The Use of MDL to Select among Computational Models of Cognition

Myung, In Jae, Pitt, Mark A., Zhang, Shaobo, Balasubramanian, Vijay

Neural Information Processing Systems 

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.

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