No feelings for me, no feelings for you: A meta-analysis on alexithymia and empathy in psychopathy
Bibliographic record
Abstract
Introduction: Psychopathy is characterized by extensive emotional impairments. However, the current empirical literature on empathy and emotional awareness in psychopathy provides heterogeneous results. Methods: Multiple random-effects models were performed on studies examining the association between psychopathy and the Interpersonal Reactivity Index as well as Toronto Alexithymia Scale-20. In total, 72 articles providing 716 effect sizes and representing 15,016 participants were included in the analyses. Furthermore, differences among psychopathy factors and the role of potential moderators were assessed. Results: We found negative relationships between psychopathy and empathy (r = -.31), empathic concern (r = -.29), perspective taking (r = -.22), and personal distress (r = -.14). In addition, our results yielded positive relationships between psychopathy and alexithymia (r = .21), difficulty describing feelings (r = .20), difficulty identifying feelings (r = .16), and externally-oriented thinking (r = .15). The results varied by psychopathy factors and were partly moderated by sample type (correctional/clinical vs. community) and gender. Conclusion: These findings contribute to a better understanding of impaired emotionality in psychopathy. We show that psychopathy is associated with profound deficits in affective and cognitive empathy, personal distress, and emotional awareness.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.068 | 0.045 |
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; both teacher heads agree on what is shown here.
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".