Relations between emotional awareness and alexithymia measures: Behavioral and neurobiological evidence
Bibliographic record
Abstract
The present work is the first to examine the behavioral and the neurobiological correlates of trait emotional awareness and alexithymia which are related personality constructs. Both traits are essential for understanding the abilities and deficits of psychosomatic patients to regulate emotions. However, to date little is known about their behavioral and neurobiological correlates. Therefore, the present dissertation addresses the relation between both constructs. The introduction section give an extensive overview of the available behavioral and neurobiological research. Based on the revealed literature, open research questions are identified and addressed in one psychometric and one imaging study. In study 1 the psychometric properties and relations between two different methods of measuring alexithymia and one measure of emotional awareness were evaluated. The 20-Item Toronto Alexithymia Scale (TAS-20), the Toronto Structured Interview for Alexithymia (TSIA), and the Levels of Emotional Awareness Scale (LEAS), which is a performance-based measure of emotional awareness, were administered to 84 university students. Study 2 addressed automatic brain reactivity to emotional stimuli as a function of trait emotional awareness. During scanning, happy, angry, fearful, and neutral facial expressions were subliminally presented to 46 healthy subjects, who had to rate the fit between artificial and emotional words. The results of the studies are summarized and integrated in the existing literature. Finally, open research questions are discussed, implications for future research are outlined.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".