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The basal ganglia and dopaminergic systems are well studied for their roles in reinforcement learning and reward-based decision making. Much work focuses on “reward prediction error” (RPE) signals conveyed by dopamine and used for learning. Computational considerations suggest that such signals may be enriched beyond the classical global and scalar RPE computation to support more structured learning in distinct sub-circuits (“vector RPEs”). I will present experimental data from both mouse (calcium imaging of dopamine terminals in striatum) and humans which provide preliminary support of this notion.
