Reviews
Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as p
Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we haveavailable a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who areactively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophyduring the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use ofcounterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge.People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES
Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we haveavailable a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who areactively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophyduring the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use ofcounterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficulttechnical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence ofa truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications rangingfrom how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and whichcan be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judgingcausal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences.Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a goodstarting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge.People who are activelyworking in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES
Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers. --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology<br> In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple. --Richard Scheines, Professor of Philosophy, Carnegie Mellon University<br> The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we haveavailable a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it. --Reid Hastie, Professor of Behavioral Science, University of Chicago<br> The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining. --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick<br> Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who areactively working in this area will probably find this book a quick and enjoyable read. --Michael Palij, PsycCRITIQUES<br>
