Processing of Facial Expressions of Negative Emotion in Alexithymia: The Influence of Temporal Constraint
Bibliographic record
Abstract
Alexithymia, a characteristic involving a limited affective vocabulary appears to involve three components: difficulty identifying feelings, difficulty describing feelings, and externally oriented thinking. There is evidence that alexithymic characteristics are associated with differences in emotion information-processing. We examined the role of temporal factors in alexithymic emotion-processing deficits, taking into account the confound between alexithymic characteristics and positive and negative affectivity. One hundred forty-six participants completed the 20-item Toronto Alexithymia Scale and the Positive and Negative Affect Schedule. In a signal-detection paradigm, participants judged facial expressions depicting neutral or negative emotions under slow and rapid presentation conditions. The alexithymia component of difficulty in describing feelings was inversely related to the ability to detect expressions of negative emotion in the speeded condition. This relationship was independent of positive and negative affectivity. Alexithymic components positive and negative affectivity were unrelated to response bias. The results emphasize the influence of difficulty describing feelings within the alexithymia construct and its difference from positive and negative affectivity. They suggest that an alexithymic deficit in describing feelings is associated with a deficit in processing negative emotions that is most apparent when processing capacity is challenged. Theoretical and methodological implications are discussed.
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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 | 0.004 |
| 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) | 0.001 | 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".