Bayesian Rationality

Oaksford and Chater make a very strong case in favour of a probabilistic view of human reasoning. This publication in therefore highly recommended to any cognitive psychologists, and particularly to master's or doctoral students doing research in this field. * The Psychologist, *
Oaksford and Chater have been at the center of a major reconceptualization of how humans reason. This book explains the deep reasons for this new approach and provides an excellent summary of their work. * Professor John R. Anderson, Carnegie Mellon University, USA *
Oaksford and Chater convincingly argue that rationality in the real world cannot be reduced to logical thinking and demonstrate how apparently logical problems can instead be reconstructed in a probabilistic way. This is an important step towards the ultimate goal of understanding the heuristic mechanisms underlying behavior. An excellent book on a Bayesian approach to cognition. * Professor Gerd Gigerenzer, Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Germany *
For years, Oaksford and Chater have taken a maverick approach to the analysis of human reasoning, applying probabilistic ideas to construct radically new interpretations of what people are doing when they reason and whether or not those actions are rational. The field has started to follow Oaksford and Chater's lead; probabilistic concepts are becoming central to all areas of cognitive science. In this book, Oaksford and Chater offer an exceptionally lucid and compelling introduction to their own work and in the process provide an accessible introduction to a number of technical issues in reasoning. This book is a must for those interested in the latest theoretical ideas in the study of human reasoning. * Professor Steve Sloman, Brown University, USA *
This fascinating book is the capstone of one of the most important and original programs of research on reasoning in the last twenty years. Oaksford and Chater argue persuasively that human thinking is best understood not in terms of how poorly it approximates the philosopher's norms of deductive logic, but rather in terms of how well it captures the more powerful and subtle principles of Bayesian probability. * Professor Josh Tenenbaum, Massachusetts Institute of Technology, USA *