Forcing Strategy Uniformity with Utility Manipulation: A Computational Model

Abstract

This paper provides a detailed account of the development of a cognitive model, based on utility learning, of a repetitive computer-based task. The reasoning behind each modeling decision is discussed, which lead to novel ways of dealing with modeling constraints. The resulting model provides evidence for utility learning as an explanation of observed behavior after subjects have experience with the task; however, a key failure of the model implies that utility learning cannot explain subjects' behavior when they first approach the task. The model is the connection between hypotheses and empirical observations, making the modeling process itself critical to the interpretation of such conflicting results.


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