Exploring when to exploit: The cognitive underpinnings of foraging-type decisions in relation to psychopathy

Impairments in reinforcement learning (RL) might underlie the tendency of individuals with elevated psychopathic traits to behave exploitatively, as they fail to learn from their mistakes. Most studies on the topic have focused on binary choices, while everyday functioning requires us to learn the value of multiple options. In this study, we evaluated the cognitive correlates of naturalistic foraging-type decision-making and their electrophysiological signatures in a community sample (n=108) with varying degrees of psychopathic traits. The latent cognitive processes in RL from reinforcers with different salience were estimated with a computational model, and their relationship to psychopathic traits assessed. Higher Antisocial traits were associated with a bias towards expecting more volatility in the environment when high-salience reinforcers were used. Additionally, higher levels of Interpersonal traits were associated with reduced learning from personalized rewards as evidenced by reductions in the prediction errors (PEs) about rate of change. Higher Affective traits were, on the other hand, associated with lower PEs and aberrant learning from painful punishments. Lastly, the PEs about rate of change were reflected in the trialwise trajectories of Feedback-Related Negativity event-related potentials. Together, our results point to the importance of volatility processing in understanding aberrant decision-making in psychopathy, demonstrate the specific impact of different psychopathic traits on reward and punishment learning, and emphasise the potentially more beneficial effect of personalized rewards and punishment for improving reinforcement-based decision-making in individuals with elevated psychopathic traits.