6 found
  1.  54
    Sleep Deprivation and Sustained Attention Performance: Integrating Mathematical and Cognitive Modeling.Glenn Gunzelmann, Joshua B. Gross, Kevin A. Gluck & David F. Dinges - 2009 - Cognitive Science 33 (5):880-910.
    A long history of research has revealed many neurophysiological changes and concomitant behavioral impacts of sleep deprivation, sleep restriction, and circadian rhythms. Little research, however, has been conducted in the area of computational cognitive modeling to understand the information processing mechanisms through which neurobehavioral factors operate to produce degradations in human performance. Our approach to understanding this relationship is to link predictions of overall cognitive functioning, or alertness, from existing biomathematical models to information processing parameters in a cognitive architecture, leveraging (...)
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  2.  31
    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.  28
    Model flexibility analysis.Vladislav D. Veksler, Christopher W. Myers & Kevin A. Gluck - 2015 - Psychological Review 122 (4):755-769.
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  4.  27
    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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  5.  53
    SA w_ S _u: An Integrated Model of Associative and Reinforcement Learning.Vladislav D. Veksler, Christopher W. Myers & Kevin A. Gluck - 2014 - Cognitive Science 38 (3):580-598.
    Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The (...)
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  6.  87
    Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents.Michael G. Collins, Ion Juvina & Kevin A. Gluck - 2016 - Frontiers in Psychology 7.
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