Learning to Learn Causal Models

Cognitive Science 34 (7):1185-1243 (2010)

Authors
Charles Kemp
Carnegie Mellon University
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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DOI 10.1111/j.1551-6709.2010.01128.x
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
Attention, Similarity, and the Identification–Categorization Relationship.Robert M. Nosofsky - 1986 - Journal of Experimental Psychology: General 115 (1):39-57.
From Covariation to Causation: A Causal Power Theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.

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Citations of this work BETA

Formal Epistemology and the New Paradigm Psychology of Reasoning.Niki Pfeifer & Igor Douven - 2013 - Review of Philosophy and Psychology 5 (2):199-221.
Domain-Creating Constraints.Robert L. Goldstone & David Landy - 2010 - Cognitive Science 34 (7):1357-1377.

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