Does depressive symptomology moderate the relationship between alexithymic traits and emotion perception ability?
Bibliographic record
Abstract
While a relationship between alexithymic traits and emotion perception difficulties has been consistently demonstrated, no prior research has examined whether depressive symptomology influences this relationship. The present study examined the relationship between alexithymic traits and the ability to identify a range of dynamically displayed basic emotions (happiness, sadness, and fear), across various emotion intensity levels (20%, 60%, and 100%), and whether depressive symptomology moderated this relationship. One-hundred and twenty participants (68 females; aged 18 to 65 years, `M` = 24.95, `SD` = 7.19) completed the Toronto Alexithymia Scale, the Depression, Anxiety, and Stress Scale, and the Emotion Recognition Task. The present results indicate that higher levels of alexithymic traits may be associated with a reduced ability to identify fear at full intensity levels, which provides some limited support for the prior literature. Furthermore, higher levels of depressive symptomology may be associated with an enhanced ability to identify fear at low intensity levels, which provides tentative support for the negative bias in emotion processing typically found in depressed individuals. However, no further enhancement, attenuation, or moderation effects were evident. Future research in individuals with higher levels of alexithymic traits and depressive symptomology is required to support these findings and to better inform potential targeted treatment programs for those who may be experiencing interpersonal difficulties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".