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This project aims to establish a comprehensive theoretical and empirical framework for military training that links training methods to performance outcomes in the networked battlefield environment. Key goals include predicting training impacts on performance and designing optimal training systems. The research involves developing a robust list of training principles, conducting taxonomic analyses of tasks and training methods, and assessing the influence of cognitive complications on performance under fatigue. Laboratory experiments and computational models will illustrate and evaluate these concepts.
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TRAINING FOR THE NETWORKED BATTLEFIELDAlice F. Healy and Lyle E. Bourne, Jr. University of Coloradohttp://psych.colorado.edu/~ahealy/MuriFrame.htm alice.healy@colorado.edu OBJECTIVE Construct a theoretical and empirical framework for training that can relate training methods and performance for military tasks in the networked battlefield DOD CAPABILITIES ENHANCED • Predicting the impact of training on performance • Designing optimal, cost-effective training systems Adding cognitive complications to a routine task overcomes decline in accuracy due to fatigue. ACCOMPLISHMENTS • Working list of training principles • Initial taxonomic analysis for task types and training methods • Laboratory experiments testing training principles and examining basic skill components • Computational models of a laboratory task illustrating multiple training principles • Design of complex laboratory tasks similar to actual military tasks in the networked battlefield SCIENTIFIC/TECHNICAL APPROACHES • Derive training principles from empirical data collected in psychological laboratory experiments (e.g., cognitive antidote principle illustrated above) • Develop training and task taxonomies and performance metrics • Quantify effects of training as computational models (algorithms)
