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 the strengths from each to develop a more comprehensive explanation. The integration of these methodologies is used to account for changes in human performance on a sustained attention task across 88 h of total sleep deprivation. The integrated model captures changes due to time awake and circadian rhythms, and it also provides an account for underlying changes in the cognitive processes that give rise to those effects. The results show the potential for developing mechanistic accounts of how fatigue impacts cognition, and they illustrate the increased explanatory power that is possible by combining theoretical insights from multiple methodologies.

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 evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation (PPE). Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought-Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides.

A good fit of model predictions to empirical data are often used as an argument for model validity. However, if the model is flexible enough to fit a large proportion of potential empirical outcomes, finding a good fit becomes less meaningful. We propose a method for estimating the proportion of potential empirical outcomes that the model can fit: Model Flexibility Analysis (MFA). MFA aids model evaluation by providing a metric for gauging the persuasiveness of a given fit. We demonstrate that MFA can be more informative than merely discounting the fit by the number of free parameters in the model, and show how the number of free parameters does not necessarily correlate with the flexibility of the model. Additionally, we contrast MFA with other flexibility assessment techniques, including Parameter Space Partitioning, Model Mimicry, Minimum Description Length, and Prior Predictive Evaluation. Finally, we provide examples of how MFA can help to inform modeling results and discuss a variety of issues relating to the use of MFA in model validation. (PsycINFO Database Record

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AimAlthough evidence supports brief, frequent CPR training, optimal training intervals have not been established. The purpose of this study was to compare nursing students' CPR skills (compressions and ventilations) with 4 different spaced training intervals: daily, weekly, monthly, and quarterly, each for 4 times in a row.