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Title page for ETD etd-03272006-134105


Type of Document Dissertation
Author Luhmann, Christian Conrad
Author's Email Address christian.luhmann@vanderbilt.edu
URN etd-03272006-134105
Title BUCKLE: A Model of Causal Learning
Degree PhD
Department Psychology
Advisory Committee
Advisor Name Title
Thomas Palmeri Committee Chair
David Noelle Committee Member
Gordon Logan Committee Member
Woo-kyoung Ahn Committee Member
Keywords
  • unobserved causes
  • determinism
  • computational modeling
  • Bayesian inference
  • Causation -- Mathematical models
  • Learning
  • Psychology of -- Mathematical models
Date of Defense 2006-03-13
Availability unrestricted
Abstract
Dealing with alternative causes is necessary to avoid making inaccurate causal inferences from covariation data. However, information about alternative causes is frequently unavailable, rendering them unobserved. Some theories of causal learning make simplifying assumptions to ease the difficulty associated with unobserved alternative causes. Here I present a new model of causal learning, BUCKLE (Bidirectional Unobserved Cause LEarning), which extends existing models of causal learning by dynamically inferring information about unobserved, alternative causes. During the course of causal learning, BUCKLE continually computes the likelihood that an unobserved cause is present during a given observation and then uses the results of these inferences to learn the causal strengths of the unobserved as well as observed causes. I will also present empirical evidence demonstrating that BUCKLE provides a better explanation of people’s causal learning than existing models.
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