Reduced Model-Based Control in Gambling Disorder Despite Intact Neural Value and Task Structure Representations

Disordered gambling has been linked to impairments in goal-directed (model-based) control and reinforcement learning. Here we investigated the potential neural basis of this impairment using a sequential reinforcement learning task (modified two-step-task), computational modeling, and functional magnetic resonance imaging (fMRI) in individuals exhibiting symptoms of disordered gambling (GD) and matched healthy controls (HC, n=30 per group). Model-agnostic analyses replicated the effects of reduced performance and reduced model-based control in the gambling group, both in terms of choice and response time effects. Computational modeling of choice behavior confirmed that this effect was due to reduced model-based control in the gambling group. Analyses of choices and response times using drift diffusion modeling revealed a more complex pattern, where behavioral impairments in the gambling group were linked to changes across several parameters reflecting drift rate modulation and asymptote, as well as non-decision time. Despite these pronounced behavioral differences, the gambling group exhibited largely intact neural effects related to the task transition structure, reward feedback and trial-to-trial behavioral adjustments. Results are discussed with respect to current neurocomputational models of behavioral dysregulation in disordered gambling.