Cold‐blooded lie catchers? An investigation of psychopathy, emotional processing, and deception detection
Bibliographic record
Abstract
Purpose. The process of catching liars is challenging, though evidence suggests that deception detection abilities are influenced by the characteristics of the judge. This study examined individual differences in emotional processing and levels of psychopathic traits on the ability to judge the veracity of written narratives varying in emotional valence. Methods. Undergraduate participants ( N = 251) judged the veracity of 12 written narratives (truthful/deceptive) across three emotional categories: positive, negative, and neutral events. Levels of psychopathy were assessed to investigate its relation to accuracy and cue use. Results. Overall accuracy was close to chance, although participants were more accurate in determining the veracity of truthful relative to deceptive narratives. Accuracy was impaired for emotional (positive and negative) relative to neutral narratives. Psychopathy was not associated with levels of overall accuracy, but related to discriminative ability, and differential use of cues in decision making. Reported cue use also differed across emotional narrative conditions. Conclusions. We speculated that an emotive truth bias may have detracted judges from attending to valid cues that are indicative of the deceptive nature of stimuli because they were distracted by the emotional content of the report. Implications for deception detection in forensic settings are discussed.
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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 | 0.001 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".