Abstract
A causal theory can be thought of as a grammar that generates events, and that can be used to parse events to identify underlying causal structure. This chapter considers what the components of such a grammar might be - the analogues of syntactic categories and the rules that relate them in a linguistic grammar. It presents two proposals for causal grammars. The first asserts that the variables which describe events can be organized into causal categories, and allows relationships between those categories to be expressed. The second uses a probabilistic variant of first-order logic in order to describe the ontology and causal laws expressed in an intuitive theory. This chapter illustrates how both kinds of grammar can guide causal learning.
Original language | English (US) |
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Title of host publication | Causal Learning |
Subtitle of host publication | Psychology, Philosophy, and Computation |
Publisher | Oxford University Press |
ISBN (Electronic) | 9780199958511 |
ISBN (Print) | 9780195176803 |
DOIs | |
State | Published - Apr 1 2010 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Psychology
Keywords
- Bayesian inference
- Causal learning
- Causal reasoning
- First-order logic
- Generative grammar
- Intuitive theories
- Probabilistic models