A Neurocomputational Model of Dopamine and Prefrontal–Striatal Interactions during Multicue Category Learning by Parkinson Patients

Abstract

Abstract■ Most existing models of dopamine and learning in Parkinson disease (PD) focus on simulating the role of basal ganglia dopamine in reinforcement learning. Much data argue, however, for a critical role for prefrontal cortex (PFC) dopamine in stimulus selection in attentional learning. Here, we present a new computational model that simulates performance in multicue category learning, such as the "weather prediction" task. The model addresses how PD and dopamine medications affect stimulus selection processes, which mediate reinforcement learning. In this model, PFC dopamine is key for attentional learning, whereas basal ganglia dopamine, consistent with other models, is key for reinforcement and motor learning. The model assumes that competitive dynamics among PFC neurons is the neural mechanism underlying stimulus selection with limited attentional resources, whereas competitive dynamics among striatal neurons is the neural mechanism underlying action selection. According to our model, PD is associated with decreased phasic and tonic dopamine levels in both PFC and basal ganglia. We assume that dopamine medications increase dopamine levels in both the basal ganglia and PFC, which, in turn, increase tonic dopamine levels but decrease the magnitude of phasic dopamine signaling in these brain structures. Increase of tonic dopamine levels in the simulated PFC enhances attentional shifting performance. The model provides a mechanistic account for several phenomena, including (a) medicated PD patients are more impaired at multicue probabilistic category learning than unmedicated patients and (b) medicated PD patients opt out of reversal when there are alternative and redundant cue dimensions. ■

BDNF val66met genotype and schizotypal personality traits interact to influence probabilistic association learning

“…Finally, we confirmed that the multicue strategy was the optimal strategy, which supports previous findings on strategy use during probabilistic association learning [41,51]. We also found that those with a low expression of schizotypal personality traits used the more optimal multi-cue strategy which is consistent with displaying a significantly better performance.…”

Dissociating the Cognitive Effects of Levodopa versus Dopamine Agonists in a Neurocomputational Model of Learning in Parkinson’s Disease

“…For example, prior models do not simulate dissociable effects of different PD medications on brain and cognition [52,67]. Most past models have also ignored any potential function of D 2 receptors in the PFC in working memory [67,68], arguing for a more important role for PFC D 1 receptors.…”

“…However, the critic is not informed about the action that the actor selects, but is informed about whether the selected action culminated in a rewarding consequence or not. The temporal difference model is utilized to train the model [52]. …”

“…We simulated the effects of phasic dopamine by manipulating the learning rate parameter in the PFC and striatal modules. We also simulated the effects of tonic dopamine by manipulating the effects of gain parameter in sigmoidal activation in the simulated brain region [52]. …”

A Trade-Off between Feedback-Based Learning and Episodic Memory for Feedback Events: Evidence from Parkinson’s Disease

“…More recently, it has been recognized that cognitive functions are impacted as well [3,4,5,6,7,8,9,10]. Understanding the precise nature of these cognitive symptoms and how they are affected by treatment for PD is of tremendous interest to both basic and clinical research.…”