Modeling the Impact of Workload in Network Centric Supervisory Control Settings

The Department of Defense’s vision of network centric operations will likely bring about higher operator mental workload due to the large volume of incoming information. As a result, it is critical that systems designers develop predictive models of both human and system performance, such that they can determine how a proposed technology will influence not only operator workload, but also system performance. To this end, this paper introduces a discrete event simulation approach to human-system modeling that includes a quantitative relationship between workload and performance, inspired by the Yerkes-Dodson relationship. Using the concept of utilization, or operator percent busy time, as a surrogate workload measure, we demonstrate that a quantitative instantiation of an inverted-U workloadperformance curve improves discrete event simulation model predictions in a human supervisory control model of single operator control of multiple unmanned vehicles. While this work generates the first known empirical evidence of a parabolic workload-performance as a variable in a quantitative predictive human performance model, this effort is preliminary and future research implications are discussed.

[1]  Mary L. Cummings,et al.  Developing Operator Capacity Estimates for Supervisory Control of Autonomous Vehicles , 2007, Hum. Factors.

[2]  Carl Nehme,et al.  Modeling human supervisory control in heterogeneous unmanned vehicle systems , 2009 .

[3]  Jacob W. Crandall,et al.  Developing performance metrics for the supervisory control of multiple robots , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[4]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[5]  John W. Senders,et al.  The Human Operator as a Monitor and Controller of Multidegree of Freedom Systems , 1964 .

[6]  P. Pina.  Identifying Generalizable Metric Classes to Evaluate Human-Robot Teams , 2008 .

[7]  Mary L. Cummings,et al.  Predicting Controller Capacity in Supervisory Control of Multiple UAVs , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Jacob W. Crandall,et al.  Identifying Predictive Metrics for Supervisory Control of Multiple Robots , 2007, IEEE Transactions on Robotics.

[9]  R. Yerkes,et al.  The relation of strength of stimulus to rapidity of habit‐formation , 1908 .

[10]  Diana Schmidt.  A Queuing Analysis of the Air Traffic Controller''s Workload , 1978 .

[11]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[12]  D. Hebb.  Drives and the C.N.S. (conceptual nervous system). , 1955, Psychological review.

[13]  Mary L. Cummings,et al.  Automation Architecture for Single Operator, Multiple UAV Command and Control, , 2007 .

[14]  Jaime R. Carbonell,et al.  A Queueing Model of Visual Sampling Experimental Validation , 1968 .

[15]  Thomas B. Sheridan,et al.  On How Often the Supervisor Should Sample , 1970, IEEE Trans. Syst. Sci. Cybern..

[16]  F. T. Eggemeier,et al.  Recommendations for Mental Workload Measurement in a Test and Evaluation Environment , 1993 .

[17]  K. Preston White,et al.  Systems engineering models of human-machine interaction , 1981, Proceedings of the IEEE.

[18]  J. Crandall,et al.  Using Discrete-Event Simulation to Model Situational Awareness of Unmanned-Vehicle Operators , 2008 .

[19]  Philip J. Smith,et al.  Brittleness in the design of cooperative problem-solving systems: the effects on user performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Peter A. Hancock,et al.  From the Inverted-U to the Extended-U: The Evolution of a Law of Psychology , 2003 .

[21]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[22]  P. Hancock,et al.  A Dynamic Model of Stress and Sustained Attention , 1989, Human factors.

[23]  Yili Liu,et al.  Queueing network modeling of human performance of concurrent spatial and verbal tasks , 1994, IEEE Trans. Syst. Man Cybern. Part A.

[24]  Yili Liu,et al.  Queueing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems , 2006, TCHI.

[25]  Thomas B. Sheridan,et al.  Telerobotics, Automation, and Human Supervisory Control , 2003 .

[26]  Jacob W. Crandall,et al.  Attention Allocation Efficiency in Human-UV Teams , 2007 .