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 11
The model can be applied to human causal learning by substituting causes for the conditioned stimulus and effects for the unconditioned stimulus. The associative strength between the two variables is then taken as indicating the causal ...
The model can be applied to human causal learning by substituting causes for the conditioned stimulus and effects for the unconditioned stimulus. The associative strength between the two variables is then taken as indicating the causal ...
Page 12
Similarly, the R-W equation assumes that all the variables have already been categorized as causes or effects and then calculates the associative strength between each cause and each effect. However, the model cannot determine whether ...
Similarly, the R-W equation assumes that all the variables have already been categorized as causes or effects and then calculates the associative strength between each cause and each effect. However, the model cannot determine whether ...
Page 13
Much like your Shultz they argue that causation involves the spatiotemporal transmission of some sort of “mark” or “impetus” from cause ... My learning algorithms, like your sprogs, can infer causal structure rather than just strength; ...
Much like your Shultz they argue that causation involves the spatiotemporal transmission of some sort of “mark” or “impetus” from cause ... My learning algorithms, like your sprogs, can infer causal structure rather than just strength; ...
Page 19
Jim Woodward Introduction Broadly speaking, recent philosophical accounts of causation may be grouped into two main ... of causal learning and with the use of contingency information (conditional p) as a measure of causal strength ...
Jim Woodward Introduction Broadly speaking, recent philosophical accounts of causation may be grouped into two main ... of causal learning and with the use of contingency information (conditional p) as a measure of causal strength ...
Page 24
... as causal, how they assess causal efficacy or strength in such cases, and the ease with which such relationships can be learned. Double prevention cases suggest that energy transmission is not necessary for causal relatedness.
... as causal, how they assess causal efficacy or strength in such cases, and the ease with which such relationships can be learned. Double prevention cases suggest that energy transmission is not necessary for causal relatedness.
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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 |