U.S. Department of Energy

Pacific Northwest National Laboratory

Modeling Workload Capacity

Thursday, June 18, 2015
Dr. Leslie Blaha
Workload capacity: ability of our cognitive processing mechanisms to respond to the changes in task demands that influence the workload. These include changes to the number of subtasks within a task, items in a visual or memory search display, and manipulated features in a visual processing task. Blaha will discuss a human information processing modeling perspective on workload capacity, from which we measure mental work with hazard functions of response time data. Capacity Coefficient measures of workload capacity will be defined based on comparison of observed performance to baseline unlimited capacity models. These model-based measures are non-parametrically defined on empirical response time data, and the non-parametric nature makes them flexible and broadly applicable across perception and decision making tasks. The use of the capacity coefficient on motor movement time in human-computer interaction and cognitive response time visual decision making tasks will be illustrated, and efforts moving these models into dynamic, multitasking environments will be discussed.
Speaker Bio

Mathematical psychologist Dr. Leslie M. Blaha is an Engineering Research Psychologist in the Battlespace Visualization Branch, Warfighter Interface Division, Human Effectiveness Directorate, 711th Human Performance Wing, Air Force Research Laboratory, Air Force Materiel Command, Wright-Patterson Air Force Base, Ohio. She leads the in-house research team supporting the development of visual analytics interfaces for Warfighter needs. Blaha is a 2010 graduate of Indiana University with a joint Ph.D. in Psychology and Cognitive Science, and a 2003 graduate of Miami University with a Bachelors's degrees in Mathematics and Statistics, and Interdisciplinary Studies. Her research is focused on characterizing and modeling the efficiency with which people are able to use information under changing cognitive and perceptual workload demands, which is termed processing capacity. In particular, she aims to model experience-dependent changes in processing capacity resulting from perceptual learning.

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