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Books > Social sciences > Psychology > Cognition & cognitive psychology
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Casual Models - How People Think About the World and Its Alternatives (Paperback)
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Casual Models - How People Think About the World and Its Alternatives (Paperback)
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Human beings are active agents who can think. To understand 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
causal structure is to understand and predict the effects of
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 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 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
"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
"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
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