Dissecting how psychopathic traits are linked to learning in different contexts: A multilevel computational and...

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.