Learning to Learn Causal Models
Article first published online: 23 AUG 2010
Copyright © 2010 Cognitive Science Society, Inc.
Special Issue: Mechanisms of Cognitive Development: Domain-General Learning or Domain-Specific Constraints?
Volume 34, Issue 7, pages 1185–1243, September 2010
How to Cite
Kemp, C., Goodman, N. D. and Tenenbaum, J. B. (2010), Learning to Learn Causal Models. Cognitive Science, 34: 1185–1243. doi: 10.1111/j.1551-6709.2010.01128.x
- Issue published online: 2 SEP 2010
- Article first published online: 23 AUG 2010
- Received 6 November 2008; received in revised form 11 June 2010; accepted 14 June 2010
- Causal learning;
- Learning to learn;
- Learning inductive constraints;
- Transfer learning;
- Hierarchical Bayesian models
Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning.