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 87
Page 3
... evidence. But, philosophers of science found it difficult to explain how these inferences are possible. Although classical logic could provide a formal account of deductive inferences, it was much more difficult to provide an inductive ...
... evidence. But, philosophers of science found it difficult to explain how these inferences are possible. Although classical logic could provide a formal account of deductive inferences, it was much more difficult to provide an inductive ...
Page 4
... evidence itself could generate a hypothesis . Causal Bayes nets provide a kind of logic of induc- tive inference and discovery . They do so , at least , for one type of inference that is particularly important in scientific theory ...
... evidence itself could generate a hypothesis . Causal Bayes nets provide a kind of logic of induc- tive inference and discovery . They do so , at least , for one type of inference that is particularly important in scientific theory ...
Page 6
... evidence about observed probabilities to inferences about interventions and vice versa. These two assumptions, then, allow us to take a par- ticular causal structure and accurately predict the con- ditional probabilities of events, and ...
... evidence about observed probabilities to inferences about interventions and vice versa. These two assumptions, then, allow us to take a par- ticular causal structure and accurately predict the con- ditional probabilities of events, and ...
Page 7
... evidence will follow from par- ticular causal structures , given the Markov , interven- tion , and faithfulness ... evidence and uses those assumptions to learn the structure from the evidence ( your causal Markov , intervention ...
... evidence will follow from par- ticular causal structures , given the Markov , interven- tion , and faithfulness ... evidence and uses those assumptions to learn the structure from the evidence ( your causal Markov , intervention ...
Page 8
... evidence. sprogs can Plus, even the very smallest combine information from observation and intervention. Little babies who learn a new skill—like reaching for objects—understand other people's actions on objects better than babies who ...
... evidence. sprogs can Plus, even the very smallest combine information from observation and intervention. Little babies who learn a new skill—like reaching for objects—understand other people's actions on objects better than babies who ...
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