Chapter 3 describes the variety of sequential-sampling models of decision making that have been proposed in the literature and situates diffusion process models among them. The chapter provides a taxonomy of model types, in which models are classified according to their stopping rule and whether evidence is accumulated in discrete or continuous amounts in discrete or continuous time. Historically important models have been the recruitment model, the Vickers accumulator model, the Poisson counter model, and several random walk models, including one that implements the sequential probability ratio test, which accumulates the log-likelihood ratios of the evidence states. The Wiener diffusion process assumes that evidence is continuously distributed and accumulated in continuous time, and that decisions are made using a relative stopping rule. The chapter describes two alternative ways to characterize diffusion processes mathematically and obtain predictions for them, which are evaluated in Chapter 5: one using partial differential equations and the other using stochastic differential equations.