More than meets the eye: the role of psychopathic traits in attention to distress
Bibliographic record
Abstract
Although psychopathic traits have long been associated with predation and violence, it is unclear how individuals high in psychopathic traits choose victims. Victim selection and violence perpetration may be facilitated by attention to, or unawareness of, distressful facial expressions. Using a novel eye-tracking paradigm, the present study aimed to identify whether psychopathic traits are associated with unconscious attentional biases to expressions of distress. A sample of 138 undergraduates (23 males, 115 females, Mage = 20.4) viewed 80 paired images portraying a neutral, and authentic expression of either fear, pain, embarrassment, startle, or sadness. Psychopathic traits did not predict initial orientation to distressing over neutral expressions. However, callous-affective traits negatively predicted attentional maintenance to expressions of embarrassment and pain, whereas criminal tendencies and erratic lifestyle positively predicted attentional maintenance to embarrassment and pain, respectively. Findings offer insight into perceptual processing of others’ distress, with implications for violence and victim selection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".