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 6-10 of 88
Page 13
... causal attribution. Cognition, 54, 299–352. Baillargeon, R., Kotovsky, L., & Needham, A. (1995). The acquisition of physical knowledge in infancy. In D. Sperber & D. Premack (Eds.), Causal cognition: A multidisciplinary debate. Symposia ...
... causal attribution. Cognition, 54, 299–352. Baillargeon, R., Kotovsky, L., & Needham, A. (1995). The acquisition of physical knowledge in infancy. In D. Sperber & D. Premack (Eds.), Causal cognition: A multidisciplinary debate. Symposia ...
Page 14
... Knowledge and Domain Specificity,” held in Ann Arbor, Michigan, October 13–16, 1990 (pp. 257–293). New York: Cambridge University Press. Hickling, A. K., & Wellman, H. M. (2001). The emergence of children's causal explanations and ...
... Knowledge and Domain Specificity,” held in Ann Arbor, Michigan, October 13–16, 1990 (pp. 257–293). New York: Cambridge University Press. Hickling, A. K., & Wellman, H. M. (2001). The emergence of children's causal explanations and ...
Page 15
... Knowledge-based causal induction. In D. R. Shanks, K. Holyoak, & D. L. Medin (Eds.), Causal learning (pp. 47–88). San Diego, CA: Academic Press. Waldmann, M. R. (2000). Competition among causes but not effects in predictive and ...
... Knowledge-based causal induction. In D. R. Shanks, K. Holyoak, & D. L. Medin (Eds.), Causal learning (pp. 47–88). San Diego, CA: Academic Press. Waldmann, M. R. (2000). Competition among causes but not effects in predictive and ...
Page 22
... causal relationships. Obviously, there are many other ways in which humans may learn about causal relationships; these include passive observation of statistical relationships, instruction, and the combination of these with background ...
... causal relationships. Obviously, there are many other ways in which humans may learn about causal relationships; these include passive observation of statistical relationships, instruction, and the combination of these with background ...
Page 31
You have reached your viewing limit for this book.
You have reached your viewing limit for this book.
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 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