Exploring the relationship between alexithymia and empathy : the role of emotion recognition and metacognitive ability
Bibliographic record
Abstract
Alexithymic traits have been consistently found to be associated with deficits in social functioning. These social deficits in individuals with alexithymic traits have been suggested to be related to the reduced levels of empathy in those with alexithymic traits. Factors underlying this relationship, however, are presently not understood. Such an understanding may provide insight into addressing the empathy deficits seen in alexithymic individuals. This study aimed to support prior studies that indicate a relationship between cognitive and affective alexithymic traits and cognitive and affective domains of empathy, respectively. A preliminary investigation of the impact of emotion recognition ability (anger, fear, sadness) and metacognitive ability on the relationship between alexithymic traits and empathy was also of interest. One hundred and twenty participants aged between 18 and 55 years (68 females; `M` = 24.95, `SD` = 7.19) completed the Toronto Alexithymia Scale ‚Äö- 20, the Interpersonal Reactivity Index (IRI), the Emotion Recognition Task (ERT) and provided confidence ratings for each presented emotion of the ERT to assess metacognitive ability. The study identified negative relationships between cognitive alexithymic traits, and cognitive empathy across the emotions of anger, fear, and sadness. A moderated mediation effect for this relationship was also found for the emotion sadness. The findings suggest poor emotion recognition of sadness negatively impacts the relationship between cognitive alexithymic traits and cognitive empathy, but only for those with low levels of metacognitive ability. No significant relationships were found between affective alexithymic traits and affective empathy. These findings upon further testing in a clinical population may contribute to addressing empathy deficits seen in alexithymic individuals, in order to improve their social functioning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".