A “fine-cuts” approach disentangling psychopathic, autistic and alexithymic traits in their associations with affective, cognitive and motor empathy
Bibliographic record
Abstract
Atypical empathy is seen in relation to psychopathy and autistic traits; however, studies typically conflate affective and cognitive facets of empathy. Moreover, motor empathy has been suggested as another facet of empathy, advocating for further delineation of empathy dimensions. In addition, alexithymia may affect responding to emotional, cognitive or motor states in others. The current study investigated how psychopathic, autistic and alexithymic traits are associated with those empathy facets. Nonclinical participants ( N = 212) completed online self-report measures of affective, cognitive and motor empathy, primary and secondary psychopathy, autistic and alexithymic traits. A subsample ( N = 157) also completed a behavioral measure of motor empathy (i.e., behavioral synchrony) using a virtual agent. Whilst all traits were associated with reduced cognitive empathy and behavioral synchrony; path analyses supported a mediation model of cognitive empathy difficulties through alexithymia only for primary psychopathy. Secondary psychopathy and alexithymia were associated with increased motor empathy, specifically tendencies to mimic negative emotions. In contrast, primary psychopathy was associated with reduced affective empathy and inhibition of positive emotion imitation, despite reporting self-other overlap experiences induced by behavioral synchrony. Overall, these findings highlight the need for a “fine-cuts” approach; delineating the role of empathy subfacets in atypical empathy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".