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True to Thyself: Assessing Whether Computational Models of Cognition Remain Faithful to Their Theoretical Principles
Abstract
This study investigated the model selection problem in cognitive psychology: How should one decide between two computational models of cognition? The focus was on model "faithfulness, " which refers to the degree to which a model's behavior originates from the theoretical principles that it embodies. The guiding principle is that among a set of models that simulate human performance equally well, the model whose behavior is most stable or robust with variation in parameter values should be favored. This is because such a model is likely to have captured the underlying mental process in the least complex way while at the same time being faithful to the theoretical principles that guided the model's development. Sensitivity analysis is introduced as a tool for assessing model faithfulness. Its application is demonstrated in the context of two localist connectionist models of speech perception, TRACE and MERGE.
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