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Oxford University Press, USA
Causal Models: How People Think about the World and Its Alternatives
Causal Models: How People Think about the World and Its Alternatives
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Human beings are active agents who can think. Understanding how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. In cognitive terms, how do people construct and reason with the causal models we use to
represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and
probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of the causal structure is to understand and predict the effects of the intervention. How does intervening on one thing affect other things? This is not a question merely about
probability (or logic), but about action. The framework offers a new understanding of the mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a
conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world
as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion
about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.
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
Elegant and entertaining.--Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick
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 presents 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.
represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and
probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of the causal structure is to understand and predict the effects of the intervention. How does intervening on one thing affect other things? This is not a question merely about
probability (or logic), but about action. The framework offers a new understanding of the mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a
conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world
as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion
about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.
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
The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides a 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
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 presents 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.
Author: Steven Sloman
Publisher: Oxford University Press, USA
Published: 04/17/2009
Pages: 224
Binding Type: Paperback
Weight: 0.85lbs
Size: 9.10h x 6.00w x 0.60d
ISBN: 9780195394290
About the Author:
Steven Sloman, Professor of Psychology, at Brown University, has been on the faculty in Cognitive and Linguistic Sciences at Brown University since 1992. He completed his undergraduate studies at the University of Toronto in 1986 and received a Ph.D. in Psychology from Stanford in 1990. He has published a book and many articles about human cognition on topics ranging from categorization and memory to decision making, inductive inference, and reasoning.
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