How to Fish Like a Psychopath: Facial Expression Recognition in Individuals with Psychopathic Tendencies and Explicit Attitudes of Sexual Coercion and Aggression
Bibliographic record
Abstract
The current study explored the extent to which psychopathic traits and explicit evaluations of sexual aggression predicted accuracy of overall emotion recognition, and fear specifically.Participants (139 undergraduate and community men) were asked to complete self-report measures, including a psychopathy scale, explicit evaluations of sexual aggression and sexual preference indicators They viewed photographs of adults, adolescents, and children, and indicated which of six universal emotions the images expressed.The findings suggested that psychopathy and evaluation of sexual aggression had minute implications on accuracy of emotion recognition, though gender, gaze direction, and age of the individuals pictured in the images influenced overall and fear emotion recognition accuracy.There was no perceived difference between the undergraduate and community samples.Replication of this study should include a forensic population to see whether this deficit in affect recognition exists with these predictor variables but with individuals with higher levels of psychopathy.This could impact risk assessment and therapeutic interventions used.
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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 | 0.000 | 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 | 0.001 |
| 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".