Individual differences in psychopathic traits and identifying mental states and emotions in others
Bibliographic record
Abstract
Psychopathy has gained interest as a research topic in recent years due to the devastating effects it has on society and the failure of traditional rehabilitation techniques to work. Of particular interest is the identification of early indicators of psychopathy in children who may be more susceptible to treatment. There are three distinct factors to psychopathy in childhood-antisocial behaviour, callous-unemotional traits, and narcissism-and it is unclear if these traits exist in various degrees in non-clinical samples and if each factor relates to unique deficits. This study examined how individual differences in these psychopathic traits in typically developing children relate to their competence in identifying both emotional and non-emotional mental states in others. Forty-three children from the Greater Vancouver Area aged 6 to 11 (M = 8yrs 3mos, SD = 1yr 5mos) participated (23 boys, 20 girls). The participants completed an emotion recognition task, a mental state identification task, and an intelligence measure. The parents of the participants completed two well-validated measures of psychopathy in children. The results revealed no relationship between individual differences in these traits and the identification of non-emotional mental states or the overall ability to correctly detect emotions in others. There was, however, a diminished ability to detect negative emotions in those with higher levels of callous-unemotional traits as well as a tendency to incorrectly attribute threat emotions for those with lower levels of psychopathy. These effects were found while controlling for the effects of age, gender, and intelligence. These findings are discussed in relation to current theories of psychopathy and potential avenues for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".