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 60
Page 4
... correlation disappears when z is partialed out , we mean that x and y are independent in probability conditional on z . The structure of the causal graph puts constraints on these patterns of probability among the variables . These ...
... correlation disappears when z is partialed out , we mean that x and y are independent in probability conditional on z . The structure of the causal graph puts constraints on these patterns of probability among the variables . These ...
Page 15
... correlation in biology . Oxford , England : Oxford University Press . Silva , R. , Scheines , R. , Glymour , C. , & Spirtes , P. ( 2003 ) . Learning measurement models for unobserved variables . Proceedings of the 18th Conference on ...
... correlation in biology . Oxford , England : Oxford University Press . Silva , R. , Scheines , R. , Glymour , C. , & Spirtes , P. ( 2003 ) . Learning measurement models for unobserved variables . Proceedings of the 18th Conference on ...
Page 23
... correlations that will be observed in the absence of these interventions. Although an interventionist account does not attempt to reduce causal claims to information about conditional probabilities, it readily agrees that such ...
... correlations that will be observed in the absence of these interventions. Although an interventionist account does not attempt to reduce causal claims to information about conditional probabilities, it readily agrees that such ...
Page 26
... correlation between two variables X and Y, it would not matter how the correlation arises—whether because (a) X causes Y or because (b) X and Y have a common cause—as long as correlation is stable and projectable. The difference between ...
... correlation between two variables X and Y, it would not matter how the correlation arises—whether because (a) X causes Y or because (b) X and Y have a common cause—as long as correlation is stable and projectable. The difference between ...
Page 29
... correlation between voluntariness and satisfaction of the conditions for an intervention is imperfect. In a badly designed clinical trial, an experimenter might be subconsciously influenced, in decisions to give a drug to some patients ...
... correlation between voluntariness and satisfaction of the conditions for an intervention is imperfect. In a badly designed clinical trial, an experimenter might be subconsciously influenced, in decisions to give a drug to some patients ...
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