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 20
an interventionist interpretation (Gopnik & Shulz, 2004; Woodward, 2003). It is
usual in the philosophical literature to contrast so-called type causal claims that
relate one type of event or factor to another (“Aspirin causes headache relief”)
with ...
an interventionist interpretation (Gopnik & Shulz, 2004; Woodward, 2003). It is
usual in the philosophical literature to contrast so-called type causal claims that
relate one type of event or factor to another (“Aspirin causes headache relief”)
with ...
Page 24
I suggested above that an interventionist account will lead to different causal
judgments about particular cases than causal process accounts. Consider cases
of double prevention, in which A prevents the occurrence of B, which had it
occurred ...
I suggested above that an interventionist account will lead to different causal
judgments about particular cases than causal process accounts. Consider cases
of double prevention, in which A prevents the occurrence of B, which had it
occurred ...
Page 25
However, rather than trying to explicate the notion of a causal mechanism in
terms of notions like force, energy, or generative transmission, interventionists
will instead appeal to interventionist counterfactuals. Simplifying greatly,
information ...
However, rather than trying to explicate the notion of a causal mechanism in
terms of notions like force, energy, or generative transmission, interventionists
will instead appeal to interventionist counterfactuals. Simplifying greatly,
information ...
Page 27
Causal Judgment and Interventionist Counterfactuals I noted that interventionist
theories are just one species of the more general category of difference-making
theories. The sensitivity of causal judgment to contingency information is ...
Causal Judgment and Interventionist Counterfactuals I noted that interventionist
theories are just one species of the more general category of difference-making
theories. The sensitivity of causal judgment to contingency information is ...
Page 58
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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 |
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 |