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Inferring Hidden Causes Tamar Kushnir (tkushnir@socrates.berkeley.edu), Alison Gopnik (gopnik@socrates.berkeley.edu), Laura
 

Summary: 1
Inferring Hidden Causes
Tamar Kushnir (tkushnir@socrates.berkeley.edu), Alison Gopnik (gopnik@socrates.berkeley.edu), Laura
Schulz (laurasch@socrates.berkeley.edu)
Department of Psychology, Tolman Hall, University of California
Berkeley, CA 94720 USA
David Danks (ddanks@ai.uwf.edu)
Institute for Human & Machine Cognition, University of West Florida
Pensacola, FL 32501 USA
Abstract
One of the important aspects of human causal reasoning is
that from the time we are young children we reason about
unobserved causes. How can we learn about unobserved
causes from information about observed events? Causal
Bayes nets provide a formal account of how causal structure
is learned from a combination of associations and
interventions. This formalism makes specific predictions
about the conditions under which learners postulate hidden
causes. In this study adult learners were shown a pattern of
associations and interventions on a novel causal system. We

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics