We present a neural network model of cognition in medicated and unmedicated patients with Parkinson's disease (PD) in various learning and memory tasks. The model extends our prior models of the basal ganglia and PD with further modeling of the role of prefrontal cortex (PFC) dopamine in stimulus-response learning, reversal, and working memory. In our model, PD is associated with decreased dopamine levels in the basal ganglia and PFC, whereas dopamine medications increase dopamine levels in both brain structures. Simulation results suggest that dopamine medications impair stimulus-response learning in agreement with experimental data ( Breitenstein etal., 2006; Gotham, Brown, & Marsden, 1988). We show how decreased dopamine levels in the PFC in unmedicated PD patients are associated with impaired working memory performance, as seen experimentally ( Costa etal., 2003; Lange etal., 1992; Moustafa, Sherman, & Frank, 2008; Owen, Sahakian, Hodges, Summers, & Polkey, 1995). Further, our model simulations illustrate how increases in tonic dopamine levels in the PFC due to dopamine medications will enhance working memory, in accord with previous modeling and experimental results ( Cohen, Braver, & Brown, 2002; Durstewitz, Seamans, & Sejnowski, 2000; Wang, Vijayraghavan, & Goldman-Rakic, 2004). The model is also consistent with data reported in Cools, Barker, Sahakian, and Robbins (2001), who showed that dopamine medications impair reversal learning. In addition, our model shows that extended training of the reversal phase leads to enhanced reversal performance in medicated PD patients, which is a new, and as yet untested, prediction of the model. Overall, our model provides a unified account for performance in various behavioral tasks using common computational principles.