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 vii
... INTERVENTION 1 Interventionist Theories of Causation in Psychological Perspective 19 Jim Woodward 2 Infants' Causal Learning: Intervention, Observation, Imitation 37 Andrew N. Meltzoff 3 Detecting Causal Structure: The Role of Interventions ...
... INTERVENTION 1 Interventionist Theories of Causation in Psychological Perspective 19 Jim Woodward 2 Infants' Causal Learning: Intervention, Observation, Imitation 37 Andrew N. Meltzoff 3 Detecting Causal Structure: The Role of Interventions ...
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... intervention, and conditional probability through a second assumption, an assumption about how interventions should be represented in the graph. The Intervention Assumption A variable I is an intervention on INTRODUCTION 5.
... intervention, and conditional probability through a second assumption, an assumption about how interventions should be represented in the graph. The Intervention Assumption A variable I is an intervention on INTRODUCTION 5.
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Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. The Intervention Assumption A variable I is an intervention on a variable X in a causal graph if and only if (a) I is exogenous (that is, it is not caused by any other ...
Psychology, Philosophy, and Computation Alison Gopnik, Laura Schulz. The Intervention Assumption A variable I is an intervention on a variable X in a causal graph if and only if (a) I is exogenous (that is, it is not caused by any other ...
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... intervention (Glymour & Cooper, 1999; Spirtes et al., 1993). Computationally tractable learning algorithms have been designed to accomplish this task and have been extensively applied in a range of disciplines (e.g., Ramsey, Roush ...
... intervention (Glymour & Cooper, 1999; Spirtes et al., 1993). Computationally tractable learning algorithms have been designed to accomplish this task and have been extensively applied in a range of disciplines (e.g., Ramsey, Roush ...
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... intervention. Little babies who learn a new skill—like reaching for objects—understand other people's actions on objects better than babies who don't have the skill. Jessica Sommerville will show you next week how giving babies “sticky ...
... intervention. Little babies who learn a new skill—like reaching for objects—understand other people's actions on objects better than babies who don't have the skill. Jessica Sommerville will show you next week how giving babies “sticky ...
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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