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 37
Page 3
... conditional probabilities and interventions, and perhaps most significantly discriminate between conditional probabilities and interventions and counterfactuals. So, I decided to move to Carnegie Tech for graduate school and work on ...
... conditional probabilities and interventions, and perhaps most significantly discriminate between conditional probabilities and interventions and counterfactuals. So, I decided to move to Carnegie Tech for graduate school and work on ...
Page 4
... conditional probability among events (as in statistical analysis), by examining the consequences of interventions ... Probabilities The Bayes net formalism makes systematic connections between the causal hypotheses that are represented ...
... conditional probability among events (as in statistical analysis), by examining the consequences of interventions ... Probabilities The Bayes net formalism makes systematic connections between the causal hypotheses that are represented ...
Page 5
... conditional probability among the three variables. Suppose you keep track of all the times you drink and party and ... probabilities and discovered that representing the variables in a directed graph allowed them to do this. The graph ...
... conditional probability among the three variables. Suppose you keep track of all the times you drink and party and ... probabilities and discovered that representing the variables in a directed graph allowed them to do this. The graph ...
Page 6
... probabilities to inferences about interventions and vice versa. These two assumptions, then, allow us to take a particular causal structure and accurately predict the conditional probabilities of events, and the consequences of ...
... probabilities to inferences about interventions and vice versa. These two assumptions, then, allow us to take a particular causal structure and accurately predict the conditional probabilities of events, and the consequences of ...
Page 7
... conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of conditional probability and intervention ...
... conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness assumption, it is possible to infer complex causal structure from patterns of conditional probability and intervention ...
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
Bibliographic information
| Title | Causal Learning: Psychology, Philosophy, and Computation Oxford Series in Cognitive Development |
| Editors | Alison Gopnik, Laura Schulz |
| Publisher | Oxford University Press, 2007 |
| ISBN | 019803928X, 9780198039280 |
| Length | 384 pages |
| Subjects | › Psychology / Cognitive Psychology & Cognition |
| Export Citation | BiBTeX EndNote RefMan |