Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions

Abstract

Sequential sampling models assume that people make speeded decisions by gradually accumulating noisy information until a threshold of evidence is reached. In cognitive science, one such model—the diffusion decision model—is now regularly used to decompose task performance into underlying processes such as the quality of information processing, response caution, and a priori bias. In the cognitive neurosciences, the diffusion decision model has recently been adopted as a quantitative tool to study the neural basis of decision making under time pressure. We present a selective overview of several recent applications and extensions of the diffusion decision model in the cognitive neurosciences.