This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience. The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition). The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology-including cognitive science and related social and behavioral sciences such as consumer behavior and communication-will find the text useful.

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

This book constitutes the refereed proceedings of the 6th International Symposium on Quantum Interaction, QI 2012, held in Paris in June 2012. The 21 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers cover various topics on quantum interaction.

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

The emerging interdisciplinary field of cognitive choice models integrates theory and recent research findings from both decision process and choice behavior. Cognitive decision processes provide the interface between the environment and brain, enabling choice behavior, and the basic cognitive mechanisms underlying decision processes are fundamental to all fields of human activity. Yet cognitive processes and choice processes are often studied separately, whether by decision theorists, consumer researchers, or social scientists. In Cognitive Choice Modeling, Zheng Joyce Wang and Jerome R. Busemeyer introduce a new cognitive modeling approach to the study of human choice behavior. Integrating recent research findings from both cognitive science and choice behavior, they lay the groundwork for the emerging interdisciplinary field of cognitive choice modeling.

Cognitive Modeling is the first book to provide students with an easy-to understand introduction to the basic methods used to build and test cognitive models. Authors Jerome R. Busemeyer and Adele Diederich answer many of the questions that researchers face when beginning work on cognitive models, such as the following: What makes a cognitive model different from conceptual or statistical models? How do you develop such a model? How can you derive qualitatively different predictions between two cognitive models? Focusing on a few key representations, the authors introduce a basic problem in each chapter, illustrate the concept with three examples, and end with a summary of general principles, making this book by far the most accessible cognitive modeling book on the market. Key Features Emphasizes modeling by presenting the tools needed to build a cognitive model, rather than simply reviewing existing models of cognition Provides tutorial presentations of psychological, mathematical, statistical, and computational methods used in all areas of cognitive modeling Includes detailed examples applied to real cognitive models published in the literature in a variety of areas, including recognition, categorization, decision making, and learning Stresses the importance of designing the right conditions for evaluating models Addresses the issues of individual differences in cognitive modeling head-on

Cognitive Modeling is ideal for students and researchers across the various domains of cognitive sciences, including perception, learning, decision making, and inference. It is intended for use in upper-level undergraduate and graduate courses such as Cognition/Cognitive Modeling, Cognitive Science, Cognitive Psychology, Quantitative Methods, and Mathematical Modeling in Psychology.