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 26
... people ought to make in various situations, how they ought to use evidence in reaching such judgments, and so on. I ... people's causal judgments connect with or fail to connect with various other concepts and patterns of reasoning ...
... people ought to make in various situations, how they ought to use evidence in reaching such judgments, and so on. I ... people's causal judgments connect with or fail to connect with various other concepts and patterns of reasoning ...
Page 27
... people actually think and reason. In fact, there is considerable evidence that people employ counterfactuals extensively in various forms of ordinary reasoning, and that they connect causal claims and counterfactuals in something like ...
... people actually think and reason. In fact, there is considerable evidence that people employ counterfactuals extensively in various forms of ordinary reasoning, and that they connect causal claims and counterfactuals in something like ...
Page 28
... people consciously or explicitly represent to themselves the full technical definition of a normatively appropriate notion of intervention when they engage in causal reasoning? 2. Do people learn and reason in accord with the normative ...
... people consciously or explicitly represent to themselves the full technical definition of a normatively appropriate notion of intervention when they engage in causal reasoning? 2. Do people learn and reason in accord with the normative ...
Page 29
... people either do not distinguish at all between backtracking and nonbacktracking counterfactuals or do not preferentially employ the latter in contexts involving causal reasoning. In addition, the experiments provide additional evidence ...
... people either do not distinguish at all between backtracking and nonbacktracking counterfactuals or do not preferentially employ the latter in contexts involving causal reasoning. In addition, the experiments provide additional evidence ...
Page 42
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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