Psychopathy and deception detection using indirect measures
Bibliographic record
Abstract
Purpose. The goal of the current study was to examine psychopathy and indirect measures of deception detection. Methods. Undergraduate students ( N =444) viewed video clips of adult male offenders telling true and false stories about crimes. For each story, participants rated indirect measures of deception (thinking hard, nervousness, emotional arousal, and attempting to control behaviour) and credibility. Participants also chose the story they believed to be true and rated the confidence in their decision. Offenders were rated on the psychopathy checklist – revised. Results. Consistent with past research, deception detection accuracy was at chance level and unrelated to confidence. Ratings on indirect measures by undergraduates did not distinguish true and false statements in offenders. Psychopathic offenders were less successful at deception than non‐psychopathic offenders. Psychopathic traits were associated with lower perceived credibility during deception and ratings of thinking harder while lying. Conclusions. The results suggest that indirect measures of deception detection may be less useful in offender samples. Further, the findings are consistent with the general inability of psychopathic offenders to demonstrate superior deception skills in empirical studies. Indirect measures of deception uniquely related to psychopathic traits offer new insight into the relationship between psychopathy and deception.
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".