Modeling human performance using the queuing network-model human processor (QN-MHP).

Predicting human performance (temporally and strategically) in various scenarios has significant implications for understanding human behavior. Researchers who develop approaches to predict human performance attempt to look inside the black box of the mind to understand its inner workings. In turn, comprehensive and computational human performance modeling approaches allow the consideration of human capabilities when evaluating product or system design alternatives, improving both the functionality and safety of designs. Many current approaches utilize knowledge-based techniques to model performance but, although they have unique strengths in modeling human behavior, they lack a rigorous mathematical structure. In contrast, mathematical approaches such as queuing network theory are based on a solid mathematical foundation for time- and capacity-based performance analysis, but cannot model how humans choose to act in specific situations. By linking elements of the Model Human Processor (MHP) and GOMS methods to a general queuing network representing human information processing, the Queuing Network - Model Human Processor (QN-MHP) bridges the gap between the knowledge-based and mathematical approaches. Neurophysiological findings provide the basis for the underlying framework of servers while concepts from the MHP, GOMS, and other human performance research guide the logic and timing used to generate responses to stimuli. To examine the QN-MHP's feasibility, two reaction time tasks, a visual search task, and a steering task were modeled. Each was described using a variant of the GOMS language to dictate procedural flow. After mapping the GOMS description to the QN-MHP, the resulting model was run using commercial discrete-event simulation software. Simple reaction times were consistent with human performance literature, typically ranging from 200 to 250 msecs. Choice reaction times matched times predicted by the Hick-Hyman law (r 2 > 0.99); a sensitivity analysis of the model revealing a direct relationship between default parameters used in the QN-MHP and constants used in the law. Three visual search strategies were modeled, each yielding reasonable eye movement and search times as well as patterns consistent with each strategy. Although the QN-MHP simulation indicated flaws in the logic underlying three GOMS task descriptions for steering, the QN-MHP nonetheless produced actions consistent and reasonable for each description.Ph.D.Applied SciencesComputer scienceIndustrial engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/129422/2/3042067.pd