Trials and Tribulations: Psychopathic Traits, Emotion, and Decision-Making in an Ambiguous Case of Sexual Assault
Bibliographic record
Abstract
Judgments of criminal culpability often are influenced by factors unrelated to case content, such as the emotionality of the victim and the personality of the judge. In the current study, we investigated the relationship between psychopathic traits (high/low) and information processing modes (experiential vs. rational) in a group of mock jurors (N = 383) asked to judge a “he said, she said” ambiguous case of sexual assault that varied according to both victim and defendant emotionality (high/low). The results demonstrated that victim and defendant emotionality was critical in determining case outcomes, which interacted with the processing style that participants utilized more. Specifically, experiential processors were more punitive towards the defendant when the defendant displayed low levels of emotion relative to high emotionality, whereas rational processors were slightly more punitive when high levels of emotion were being displayed. Psychopathic traits had no influence on ratings of veracity/credibility of the victim and defendant, or on overall guilt determinations and severity of sentencing. However, participants high in psychopathic traits believed that the alleged victim was making a false allegation more often when she was less emotional, and they were less punitive towards the false allegation than individuals low in psychopathic traits. These findings have important implications concerning how cases of sexual assault are interpreted in court, and extra-legal factors that may alter case outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".