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Type: Journal article
Title: Assessing the distinguishability of models and the informativeness of data
Author: Navarro, D.
Pitt, M.
Myung, J.
Citation: Cognitive Psychology, 2004; 49(1):47-84
Publisher: Academic Press
Issue Date: 2004
ISSN: 0010-0285
Statement of
Daniel J. Navarro, Mark A. Pitt, and In Jae Myung
Abstract: A difficulty in the development and testing of psychological models is that they are typically evaluated solely on their ability to fit experimental data, with little consideration given to their ability to fit other possible data patterns. By examining how well model A fits data generated by model B, and vice versa (a technique that we call landscaping), much safer inferences can be made about the meaning of a model’s fit to data. We demonstrate the landscaping technique using four models of retention and 77 historical data sets, and show how the method can be used to: (1) evaluate the distinguishability of models, (2) evaluate the informativeness of data in distinguishing between models, and (3) suggest new ways to distinguish between models. The generality of the method is demonstrated in two other research areas (information integration and categorization), and its relationship to the important notion of model complexity is discussed.
Keywords: Model distinguishability; Retention models; Landscaping; Data informativeness
Rights: Copyright © 2004 Elsevier Inc. All rights reserved.
RMID: RQF0000012
DOI: 10.1016/j.cogpsych.2003.11.001
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Appears in Collections:Psychology publications

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