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 84
Page viii
... Represent Causal Relations ? 245 Michael Strevens 16 Causal Reasoning as Informed by the Early Development of Explanations 261 Henry M. Wellman and David Liu 17 Dynamic Interpretations of Covariation Data 280 Woo - kyoung Ahn , Jessecae ...
... Represent Causal Relations ? 245 Michael Strevens 16 Causal Reasoning as Informed by the Early Development of Explanations 261 Henry M. Wellman and David Liu 17 Dynamic Interpretations of Covariation Data 280 Woo - kyoung Ahn , Jessecae ...
Page 4
... represent ing types of events or states of the world and directed edges ( arrows ) representing the direct causal relations between those variables . The variables can be discrete ( like school grade ) or continuous ( like weight ) ...
... represent ing types of events or states of the world and directed edges ( arrows ) representing the direct causal relations between those variables . The variables can be discrete ( like school grade ) or continuous ( like weight ) ...
Page 5
... represent by two simple causal graphs : Graph 1 is a chain P → W → I ; Graph 2 is a common cause structure I ← P → W. Maybe parties lead me to drink , and wine keeps me up ; maybe parties both keep me up and lead me to drink . The ...
... represent by two simple causal graphs : Graph 1 is a chain P → W → I ; Graph 2 is a common cause structure I ← P → W. Maybe parties lead me to drink , and wine keeps me up ; maybe parties both keep me up and lead me to drink . The ...
Page 6
... represented by replacing the original graph with an altered graph in which arrows directed into the inter- vened on variable are eliminated (Judea Pearl in 2000 vividly referred to this process as graph surgery). The conditional ...
... represented by replacing the original graph with an altered graph in which arrows directed into the inter- vened on variable are eliminated (Judea Pearl in 2000 vividly referred to this process as graph surgery). The conditional ...
Page 25
... represent or to be sensitive to such information.6 Matters are further complicated, though, by the fact that insofar as philosophical accounts of causation have psychological implications, they are often A useful point of departure is ...
... represent or to be sensitive to such information.6 Matters are further complicated, though, by the fact that insofar as philosophical accounts of causation have psychological implications, they are often A useful point of departure is ...
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