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Starting from the premise that the purpose of cognitive modeling is to gain information about the cognitive processes of individuals, we develop a general theoretical framework for assessment of models on the basis of tests of the models' ability to yield information about the true performance patterns of individual subjects and the processes underlying them. To address the central problem that observed performance is a composite of true performance and error, we present formal derivations concerning inference from noisy data to true performance. Analyses of model fits to simulated data illustrate the usefulness of our approach for coping with difficult issues of model identifiability and testability.

Predicting true patterns of cognitive performance from noisy data