Perceiving Emotion Through the Lens of Psychopathy: A Comparison of Self-Report Measures
Bibliographic record
Abstract
Psychopathy is characterized by interpersonal and affective deficits, particularly difficulties in accurately perceiving emotional cues. Although psychopathy has been extensively studied within forensic populations, there is limited research exploring psychopathy and emotion perception in non-forensic contexts, especially regarding the effectiveness of self-report measures. Addressing this gap, the present study investigated the relationship between self-reported psychopathic traits and accuracy in emotion perception. Undergraduate participants completed three self-report measures of psychopathy (SRP-SF, ICU, and EPA-SSF), followed by an emotion perception task using dynamic audio-video stimuli. Results indicated significant negative correlations between psychopathy scores and emotion perception accuracy, particularly for negative emotions such as sadness, fear, and disgust. Traits measured by the ICU consistently demonstrated the strongest predictive relationships. These findings reinforce the significance of affective dysfunction in psychopathy and support the validation of self-report measures, promoting broader and more accessible research on psychopathic traits across diverse contexts.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".