Joint Models of Neural and Behavioral Data

Brandon Turner is an Assistant Professor in the Psychology Department at The Ohio State University. His research interests include dynamic models of cognition and perceptual decision making, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition. His current focus is on understanding how external factors such as the environment, and internal factors such as working memory interact to shape an observer's perception of the world, and ultimately how this perception drives their decisions. Dr. Turner is an editorial board member for the journal Decision.Birte U. Forstmann is a Professor for Cognitive Neurosciences at the University of Amsterdam. She earned her PhD in 2006 at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. After completing her postdoc in 2008 at the University of Amsterdam, she became tenured Research Fellow at the Cognitive Science Center Amsterdam with the focus of model-based cognitive neurosciences. Since then she has contributed to a range of topics in cognitive neuroscience, experimental psychology, mathematical psychology, and lately also in quantiative neuroanatomy. Despite its diversity, her work is motivated by a single strong conviction, namely that behavioral data and brain measurements need to make contact with psychological theory via concrete mathematical models of latent cognitive processes. Mark Steyvers is a professor in the Department of Cognitive Sciences at the University of California, Irvine. He also holds joint faculty appointments with the Department of Computer Science and the Department of Psychology and Social Behavior at the University of California, Irvine. Dr. Steyvers is Associate Editor for Computational Brain & Behavior and consulting editor for Psychological Review and an editorial board member for Behavior Research Methods.

• Chapter 1. Motivation.- Chapter 2. A Tutorial on Joint Modeling.- Chapter 3. Assessing Model Performance with Generalization Tests.- Chapter 4. Applications.- Chapter 5. Future Directions.- Chapter 6. Other Approaches.