Emotion recognition deficits in patients with skin picking disorder: the role of alexithymia while controlling depression, and anxiety levels
Bibliographic record
Abstract
Objective: Skin Picking Disorder (SPD) has been associated with higher levels of alexithymia, a condition that predicts self-injurious behaviors, a core feature of SPD. Recent studies have expanded the understanding of alexithymia beyond deficits in emotional awareness, highlighting its role on the ability to recognize and process others' emotions. This study aimed to explore how emotion recognition abilities differ in individuals with varying levels of alexithymia and the presence of SPD. Methods: This cross-sectional case-control study included 45 individuals diagnosed with SPD and 47 controls. Participants were assessed through the Toronto Alexithymia Scale, the Facial Emotion Recognition Test, the Beck Depression Inventory, and the Beck Anxiety Inventory. Two-way analysis of covariance tests were conducted to evaluate the effects of SPD diagnosis and alexithymia levels on facial emotion recognition tasks, while controlling for anxiety and depression. Results: Individuals with Skin Picking Disorder (SPD) showed significant impairments in recognizing fear, neutral expressions, and surprise compared to controls. SPD was linked to lower fear recognition accuracy, while both SPD and higher alexithymia were associated with poorer recognition of neutral expressions and surprise. No significant differences were found for happiness, sadness, anger, or disgust. No interaction effects were observed between SPD and alexithymia for any emotion. Conclusions: This study enhances the understanding of emotion recognition in SPD and alexithymia by highlighting their shared and unique challenges. The absence of a significant interaction effect suggests that these conditions independently contribute to emotion recognition deficits without compounding effects, underscoring the need for targeted interventions.
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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 | 0.001 |
| 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".