Dr. Jay I. Myung Profile

Dr. Jay I. Myung

at The Ohio State Univ

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Proceedings Article | 11 March 2005 Paper

Model selection in cognitive science as an inverse problem

Jay Myung, Mark Pitt, Daniel Navarro

Proceedings Volume 5674, (2005) https://doi.org/10.1117/12.610320

KEYWORDS: Data modeling, Cognitive modeling, Inverse problems, 3D modeling, Statistical modeling, Visualization, Visual process modeling, Stochastic processes, Psychology, Binary data

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How should we decide among competing explanations (models) of a cognitive phenomenon? This problem of model selection is at the heart of the scientific enterprise. Ideally, we would like to identify the model that actually generated the data at hand. However, this is an un-achievable goal as it is fundamentally ill-posed. Information in a finite data sample is seldom sufficient to point to a single model. Multiple models may provide equally good descriptions of the data, a problem that is exacerbated by the presence of random error in the data. In fact, model selection bears a striking similarity to perception, in that both require solving an inverse problem. Just as perceptual ambiguity can be addressed only by introducing external constraints on the interpretation of visual images, the ill-posedness of the model selection problem requires us to introduce external constraints on the choice of the most appropriate model. Model selection methods differ in how these external constraints are conceptualized and formalized. In this review we discuss the development of the various approaches, the differences between them, and why the methods perform as they do. An application example of selection methods in cognitive modeling is also discussed.

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