Attentional Dynamics During Emotional Face Processing Differentiate Alexithymia From Mood and Affective Symptoms
Bibliographic record
Abstract
INTRODUCTION: Alexithymia refers to difficulties in experiencing and expressing emotions, differentiating them from bodily sensations, restricted imagination, and externally oriented thinking. Mood and affective symptoms are often confounded with alexithymia due to the typical assessment through self-report. Performance measures may allow a more objective assessment of alexithymia. The goal of this study was to identify unique or shared performance-based features during emotional face processing. METHODS: A total of 171 students provided data on alexithymia (BVAQ) and mood/affective symptoms (DASS-21), along with performance and eye movements during an emotional face processing task. LASSO regressions isolated features associated with alexithymia or mood/affective symptoms. RESULTS: Cognitive alexithymia in the BVAQ was linked to delayed attentional disengagement from facial eye regions, increased face fixations/visual search, and accurate but slower responses. Mood/affective symptoms showed a pervasive link to faster but less accurate responses, accompanied by decreased facial fixations and visual search. CONCLUSION: Performance-based attentional dynamics during emotional face processing clearly distinguished (cognitive) alexithymia from mood and may aid in a multi-method assessment of alexithymia. Metrics such as these may better reflect behavioral dispositions and can be used as possible transdiagnostic markers of psychopathology.
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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 | 0.001 | 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) | 0.003 | 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".