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 94
Page 3
method of examples and counterexamples; philosophers give examples of cases in which everyone agrees that X causes Y and then try to find some generalization that will capture those examples. Then, other philosophers find examples that ...
method of examples and counterexamples; philosophers give examples of cases in which everyone agrees that X causes Y and then try to find some generalization that will capture those examples. Then, other philosophers find examples that ...
Page 5
Many real-life inferences involve complex combinations of conditional probabilities among variables—consider a medical expert, for example, trying to predict one set of symptoms from another set. Trying to predict all the combinations ...
Many real-life inferences involve complex combinations of conditional probabilities among variables—consider a medical expert, for example, trying to predict one set of symptoms from another set. Trying to predict all the combinations ...
Page 6
Suppose, for example, I want to know what I can do to prevent my insomnia. Should I sit in my room alone but continue to drink when I want to or go to parties just the same but stick to Perrier? I can calculate the effects of such ...
Suppose, for example, I want to know what I can do to prevent my insomnia. Should I sit in my room alone but continue to drink when I want to or go to parties just the same but stick to Perrier? I can calculate the effects of such ...
Page 21
In other words, we want to rule out the possibility that the intervention on X (or anything that causes the intervention) affects Y via a causal route that does not go through X, as happens, for example, when B in the example above is ...
In other words, we want to rule out the possibility that the intervention on X (or anything that causes the intervention) affects Y via a causal route that does not go through X, as happens, for example, when B in the example above is ...
Page 22
right causal characteristics, as in the example in which the common cause A of B and S is manipulated. Nonetheless, I think that it is plausible (see the Interventions and Voluntary Actions section) that, as a matter of contingent, ...
right causal characteristics, as in the example in which the common cause A of B and S is manipulated. Nonetheless, I think that it is plausible (see the Interventions and Voluntary Actions section) that, as a matter of contingent, ...
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 |