||Wright-Patterson AFB, OH 454337905
An emerging vision of the future Air Force involves an immersive global decision environment in which live, virtual, and constructive entities participate in an integrated system that unifies analysis, training, and operational functions. One of the many challenges between the current state-of-the-art and the realization of this vision is the ability to rapidly create valid computational cognitive models of a wide range of decision makers. AFRL established the Performance and Learning Models team to address this and related scientific and technological challenges. Our research objective is to improve the scientific understanding of how perceptual, cognitive, and motor processes come together in an integrated architecture that enables and constrains human decision making in complex, dynamic, non-stationary environments of interest to the Air Force. Our approach involves empirical research with human participants and the development of computational and mathematical models that account for and/or predict the human data. Our strategy for simultaneously maximizing foundational cognitive science contributions, relevance to USAF S&T needs, and transition potential is to do the empirical research and the computational cognitive modeling with both abstract, knowledge-lean laboratory tasks that allow for the isolation of key cognitive phenomena from the confounds associated with knowledge-rich domains and also with complex synthetic task environments inspired by real-world USAF operational requirements. Research areas of interest include persistence and generativity for computational cognitive models, language-enabled synthetic teammates, mathematical models for predictive and prescriptive training tools, and robust decision making in integrated human-machine systems.
Gluck KA: Cognitive architectures for human factors in aviation, in Human Factors in Aviation, 2nd Edition. Edited by Salas E, Maurino D. New York: Elsevier, 2010: 375
Gluck KA: Barriers, bridges, and progress in cognitive modeling for military applications, in Proceedings of the National Academy of Engineering’s 2007 US Frontiers of Engineering Symposium. Washington (DC): National Academies Press, 2007: 99
Cognitive modeling; Mathematical modeling; Computational models; Human performance; Human learning; Cognitive science;