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, that is, between action and
outcome. In cognitive terms, the question becomes one of how people
construct and reason with the causal models we use to represent our
world. A revolution is occuring in how statisticians, philosophers,
and computer scientists answer this question. These 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 question is not merely about probability (or
logic), but about action. The framework offers a new understanding
of mind: Thought is about the effects of intervention, so cognition
is thereby intimately tied to actions that take place either in the
actual physical world or in imagination, in counterfactual worlds.
In this book, Steven Sloman offers a conceptual introduction to the
key mathematical ideas in the framework, presenting them in a
non-technical way, by focusing on the intuitions rather than the
theorems. He tries to show why the ideas are important to
understanding how people explain things, and why it is so central
to human action to think not only about the world as it is, but
also about the world as it could be. Sloman also reviews the role
of causality, causal models, and intervention in the basic human
cognitive functions: decision making, reasoning, judgement,
categorization, inductive inference, language, and learning. In
short, this book offers a discussion about how people think, talk,
learn, and explain things in causal terms - in terms of action and
manipulation.
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