The basal ganglia in reward and decision making: computational models and empirical studies

Publisher Summary In recent years, computational models of learning and decision-making have become increasingly prevalent in psychology and neuroscience. These models describe brain function across a wide range of levels, from highly detailed models of ion channels and compartments of individual neurons, to abstract models that focus on the cognitive machinations the brain appears to produce. This chapter reviews a series of neurocomputational models that focus on the action selection and reinforcement learning functions of basal ganglia (BG), and their modulation by dopamine, as constrained by a broad range of data. The models have been successful in predicting behavioral outcomes resulting from manipulations of BG functionality via medications, diseases, disorders, and genetics. Furthermore, the chapter discusses how core computational principles can be extracted from complex neural models to develop simplified models in abstract mathematical form, which in turn can be quantitatively fit to behavioral data to test specific hypotheses. Such models are also useful for deriving best-fitting model parameters to correlate with biological signals, which can be used for further refinement and development of mechanistic principles.