Dissecting how psychopathic traits are linked to learning in different contexts: A multilevel computational and electrophysiological approach
Bibliographic record
Abstract
Previous studies suggest that elevated psychopathic traits, linked to social norm violations and personal gain-seeking, may be caused by impairments in associative learning. Recent advances in computational modelling offer insight into the unobservable processes that are thought to underly associative learning. Using such a model, the present study investigated the associations between psychopathic traits in a nonoffender sample and the cognitive computations underlying adaptive behavior during associative learning. We also investigated the potential engagement of adaptive control processes by measuring oscillatory theta activity in the prefrontal cortex. Participants performed a reinforcement learning task in which the trade-off between using social and nonsocial information affected task performance and the associated monetary reward. The findings indicated that increasing levels of psychopathic traits co-occurred with reduced learning from social information and suggested that antisocial traits were linked to a reduced ability to track changes in the trustworthiness of social advice over time. This did not affect the preference for one information source and the risk taken to obtain a high reward. Furthermore, midfrontal theta power was negatively linked to levels of psychopathic traits, aligning with indications that theta is involved in volatility tracking of social information. Importantly, we consider that the task design may reflect reduced sensitivity to secondary, rather than specifically social information. The current study provides support for a relationship between associative learning, theta power, and psychopathic traits and contributes to our understanding of the mechanisms that may explain reduced responsiveness to current treatment interventions in individuals with psychopathy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".