Steven Sloman

Causal Models

How People Think about the World and Its Alternatives

• Gebundenes Buch

Produktbeschreibung

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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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.

Produktdetails

• Verlag: Oxford University Press, USA
• Seitenzahl: 224
• Erscheinungstermin: 28. Juli 2005
• Englisch
• Abmessung: 234mm x 156mm x 14mm
• Gewicht: 499g
• ISBN-13: 9780195183115
• ISBN-10: 0195183118
• Artikelnr.: 22149329

Autorenporträt

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.

Inhaltsangabe

* 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 causal models
* 12: Causal Learning
* Covariation-based theories of causal learning
* Structure before strength
* Insufficency of covariational data
* Cues to causal structure
* Conclusion
* Conclusion
* 13: Causation in the mind
* Assessing the causal model framework
* Cognition is for action
* What causal models can contribute to human welfare
* The human mechanism