Causal Learning: Psychology, Philosophy, and ComputationUnderstanding 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 91
Page 1
But, every time I look at one of the papers, all I see are unintelligible sentences like this: For any variable R in the directed graph, the graph represents the proposition that for any set S of variables in the graph (not containing ...
But, every time I look at one of the papers, all I see are unintelligible sentences like this: For any variable R in the directed graph, the graph represents the proposition that for any set S of variables in the graph (not containing ...
Page 4
The graphs consist of variables, representing 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 ...
The graphs consist of variables, representing 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 ...
Page 5
The 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.
The 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.
Page 6
The Intervention Assumption A variable I is an intervention on a variable X in a causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph), (b) directly fixes the value of X to x, ...
The Intervention Assumption A variable I is an intervention on a variable X in a causal graph if and only if (a) I is exogenous (that is, it is not caused by any other variables in the graph), (b) directly fixes the value of X to x, ...
Page 7
The Faithfulness Assumption In the joint distribution on the variables in the graph, all conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness assumption, it is possible to ...
The Faithfulness Assumption In the joint distribution on the variables in the graph, all conditional independencies are consequences of the Markov assumption applied to the graph. Given the faithfulness assumption, it is possible to ...
What people are saying - Write a review
We haven't found any reviews in the usual places.
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 |