The Use of MDL to Select among Computational Models of Cognition

How should we decide among competing explanations of a cognitive process given limited observations? The problem of model selection is at the heart of progress in cognitive science. In this paper, Minimum Description Length (MDL) is introduced as a method for selecting among computational models of cognition. We also show that differential geometry provides an intuitive understanding of what drives model selection in MDL. Finally, adequacy of MDL is demonstrated in two areas of cognitive modeling. 1 Model Selection and Model Complexity The development and testing of computational models of cognitive processing are a central focus in cognitive science. A model embodies a solution to a problem whose adequacy is evaluated by its ability to mimic behavior by capturing the regularities underlying observed data. This enterprise of model selection is challenging because of the competing goals that must be satisfied. Traditionally, computational models of cognition have been compared using on...

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: In Jae Myung, Mark A. Pitt, Shaobo Zhang, Vijay Balasubramanian
• Keywords: Computational Models, Model, Model Selection, NIPS 2000, NIPS 2007