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 1
My advisor has gone completely crazy over this causal Bayes nets stuff and is
insisting that I go to this conference (on the pittance that supports graduate
researchers) and that I learn everything there is to know about the philosophy
and ...
My advisor has gone completely crazy over this causal Bayes nets stuff and is
insisting that I go to this conference (on the pittance that supports graduate
researchers) and that I learn everything there is to know about the philosophy
and ...
Page 2
And, there is no real learning involved in development but only triggering or
enrichment. ... I mean, I'll tell you all about causal learning in psychology if you'll
explain those directed acyclic graphs in plain English words? So, how about it?
And, there is no real learning involved in development but only triggering or
enrichment. ... I mean, I'll tell you all about causal learning in psychology if you'll
explain those directed acyclic graphs in plain English words? So, how about it?
Page 11
Thus, the stronger the prior association is, the less learning there will be on any
given trial. The model can be applied to human causal learning by substituting
causes for the conditioned stimulus and effects for the unconditioned stimulus.
Thus, the stronger the prior association is, the less learning there will be on any
given trial. The model can be applied to human causal learning by substituting
causes for the conditioned stimulus and effects for the unconditioned stimulus.
Page 12
The Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an
account of human causal learning that resolves some of the difficulties with the
R-W account. Cheng proposes that people innately treat covariation as an index
of ...
The Power Theory of Probabilistic Contrast Patricia Cheng (1997) proposes an
account of human causal learning that resolves some of the difficulties with the
R-W account. Cheng proposes that people innately treat covariation as an index
of ...
Page 36
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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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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 |