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Learning to Learn Causal Models
Author(s) -
Kemp Charles,
Goodman Noah D.,
Tenenbaum Joshua B.
Publication year - 2010
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2010.01128.x
Subject(s) - causal model , schema (genetic algorithms) , causal structure , computer science , artificial intelligence , causal analysis , machine learning , causal inference , set (abstract data type) , cognitive psychology , psychology , mathematics , econometrics , statistics , physics , quantum mechanics , programming language
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.