Causal Learning: Psychology, Philosophy, and ComputationAlison Gopnik, Laura Schulz Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. |
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Page 10
... intervention and produce the completed action when they have seen only a failed attempt (Meltzoff, 1995). Such research suggests that young children can learn the causal relation between human actions and the events that follow them ...
... intervention and produce the completed action when they have seen only a failed attempt (Meltzoff, 1995). Such research suggests that young children can learn the causal relation between human actions and the events that follow them ...
Page 17
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. Part I CAUSATION AND INTERVENTION This page intentionally left blank Interventionist Theories of Causation in PART I: CAUSATION AND INTERVENTION.
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. Part I CAUSATION AND INTERVENTION This page intentionally left blank Interventionist Theories of Causation in PART I: CAUSATION AND INTERVENTION.
Page 20
... interventions (ideal manipulations) that change the value of X such that (b) if such an intervention (and no others) were to occur X and Y would be correlated, then X causes Y. (NC) If X causes Y, then (a) there are possible interventions ...
... interventions (ideal manipulations) that change the value of X such that (b) if such an intervention (and no others) were to occur X and Y would be correlated, then X causes Y. (NC) If X causes Y, then (a) there are possible interventions ...
Page 21
... intervention in the literature; including those by Spirtes, Glymour, and Scheines (2000); Pearl (2000); and Woodward (2003). Because the difference between these formulations will not be important for what follows, I focus on the core ...
... intervention in the literature; including those by Spirtes, Glymour, and Scheines (2000); Pearl (2000); and Woodward (2003). Because the difference between these formulations will not be important for what follows, I focus on the core ...
Page 22
... interventions and human (and animal) manipulation is thus important to the empirical psychology of causal judgment and learning, even though the notion of an intervention is not defined by reference to human action. Second, note that ...
... interventions and human (and animal) manipulation is thus important to the empirical psychology of causal judgment and learning, even though the notion of an intervention is not defined by reference to human action. Second, note that ...
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Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
Causal Learning: Psychology, Philosophy, and Computation Alison Gopnik,Laura Schulz Limited preview - 2007 |
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actions adults algorithms Bayesian inference Bayesian networks behavior beliefs birth control pills blicket detector Cambridge causal Bayes nets causal chain causal inference causal knowledge causal learning causal Markov condition causal model causal networks causal power causal reasoning causal relations causal relationships causal strength causal structure causal system chapter Cognitive Science common cause computational condition conditional independence conditional probabilities correlation counterfactuals covariation cues deterministic Development Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Hagmayer human independent infants intervention interventionist intuitive theories Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants people’s Piston predictions prior probabilistic probabilistic graphical models probability distribution psychological question Reichenbach represent representation Schulz Sloman Sobel specific statistical stickball Tenenbaum thrombosis tion trials underlying understanding unobserved cause variables Waldmann Wellman Woodward