Statistical Inference for a Computational Model of Cognition
Randy Bruno, James McClelland and Larry Wasserman
We consider a neural network model
of human performance in a cognitive task (Eriksen's
We develop methods for performing formal statistical
inference for the model and then
we consider the net as applied
to a particular experiment
involving subjects before and after
The hypothesis that the net captures
the main features of relevant cognitive
behavior can be cast as a specific
hypothesis about the parameterization of the net.
After dealing with some difficult computational problems,
we compute the Bayes factor in favor of the hypothesis
One major complication is that the likelihood
cannot be computed in closed form and so must be
simulated. This adds a component of noise
when we use a Markov chain Monte Carlo simulation.
We devise a method for correcting for this noise.
Keywords: Bayes Factors; Markov Chain Monte Carlo; Neural Networks.
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