The effects of alexithymia in the recognition of dynamic emotional faces
Bibliographic record
Abstract
Introduction Alexithymia is a multifactorial personality trait observed in several mental disorders, especially those with poor social functioning. Although it has been proposed that difficulties in interpersonal interactions in highly alexithymic individuals may stem from their reduced ability to express and recognize facial expressions, this still remains controversial. Aim In everyday life, faces displaying emotions are dynamic, although most studies have relied on static stimuli. The aim of this study was to investigate whether individuals with high levels of alexithymia differed from a control group in the categorization of emotional faces presented in a dynamic way. Given the highly dynamic nature of facial displays in real life, we used morphed videos depicting faces varying 1% from neutral to angry, disgust or happy faces, with a video presentation of 35 seconds. Method Sixty participants (27 males and 33 females) were divided into high (HA) and low levels of alexithymia (LA) by using the Toronto Alexithymia Scale (TAS-20). Participants were instructed to watch the face change from neutral to an emotion and to press a keyboard as soon as they could categorize an emotion expressed in the face. Results The results revealed an interaction between alexithymia and emotion showing that HA, compared to LA, were more inaccurate at categorizing angry faces. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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".