Description
Book SynopsisHuman 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 i
Table of Contents1. Agency and the Role of Causation in Mental Life ; Part I. The Theory ; 2. The Information Is in the Invariants ; 3. What Is a Cause? ; 4. Causal Models ; 5. Observation Versus Action ; Part II. Evidence and Application ; 6. Reasoning About Causation ; 7. Decision Making via Causal Consequences ; 8. The Psychology of Judgment: Causality Is Pervasive ; 9. Causality and Conceptual Structure ; 10. Categorical Induction ; 11. Locating Causal Structure in Language ; 12. Causal Learning ; 13. Conclusion: Causation in the Mind ; Notes ; References ; Index
