Diffusion Process Models of Decision Making: Volume 1: Fundamental Processes [Paperback]

An extensive treatment of the theory, mathematical foundations, and applications of diffusion process models in psychology and neuroscience.Diffusion process models are widely used in psychology and neuroscience to understand the processes involved in making rapid decisions, both in the laboratory and in clinical and applied settings. This book is an essential resource for students and specialists who seek a detailed understanding of the models and their applications.Diffusion process models are widely used in psychology and neuroscience to understand the processes involved in making rapid decisions, both in the laboratory and in clinical and applied settings. This book is an essential resource for students and specialists who seek a detailed understanding of the models and their applications.Diffusion decision models are widely used to characterize the cognitive and neural processes involved in making rapid decisions about objects and events in the environment. These decisions, which are made hundreds of times a day without prolonged deliberation, include recognition of people and things as well as real-time decisions made while walking or driving. Diffusion models assume that the processes involved in making such decisions are noisy and variable and that noisy evidence is accumulated until there is enough for a decision. This volume provides the first comprehensive treatment of the theory, mathematical foundations, numerical methods, and empirical applications of diffusion process models in psychology and neuroscience. In addition to the standard Wiener diffusion model, readers will find a detailed, unified treatment of the cognitive theory and the neural foundations of a variety of dynamic diffusion process models of two-choice, multiple choice, and continuous outcome decisions.Preface; 1. Overview; 2. Basic concepts and data; 3. Sequential-sampling models of decision making; 4. Obtaining predictions for diffusion models; 5. Empirical assessment of sequential-saml“"