P648 | 27440 | Busemeyer, J.

Choice is the behavioral interface between the brain and the environment, and consequently, understanding this basic process is fundamental to all fields of human activity. This course will begin with what is considered the optimal way of making choices – expected utility theory -- which prescribes how to combine beliefs with values. The synthesis expected utility with Bayesian inference produces the well known signal detection model of decision making. However, the signal detection model is a static model, and its dynamic extension is known as the random walk or diffusion model of decision making. Random walk/diffusion models have been used to model decisions ranging from sensation, perception, memory, categorization, and even consumer preferences. Artificial neural network models of choice can be viewed as nonlinear extensions of diffusion models. This course will link together all of these theories, describe how to use these models to explain data, and review empirical for and against these theories.