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 12
This explains both why ceiling effects are a boundary condition on causal inference and why covariation is not, in general, equivalent to causation. A parallel account explains inferences about candidate inhibitory causes.
This explains both why ceiling effects are a boundary condition on causal inference and why covariation is not, in general, equivalent to causation. A parallel account explains inferences about candidate inhibitory causes.
Page 21
... manipulated in this way is a badly designed experiment for the purposes of determining whether B causes S. We need to formulate conditions that restrict the allowable ways of changing B so as to rule out possibilities of this sort.
... manipulated in this way is a badly designed experiment for the purposes of determining whether B causes S. We need to formulate conditions that restrict the allowable ways of changing B so as to rule out possibilities of this sort.
Page 22
Both conditions should be understood in a way that accommodates these points: What matters to whether the relationship between X and Y is causal is not whether an intervention is actually performed on X but rather what would happen to Y ...
Both conditions should be understood in a way that accommodates these points: What matters to whether the relationship between X and Y is causal is not whether an intervention is actually performed on X but rather what would happen to Y ...
Page 23
we may think of CM as a condition that connects claims about what happens under interventions to claims about conditional probabilities involving observed outcomes, thus allowing us to move back and forth between the two kinds of claims ...
we may think of CM as a condition that connects claims about what happens under interventions to claims about conditional probabilities involving observed outcomes, thus allowing us to move back and forth between the two kinds of claims ...
Page 24
For example, if the relationship between C and E satisfies the conditions in TC, people will judge that C causes E even if there appears to be a spatiotemporal gap between C and E. Moreover, even if there is a connecting ...
For example, if the relationship between C and E satisfies the conditions in TC, people will judge that C causes E even if there appears to be a spatiotemporal gap between C and E. Moreover, even if there is a connecting ...
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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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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 |