Comprehensive and computational models of human performance have both scientific and practical importance to human-computer system design. This project will make a significan contribution in the area of HCI modeling. The PI will expand a computational model and a simulation technology (implementation) called Queueing Network-Model Human Processor (QN-MHP) he has been developing. QN-MHP is unique, in that it integrates two complementary approaches to cognitive modeling: the procedure and production systems approach (exemplified by the MHP/GOMS family of models, ACT-R, CAPS, EPIC, and SOAR), and the queueing network approach. Procedure and production systems models have achieved great success in modeling, and in generating the detailed procedures and actions that a person might take in performing, a wide range of tasks; their shortcoming is that although they employ mathematics to analyze specific aspects of their models, they lack mathematical theories to represent the overall structure of their models. The queueing network model is able to integrate a large number of influential mathematical models of mental structure as special cases (such as Sternberg's serial stages model, McClelland's cascade model, and Schweikert's critical path network model), and is in general well suited for modeling dynamic and complex tasks and architectural arrangements of processes; as a mathematical theory alone, however, queueing networks cannot be used to generate detailed actions of a person in specific task situations, lacking procedural knowledge a person may employ in accomplishing his/her specific goals. QN-MHP integrates these two approaches by expanding the three discrete serial stages of the MHP into three continuous-transmission subnetworks of a queueing network, by defining each server with procedure functions, and by using a GOMS-style method for task analysis. In this project, the PI will use extensive driving simulator data as a testbed to (1) study different methods of modeling concurrent tasks with QN-MHP; (2) apply queueing network theory such as optimal network load balancing and server scheduling in multitask modeling; (3) perform time-series modeling and visualization of the relationship between queueing network indices such as network sojourn time and server congestion with human performance data such as time, error, and mental workload; (4) further develop QN-MHP to cover a broader range of human performance such as providing certain servers with production capabilities for problem solving; and (5) further develop the simulation technology of QN-MHP.
Broader Impacts: The QN-MHP simulator is implemented in ProModel, a widely used software package that requires minimal learning time and allows an analyst to visualize in real time the QN-MHP internal network processes, in addition to the final statistical outcomes. These features are valuable not only for interface analysis, but also for promoting teaching and training in cognitive analysis and modeling. This software and other research results will be made readily available by the PI to practitioners, researchers, and educators. With the proliferation of life-critical multimodal, multitask HCI interfaces such as in-vehicle and aviation devices, QN-MHP will significantly impact system safety and product usability.