Learning to Learn Causal Models

Cognitive Science 34 (7):1185-1243 (2010)
Abstract
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
Keywords Causal learning  Transfer learning  Hierarchical Bayesian models  Learning inductive constraints  Categorization  Learning to learn
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References found in this work BETA
Alison Gopnik & Clark Glymour (2002). Causal Maps and Bayes Nets: A Cognitive and Computational Account of Theory-Formation. In Peter Carruthers, Stephen P. Stich & Michael Siegal (eds.), The Cognitive Basis of Science. Cambridge University Press. 117--132.

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