Skin Conductance Responses to a Discrete Threat in Virtual Reality: Associations with Psychopathy and Anxiety
Bibliographic record
Abstract
Abstract People with high levels of psychopathic traits are often described as fearless and lacking in emotional depth, particularly when evaluating threats in their environments. Skin conductance responsivity (SCR) to negative emotional stimuli represents a robust autonomic correlate of conduct problem behavior in children (Fanti et al., in Neuroscience and Biobehavioral Reviews, 100, 98–107, 2019). However, studies that have examined threat-related processing in youth with conduct problems have tended to use a variety of negative stimuli that might induce various and unspecific negative emotions. Few studies have taken in to account the moderating effects of anxiety on the relationship of distinct psychopathic traits (e.g., narcissism, callousness, impulsivity) with SCR to a fear inducing stimulus. In this study, we examined SCR to a virtual reality rollercoaster drop – that is, a discrete fear inducing event – in a sample of 75 youths (61 males; M = 14 years, SD = 1.4) enrolled in a non-mainstream school. The rollercoaster drop was used to more clearly examine an event-related response to a discrete threat, rather than examining SCR throughout the rollercoaster ride. We used the teacher-reported Antisocial Process Screening Device (Frick & Hare, in Antisocial process screening device: APSD. Toronto: Multi-Health Systems, 2001) to examine the relations of distinct psychopathic traits with SCR and self-reported anxiety. Lower anxiety was associated with higher callousness, but only in youths with low SCR to discrete threat. These findings suggest that fear and anxiety show complex and interactive relations with distinct psychopathic traits.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".