Abstract
This chapter presents a framework for understanding the structure, function, and acquisition of causal theories from a rational computational perspective. Using a "reverse engineering" approach, it considers the computational problems that intuitive theories help to solve, focusing on their role in learning and reasoning about causal systems, and then using Bayesian statistics to describe the ideal solutions to these problems. The resulting framework highlights an analogy between causal theories and linguistic grammars: just as grammars generate sentences and guide inferences about their interpretation, causal theories specify a generative process for events, and guide causal inference.
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
- Psychology(all)
Keywords
- Bayesian inference
- Causal learning
- Causal reasoning
- Generative grammar
- Intuitive theories
- Probabilistic models