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.
In this chapter, we describe how risk and ambiguity impact the value of choice options, how this impact can be modelled formally and how it is implemented in the brain. In particular, we give an overview of two distinct ways of how risky choice options can be decomposed – either into outcomes and probabilities as proposed in economics or into statistical moments of the probability distribution like mean, variance, or skewness, as proposed in finance theory. The components of either approach appear to be represented in common and, at least to some extent, in separate brain regions, which include the dopaminergic midbrain, striatum and the orbitofrontal cortex. Activity in different (prefrontal and striatal) brain regions also supports the distinction between decisions from experience, when knowledge about risk is learned through trial and error versus decisions from description, when it is described symbolically. The fact that the principal components of formal models from economics and finance theory and their behavioral versions that provide better descriptive fit are represented in the brain provides converging support for these models.
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