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  1.  13
    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.
  2.  19
    Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.Matthew M. Walsh, Kevin A. Gluck, Glenn Gunzelmann, Tiffany Jastrzembski & Michael Krusmark - 2018 - Cognitive Science 42 (S3):644-691.
    The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously (...)
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  3.  16
    The Discovery of Processing Stages: Extension of Sternberg’s Method.John R. Anderson, Qiong Zhang, Jelmer P. Borst & Matthew M. Walsh - 2016 - Psychological Review 123 (5):481-509.
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  4.  18
    Mechanisms for Robust Cognition.Matthew M. Walsh & Kevin A. Gluck - 2015 - Cognitive Science 39 (6):1131-1171.
    To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...)
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