The authors explore the division of labor between the basal ganglia-dopamine (BG-DA) system and the orbitofrontal cortex (OFC) in decision making. They show that a primitive neural network model of the BG-DA system slowly learns to make decisions on the basis of the relative probability of rewards but is not as sensitive to (a) recency or (b) the value of specific rewards. An augmented model that explores BG-OFC interactions is more successful at estimating the true expected value of decisions and is faster at switching behavior when reinforcement contingencies change. In the augmented model, OFC areas exert top-down control on the BG and premotor areas by representing reinforcement magnitudes in working memory. The model successfully captures patterns of behavior resulting from OFC damage in decision making, reversal learning, and devaluation paradigms and makes additional predictions for the underlying source of these deficits.Keywords: decision making, neural network, basal ganglia, orbitofrontal cortex, reinforcement learning What enables humans to make choices that lead to long-term gains, even when having to incur short-term losses? Such decisionmaking skills depend on the processes of action selection (choosing between one of several possible responses) and reinforcement learning (modifying the likelihood of selecting a given response on the basis of experienced consequences). Although all mammals can learn to associate their actions with consequences, humans are particularly advanced in their ability to flexibly modify the relative reinforcement values of alternative choices to select the most adaptive behavior in a particular behavioral, spatial, and temporal context.The behavioral and cognitive neurosciences have identified two neural systems that are involved in such adaptive behavior. On the one hand, the basal ganglia (BG) and the neuromodulator dopamine (DA) are thought to participate in both action selection and reinforcement learning