The Use of MDL to Select Among Computational Models of Cognition
Abstract
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.
Cite
Text
Myung et al. "The Use of MDL to Select Among Computational Models of Cognition." Neural Information Processing Systems, 2000.Markdown
[Myung et al. "The Use of MDL to Select Among Computational Models of Cognition." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/myung2000neurips-use/)BibTeX
@inproceedings{myung2000neurips-use,
title = {{The Use of MDL to Select Among Computational Models of Cognition}},
author = {Myung, In Jae and Pitt, Mark A. and Zhang, Shaobo and Balasubramanian, Vijay},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {38-44},
url = {https://mlanthology.org/neurips/2000/myung2000neurips-use/}
}