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Computational Perspectives on Gambling Disorder
December 12, 2020 at 12:00 pm
Jan Perters, Prof. Dr. Department Psychologie, Universität zu Köln
Host: Ulrich Ettinger, Prof. Dr.
Gambling disorder is a behavioral addiction that shares core features with substance-use disorders, including impairments in reinforcement learning, cognitive control and behavioral flexibility. Here I will present recent findings from out group showing that a computational perspective can reveal novel insights into the underlying impairment profile. First, I will show that reduced behavioral flexibility during reinforcement learning in dynamic environments in gamblers can be traced to a reduction in strategic (uncertainty-based) exploration. This is associated with connectivity changes in a frontoparietal network. Gamblers also show impaired learning in simple static environments, which using sequential sampling models can be traced to a change in the speed-accuracy trade off. Next, I will discuss temporal discounting, which refers to the de-valuation of reward value over time. Alterations in temporal discounting are associated with a range of disorders, including substance use disorders and behavioral addictions. I will first show that temporal discounting can be accurately modeled using the framework of sequential sampling models. I then show an application of this modeling scheme in a replication of a classical finding in gambling disorder, showing that gambling-related environments potentiate temporal discounting.