Despite the popularity of connectionist models in cognitive science, their performance can often be difficult to evaluate. Inspired by the geometric approach to statistical model selection, we introduce a conceptually similar method to examine the global behavior of a connectionist model, by counting the number and types of response patterns it can simulate. The Markov Chain Monte Carlo-based algorithm that we constructed finds these patterns efficiently. We demonstrate the approach using two localist network models of speech perception.
['Woojae Kim', 'Daniel J. Navarro', 'Mark A. Pitt', 'In Jae Myung']
An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science