Bibliographic record
Abstract
Alexithymia is a multi-faceted personality trait defined by difficulties in identifying and describing emotions and is considered a risk factor for multiple psychiatric disorders. Current alexithymia research debates the type of attention bias involved in the processing of negative emotional information, especially in anxiety-evoking situations that are frequently associated with stress states. Relatedly, this study aims to examine the role of emotional influence on the attentional processing of Taiwanese alexithymic individuals. Using the Chinese version of the Toronto Alexithymia Scale-20 (TAS-20), individuals with high alexithymia (HA: TAS > 60, n = 26; M age = 23.36) and individuals with low alexithymia (LA: TAS < 39, n = 26; M age = 25.76) were recruited. Participants performed an emotional counting Stroop task preceded by anxiety-evoking (threatening and aversive pictures) or neutral pictures. Reaction times (RTs) of the emotional Stroop task were compared between HA and LA groups. Our results demonstrate that compared to individuals with LA, individuals with HA show early avoidance tendency (i.e., allocate less attentional resources to anxiety-evoking stimuli), and that negative affect therefore does not interfere with subsequent attention processing during the Stroop task, resulting in faster RT for unpleasant stimuli ( M threatening = 683.87, M aversive = 685.87) than neutral stimuli ( M neutral = 695.64) ( ps < .05). In addition, the attentional bias toward specific types of negative emotion was not differentiated in individuals with HA ( p < .05), suggesting that alexithymic individuals’ emotion schemas may be underdeveloped in terms of ability to specify exact emotions. This study provides evidence regarding early sensitization to negative stimuli during information processing, consistent with the notion that alexithymia is related to avoidant emotion regulation processes.
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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 | 0.000 | 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) | 0.002 | 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".