General trust impedes perception of self-reported primary psychopathy in thin slices of social interaction
Bibliographic record
Abstract
Little is known about people's ability to detect subclinical psychopathy from others' quotidian social behavior, or about the correlates of variation in this ability. This study sought to address these questions using a thin slice personality judgment paradigm. We presented 108 undergraduate judges (70.4% female) with 1.5 minute video thin slices of zero-acquaintance triadic conversations among other undergraduates (targets: n = 105, 57.1% female). Judges completed self-report measures of general trust, caution, and empathy. Target individuals had completed the Levenson Self-Report Psychopathy (LSRP) scale. Judges viewed the videos in one of three conditions: complete audio, silent, or audio from which semantic content had been removed using low-pass filtering. Using a novel other-rating version of the LSRP, judges' ratings of targets' primary psychopathy levels were significantly positively associated with targets' self-reports, but only in the complete audio condition. Judge general trust and target LSRP interacted, such that judges higher in general trust made less accurate judgments with respect to targets higher in primary and total psychopathy. Results are consistent with a scenario in which psychopathic traits are maintained in human populations by negative frequency dependent selection operating through the costs of detecting psychopathy in others.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".