infonet-economy

In order to choose advantageously in many circumstances, the values of choice alternatives have to be learned from experience. We provide an introduction to theoretical and experimental work on reinforcement learning, that is, trial-and-error learning to obtain rewards or avoid punishments. We introduce one version, the temporal-difference learning model, and review evidence that its predictions relate to the firing properties of midbrain dopamine neurons and to activity recorded with functional neuroimaging in humans. We also present evidence that this computational and neurophysiological mechanism affects human and animal behavior in decision and conditioning tasks.