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Media type:
E-Article
Title:
Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science
Contributor:
Cavagnaro, Daniel R.;
Myung, Jay I.;
Pitt, Mark A.;
Kujala, Janne V.
Published:
MIT Press, 2010
Published in:
Neural Computation, 22 (2010) 4, Seite 887-905
Language:
English
DOI:
10.1162/neco.2009.02-09-959
ISSN:
0899-7667;
1530-888X
Origination:
Footnote:
Description:
Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying model in the fewest possible steps. When the models under consideration are nonlinear, as is often the case in cognitive science, this problem can be impossible to solve analytically without simplifying assumptions. However, as we show in this letter, a full solution can be found numerically with the help of a Bayesian computational trick derived from the statistics literature, which recasts the problem as a probability density simulation in which the optimal design is the mode of the density. We use a utility function based on mutual information and give three intuitive interpretations of the utility function in terms of Bayesian posterior estimates. As a proof of concept, we offer a simple example application to an experiment on memory retention.