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Informationen zum Autor Steven Sloman 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 many papers and a book about human cognition on topics ranging from categorization and memory to decision-making, inductive inference, and reasoning. Klappentext This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. 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. Zusammenfassung This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. 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. Inhaltsverzeichnis 1: Agency and the role of causation in mental life The High Church of Cognitive Science: A heretical view Agency is the ability to represent causal intervention The purpose of this book Plan of the book Part 1: The theory 2: The information is in the invariants Selective attention Selective attention focuses on invariants In the domain of events, causal relations are the fundamental invariants 3: What is a cause? Causes and effects are events Experiments versus observations Causal relations imply certain counterfactuals Enabling, disabling, directly responsible: Everything's a cause Problems, problems Could it be otherwise? Not all variance is causal 4: Causal models The 3 parts of a causal model Independence Structural equations What does it mean to say causal relations are probabilistic? Causal structure produces a probabilistic world: Screening off Equivalent causal models The technical advantage: How to use a graph to simplify probabilities 5: Observation versus Action Seeing the representation of observation Action: The representation of intervention Acting and thinking by doing: Graphical surgery Computing with the do operator The value of experiments: A reprise The causal modeling framework and levels of causality Part 2: Evidence and application 6: Reasoning about causation Mathematical reasoning about causal systems Social attribution and explanation discounting Counterfactual reasoning: The logic of doing Conclusion 7: Decision making via Causal Consequences Making Decisions The gambling metaphor Deciding by causal explanation Newcomb's Paradox: Causal trumps evidential expected utility The facts: People care about causal structure When causal knowledge isn't enough 8: The psychology of judgement: Causality is pervasive Causal models as a psychological theory: Knowlege is qualitative The causality heuristic and mental stimulation Belief perseveration Seeing causality when it's not there Causal models and legal relevance Conclusion 9: Causality and Conceptual Structure Inference over perception The role of function in artifact categorization Causal models of conceptual structure Some implications Causal versus other kinds of relations Basic-level categories and typical instances 10: Categorical Induction Induction and causal models Argument strength mediated by causal knowledge Causal analysis versus counting instances: The inside versus the outside Conclusion 11: Locating Causal Structure in Language Pronouns Conjunctions If The value of causa...
