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
... Sloman , David Lagnado , and Michael R. Waldmann 7 On the Importance of Causal Taxonomy 101 Christopher Hitchcock PART II : CAUSATION AND PROBABILITY Introduction to Part II : Causation and Probability 117 Alison Gopnik and Laura Schulz ...
... Sloman , David Lagnado , and Michael R. Waldmann 7 On the Importance of Causal Taxonomy 101 Christopher Hitchcock PART II : CAUSATION AND PROBABILITY Introduction to Part II : Causation and Probability 117 Alison Gopnik and Laura Schulz ...
Page viii
... Sloman 11 Theory Unification and Graphical Models in Human Categorization 173 David Danks 12 Essentialism as a Generative Theory of Classification 190 Bob Rehder 13 Data - Mining Probabilists or Experimental Determinists ? A Dialogue on ...
... Sloman 11 Theory Unification and Graphical Models in Human Categorization 173 David Danks 12 Essentialism as a Generative Theory of Classification 190 Bob Rehder 13 Data - Mining Probabilists or Experimental Determinists ? A Dialogue on ...
Page 8
... Sloman, Dave Lagnado, and Michael Waldmann will show you that even those stats class undergraduates can make remarkably sophisticated inferences about both predictions and interventions. Best of all, sprogs never do absolutely useless ...
... Sloman, Dave Lagnado, and Michael Waldmann will show you that even those stats class undergraduates can make remarkably sophisticated inferences about both predictions and interventions. Best of all, sprogs never do absolutely useless ...
Page 28
... Sloman, 2004; Sobel & Kushnir, 2006).10 This true for infants as well; Jessica Sommerville (chapter 3, this volume) reports a series of experiments that show that infants who actively intervene, for example, to obtain a toy by pulling a ...
... Sloman, 2004; Sobel & Kushnir, 2006).10 This true for infants as well; Jessica Sommerville (chapter 3, this volume) reports a series of experiments that show that infants who actively intervene, for example, to obtain a toy by pulling a ...
Page 36
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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 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 Developmental Psychology domain effect evidence example experiments explanations Figure framework Fuel Intake Glymour Gopnik graph schema graphical models Griffiths Hagmayer human independent infants intervention interventionist intuitive theories Journal of Experimental Lagnado Laplace learners manipulated Markov Markov random field mechanism Meltzoff object observed outcome participants 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