1.  44
    A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods.Richard M. Shiffrin, Michael D. Lee, Woojae Kim & Eric-Jan Wagenmakers - 2008 - Cognitive Science 32 (8):1248-1284.
    This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a (...)
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  2.  18
    Global model analysis by parameter space partitioning.Mark A. Pitt, Woojae Kim, Daniel J. Navarro & Jay I. Myung - 2006 - Psychological Review 113 (1):57-83.
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    How do PDP models learn quasiregularity?Woojae Kim, Mark A. Pitt & Jay I. Myung - 2013 - Psychological Review 120 (4):903-916.
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    Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach.Woojae Kim, Mark A. Pitt, Zhong-Lin Lu & Jay I. Myung - 2017 - Cognitive Science:2234-2252.
    Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one-step-ahead strategies in which the stimulus on each trial is (...)
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