Alexithymia and Mood: Recognition of Emotion in Self and Others
Bibliographic record
Abstract
The present study explored relationships between alexithymia-a trait characterized by difficulties identifying and describing feelings and an external thinking style-and negative moods, negative mood regulation expectancies, facial recognition of emotions, emotional empathy, and alcohol consumption. The sample consisted of 102 university (primarily psychology) students (13 men, 89 women) aged 18 to 50 years (M = 22.18 years). Participants completed the Toronto Alexithymia Scale (TAS-20), Negative Mood Regulation Scale (NMRS), Depression Anxiety Stress Scales (DASS-21), Reading the Mind in the Eyes Test (RMET), Interpersonal Reactivity Index (IRI), and Alcohol Use Disorders Identification Test (AUDIT). Results were consistent with previous findings of positive relationships of TAS-20 alexithymia scores with both alcohol use (AUDIT) and negative moods (DASS-21) and a negative relationship with emotional self-regulation as indexed by NMRS. Predicted negative associations of both overall TAS-20 alexithymia scores and the externally oriented thinking (EOT) subscale of the TAS-20 with both RMET facial recognition of emotions and the empathic concern (EC) subscale of the IRI were supported. The mood self-regulation index NMRS fully mediated the relationship between alexithymia and negative moods. Hierarchical linear regressions revealed that, after other relevant variables were controlled for, the EOT subscale of the TAS-20 predicted RMET and EC. The concrete thinking or EDT facet of alexithymia thus appears to be associated with diminished facial recognition of emotions and reduced emotional empathy. The negative moods associated with alexithymia appear to be linked to subjective difficulties in self-regulation of emotions.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".