Computational modeling is gaining increasing popularity in the field of psychiatry. By applying a machine learning framework known as reinforcement learning (RL), researchers in the domain of computational psychiatry have begun to shed light on the latent decision-making processes that are altered in mental disorders. In this chapter, we first explain how RL can be used to characterize the neurocomputational processes of decision-making and learning. To illustrate this approach, we focus on a set of studies that employ a basic class (...) of RL, known as model-free RL, to gain insights into the psychopathology of obsessive–compulsive and gambling disorders. Lastly, we review a common model-fitting procedure used in computational psychiatry research and offer a practical guide, also considering potential pitfalls. This encompasses discussions on estimating model parameters (parameter estimation), comparing multiple models based on their goodness of fits (model comparison), and testing the robustness of model-fitting procedures using synthesized data (model and parameter recovery analyses). Given the rapid growth of this area of study, we believe that these discussions will be of great value to authors, reviewers, and readers of computational psychiatry literature. (shrink)

People sometimes persistently pursue hard-to-get targets. Why people pursue such targets is unclear. Here, we hypothesized that choice perseverance, which is the tendency to repeat the same choice independent of the obtained outcomes, leads individuals to repeatedly choose a hard-to-get target, which consequently increases their preference for the target. To investigate this hypothesis, we conducted an online experiment involving an avatar choice task in which the participants repeatedly selected one avatar, and the selected avatar expressed their valence reactions through facial (...) expressions and voice. We defined “hard-to-get” and “easy-to-get” avatars by manipulating the outcome probability such that the hard-to-get avatars rarely provided a positive reaction when selected, while the easy-to-get avatars frequently did. We found that some participants repeatedly selected hard-to-get avatars. Based on a simulation, we found that higher choice perseverance accounted for the pursuit of hard-to-get avatars and that the Pursuit group had significantly higher choice perseverance than the No-pursuit group. Model fitting to the choice data also supported that choice perseverance can account for the pursuit of hard-to-get avatars in the Pursuit group. Moreover, we found that although baseline attractiveness was comparable among all avatars used in the choice task, the attractiveness of the hard-to-get avatars was significantly increased only in the Pursuit group. Taken together, we conclude that people with high choice perseverance pursue hard-to-get targets, rendering such targets more attractive. The tolerance for negative outcomes might be an important factor for succeeding in our lives but sometimes triggers problematic behavior, such as stalking. The present findings may contribute to understanding the psychological mechanisms of passion and perseverance for one’s long-term goals, which are more general than the romantic context imitated in avatar choice. (shrink)