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. |
From inside the book
Results 1-5 of 46
Page vii
... 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 хор 8 Teaching ...
... 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 хор 8 Teaching ...
Page viii
... Lagnado , Michael R. Waldmann , York Hagmayer , and Steven A. 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 ...
... Lagnado , Michael R. Waldmann , York Hagmayer , and Steven A. 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 ...
Page ix
... Lagnado Department of Psychology University College London Gower Street London WC1E 6BT, UK Andrew N. Meltzoff Institute for Learning and Brain Sciences University of Washington Seattle, WA 98195 Bob Rehder Department of Psychology New ...
... Lagnado Department of Psychology University College London Gower Street London WC1E 6BT, UK Andrew N. Meltzoff Institute for Learning and Brain Sciences University of Washington Seattle, WA 98195 Bob Rehder Department of Psychology New ...
Page 8
... 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 things like ...
... 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 things like ...
Page 28
... (Lagnado & 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 ...
... (Lagnado & 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 ...
Other editions - View all
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 |
Common terms and phrases
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