Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task.

Despite progress in smoking reduction in the past several decades, cigarette smoking remains a significant public health concern world-wide, with many smokers attempting but ultimately failing to maintain abstinence. However, little is known about how decision-making evolves in quitting smokers. Based on preregistered hypotheses and analysis plan ( https://osf.

A hierarchical reinforcement learning model explains individual differences in attentional set shifting.

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks, such as the widely used CANTAB IED, reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability and how they lead to the observed variation remain unknown.

Characterizing the dynamics, reactivity and controllability of moods in depression with a Kalman filter.

Background: Mood disorders involve a complex interplay between multifaceted internal emotional states, and complex external inputs. Dynamical systems theory suggests that this interplay between aspects of moods and environmental stimuli may hence determine key psychopathological features of mood disorders, including the stability of mood states, the response to external inputs, how controllable mood states are, and what interventions are most likely to be effective. However, a comprehensive computational approach to all these aspects has not yet been undertaken.