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  1.  7
    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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  2.  9
    Optimal Experimental Design for Model Discrimination.Jay I. Myung & Mark A. Pitt - 2009 - Psychological Review 116 (3):499-518.
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  3.  19
    Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example.Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro & James Pooley - 2008 - Cognitive Science 32 (8):1285-1303.
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    Mechanisms Underlying the Spacing Effect in Learning: A Comparison of Three Computational Models.Matthew M. Walsh, Kevin A. Gluck, Glenn Gunzelmann, Tiffany Jastrzembski, Michael Krusmark, Jay I. Myung, Mark A. Pitt & Ran Zhou - 2018 - Journal of Experimental Psychology: General 147 (9):1325-1348.
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  5.  5
    How Do PDP Models Learn Quasiregularity?Woojae Kim, Mark A. Pitt & Jay I. Myung - 2013 - Psychological Review 120 (4):903-916.
  6.  12
    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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