Abstract
With the movements toward the legalization of casinos in Japan, gambling disorder is drawing attention as a significant social issue. Here, this article reviews an attempt to examine the differences in decision‐making processes between individuals with gambling disorder and healthy controls through the application of reinforcement learning models to behavioral and neuroimaging data. First, I provide an overview of computational neuroscience research, particularly focusing on decision‐making and reinforcement learning. The concept of “lack of behavioral flexibility,” which is closely linked to a core symptom of gambling disorder (i. e., difficulty in stopping gambling despite adverse outcomes) , is then discussed within the context of reinforcement learning. Finally, I present our recent study in computational psychiatry, which explores the neural basis of behavioral inflexibility in individuals with gambling disorder by combining reinforcement learning and functional magnetic resonance imaging (fMRI) . No potential conflictis of interest were disclosed.
