thought to be less severe than in alexithymia. We compared emotion recognition ability in both individuals with alexithymia and those with the repressive coping style. METHODS: Three hundred seventy-nine subjects completed the 20-item Toronto Alexithymia Scale, the Levels of Emotional Awareness Scale, the Marlowe-Crowne Scale (a measure of repressive defensiveness), the Bendig Short Form of the Taylor Manifest Anxiety Scale, and the Perception of Affect Task. The Perception of Affect Task consists of four 35-item emotion recognition subtasks: matching sentences and words, faces and words, sentences and faces, and faces and photographs of scenes. The stimuli in each subtask consist of seven emotions (happiness, sadness, anger, fear, disgust, surprise, and neutral) depicted five times each. Recognition accuracy results were collapsed across subtasks within each emotion category. RESULTS: Highly alexithymic subjects (for all, p<.01) and those with low emotional awareness (for all, p<.001) were consistently less accurate in emotion recognition in all seven categories. Highly defensive subjects (including repressors) were less accurate in the detection of anger, sadness, fear, and happiness (for all, p<.05). Furthermore, scores on the Levels of Emotional Awareness Scale accounted for significantly more variance in performance on the Perception of Affect Task than scores on the Marlowe-Crowne Scale (p<.01). CONCLUSIONS: The results indicate that alexithymia and the repressive coping style are each associated with impairments in the recognition of both pleasant and unpleasant emotions and that the two styles of emotional self-regulation differ more in the magnitude than in the quality of these impairments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".