|
|
|
| RI | Thesis Oral | 20 Jan 2005 | |
Robotics Institute Thesis Oral 20 Jan 2005
Place and Time |
Abstract |
Further Details |
Thesis Committee
A Latent Cause Model of Classical Conditioning
Aaron Courville
Robotics Institute
Carnegie Mellon University
| Place and Time |
WEH 8220
12:30 PM
| Abstract |
Classical conditioning experiments probe what animals learn about their
environment. This thesis presents an exploration of the probabilistic,
generative latent cause theory of classical conditioning. According to the
latent cause theory, animals assume that events within their environment
are attributable to a latent cause. Learning is interpreted as an attempt
to recover the generative model that gave rise to these observed events. In
this thesis, I apply the latent cause theory to three distinct areas of
classical conditioning, in each case offering a novel account of empirical
phenomena.
In the first instance, I develop a version of a latent cause model that
explicitly encodes a latent timeline to which observed stimuli and
reinforcements are associated, thus preserving their temporal order. In
this context, the latent cause model is equivalent to a hidden Markov
model. This model is able to account for a theoretically challenging set of
experiments which collectively suggest that animals encode the temporal
relationships among stimuli and use this representation to predict
impending reinforcement.
Next, I explore the effects of inference over an uncertain latent cause
model structure. A key property of Bayesian structural inference is the
tradeoff between the model complexity and data fidelity. Recognizing the
equivalence between this tradeoff and the tradeoff between generalization
and discrimination found in configural conditioning suggests a
statistically sound account of these phenomena. By considering model
simulations of a number of conditioning paradigms (including some not
previously viewed as "configural'', I reveal behavioral signs that animals
employ model complexity tradeoffs.
Finally I explore the consequence of merging latent variable theory with a
generative model of change. A model of change describes how the parameters
and structure of the latent cause model evolve over time. The resulting
non-stationary latent cause model offers a novel perspective on the factors
that influence animal judgments about changes in their environment. In
particular, the model correctly predicts that the introduction of an
unexpected stimulus can spur fast learning and eliminate latent inhibition.
This thesis offers a unified theoretical framework for classical
conditioning. It uses state of the art machine reasoning concepts,
including reversible jump MCMC and particle filtering search techniques, to
explore a novel theoretical account of a wide range of empirical phenomena,
many of which have otherwise resisted a computational explanation.
| Further Details |
A copy of the thesis oral document can be found at http://www.cs.mcgill.ca/~jpineau/thesis.pdf.
| Thesis Committee |
This page maintained by robotwebmaster@ri.cmu.edu.