Summary The role that frontal-striatal circuits play in normal behavior remains unclear. Two of the leading hypotheses suggest that these circuits are important for action selection or reinforcement learning. To examine these hypotheses we carried out an experiment in which monkeys had to select actions in two different task conditions. In the first (random) condition actions were selected on the basis of perceptual inference. In the second (fixed) condition the animals used reinforcement from previous trials to select actions. Examination of neural activity showed that the representation of the selected action was stronger in lateral prefrontal cortex (lPFC), and occurred earlier in the lPFC than it did in the dorsal striatum (dSTR). In contrast to this, the representation of action values, in both the random and fixed conditions was stronger in the dSTR. Thus, the dSTR contains an enriched representation of action value, but it followed frontal cortex in action selection.

We were interested in gaining insight into the functional properties of frontal networks based upon their anatomical inputs. We took a neuroinformatics approach, carrying out maximum likelihood hierarchical cluster analysis on 25 frontal cortical areas based upon their anatomical connections, with 68 input areas representing exterosensory, chemosensory, motor, limbic, and other frontal inputs. The analysis revealed a set of statistically robust clusters. We used these clusters to divide the frontal areas into 5 groups, including ventral-lateral, ventral-medial, dorsal-medial, dorsal-lateral, and caudal-orbital groups. Each of these groups was defined by a unique set of inputs. This organization provides insight into the differential roles of each group of areas and suggests a gradient by which orbital and ventral-medial areas may be responsible for decision-making processes based on emotion and primary reinforcers, and lateral frontal areas are more involved in integrating affective and rational information into a common framework.

Decisions are often driven by a combination of immediate perception and previous experience. In this study, we investigated how these two sources of information are integrated and the neural systems that mediate this process. Specifically, we injected a dopamine type 1 antagonist (D1A; SCH23390) or a dopamine type 2 antagonist (D2A; eticlopride) into the dorsal striatum while macaques performed a task in which their choices were driven by perceptual inference and/or reinforcement of past choices. We found that the D2A affected choices based on previous outcomes. However, there were no effects of the D2A on choices driven by perceptual inference. We found that the D1A did not affect perceptual inference or reinforcement learning. Finally, a Bayesian model applied to the results suggested that the D2A may be increasing noise in the striatal representation of value, perhaps by disrupting the striatal population that normally represents value.