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. |
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Page 19
The interventionist theory described in the next section is a version of a counterfactual theory; the counterfactuals in question describe what would happen to E under interventions (idealized manipulations of) on C. The interventionist ...
The interventionist theory described in the next section is a version of a counterfactual theory; the counterfactuals in question describe what would happen to E under interventions (idealized manipulations of) on C. The interventionist ...
Page 20
Interventionism Interventionist accounts take as their point of departure the idea that causes are potentially a means for manipulating their effects: If it is possible to manipulate a cause in the right way, then there would be an ...
Interventionism Interventionist accounts take as their point of departure the idea that causes are potentially a means for manipulating their effects: If it is possible to manipulate a cause in the right way, then there would be an ...
Page 21
If we manipulate the value of B by manipulating the value of A, then the value of S will change even though, in contradiction to (SC), B does not cause S. Intuitively, an experiment in which B is manipulated in this way is a badly ...
If we manipulate the value of B by manipulating the value of A, then the value of S will change even though, in contradiction to (SC), B does not cause S. Intuitively, an experiment in which B is manipulated in this way is a badly ...
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, ...
Page 24
There is dependence of the sort associated with interventionist counterfactuals between whether lactose is present and the synthesis (or lack of synthesis) of the enzyme that digests it— manipulating ...
There is dependence of the sort associated with interventionist counterfactuals between whether lactose is present and the synthesis (or lack of synthesis) of the enzyme that digests it— manipulating ...
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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 |
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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 |