The Effect of Alexithymia on Attentional Bias Toward Emotional Stimuli in Depression: An Eye-Tracking Study
Bibliographic record
Abstract
Alexithymia-reflecting deficits in cognitive emotion processing-is highly prevalent in individuals with depressive disorders. Subsequently, mixed evidence for attentional bias is found in these individuals. Alexithymia may be a potential influencing factor for attentional bias in depression. In the current study, 83 currently depressed (CD) and 76 never-depressed (ND) controls completed an eye-tracker task consisting of valenced (non)-social pictures. Alexithymia scores were also included as a moderator as both a continuous and categorical measure (so high vs. low alexithymia). No group difference or moderating effect of alexithymia was found on attentional bias. Thus, alexithymic symptoms, included both dimensionally and categorically, may not influence biased attentional processing in depression compared to ND individuals. Thus, it is important to explore other potential explaining factors for the equivocal results found on biased attentional processing of emotional information in depression.
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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 | 0.002 |
| 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".