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 viii
... Covariation: Cues to Causal Structure 154 David A. Lagnado, Michael R. Waldmann, York Hagmayer, and Steven A. Sloman Theory Unification and Graphical Models in Human Categorization 173 David Danks 12 Essentialism as a Generative Theory ...
... Covariation: Cues to Causal Structure 154 David A. Lagnado, Michael R. Waldmann, York Hagmayer, and Steven A. Sloman Theory Unification and Graphical Models in Human Categorization 173 David Danks 12 Essentialism as a Generative Theory ...
Page 5
... covariation among the variables by itself is consistent with both these structures. You can discriminate between these two graphs by looking at the patterns of conditional probability among the three variables. Suppose you keep track of ...
... covariation among the variables by itself is consistent with both these structures. You can discriminate between these two graphs by looking at the patterns of conditional probability among the three variables. Suppose you keep track of ...
Page 9
... covariation of cause and effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms of generative transmission” of force and energy (1982, p. 46). In a series of experiments, Shultz ...
... covariation of cause and effect, and a causal mechanism view of causality, in which causation is understood “primarily in terms of generative transmission” of force and energy (1982, p. 46). In a series of experiments, Shultz ...
Page 10
... covariation information in making causal judgments (Ahn, Kalish, Medin, & Gelman, 1995). Covariation Accounts However, the generative transmission view of causation in particular and domain-specific knowledge in general have played a ...
... covariation information in making causal judgments (Ahn, Kalish, Medin, & Gelman, 1995). Covariation Accounts However, the generative transmission view of causation in particular and domain-specific knowledge in general have played a ...
Page 12
... covariation as an index of causal power (an unobservable entity) and suggests that people reason about causes with respect to particular focal sets, a contextually determined set of events over which people compute contrasts in covariation ...
... covariation as an index of causal power (an unobservable entity) and suggests that people reason about causes with respect to particular focal sets, a contextually determined set of events over which people compute contrasts in covariation ...
Other editions - View all
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