Equivalence: A Novel Basis for Model Analysis
نویسنده
چکیده
As cognitive models are developed that are meant to apply to a broad range of phenomena, it is necessary to evaluate how successfully they do so. This is commonly done by measures such as the Mean Squared Error. We propose and demonstrate an alternate approach based on a measure of statistical equivalence. Instead of using sample means, this method uses confidence intervals, and places an upper bound on how wrong the model may be, given the uncertainties in the data. We apply this to the RELACS model in various different repeated binary choice tasks. We show that the equivalence measure identifies ranges of canonical parameter settings that are equally equivalent. It also identifies experimental conditions that are not yet modelled well.
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