TY - JOUR
AU - Parasuraman, Raja
AB - As human-machine systems grow in size and complexity, there is a need to understand and model how human attentional limitations affect system performance, especially in large networks. As a first step, human-in-the-loop experiments can provide the requisite data. Secondly, such data can be modeled to provide insights by predicting performance with a large number of vehicles. Accordingly, we first carried out an experiment examining human-UAV system performance under low and high levels of task load. We also examined the effects of a networked environment on performance by manipulating the number and relevance of network message traffic from automated agents. Results showed that in conditions of high task load, performance degraded. Moreover, performance increased with the help of relevant messages, and decreased with irrelevant, noise messages. Furthermore, a simple correlation showed a fairly strong connection between working memory scores and our collected performance data. Using regression to model this data revealed that a simple linear equation does not provide for very accurate modeling of different aspects of decision making performance. However, inclusion of the OSPAN working memory capacity measure improves prediction capability considerably. Together, the results of this study show that human-automation team performance metrics can be modeled and used to predict performance under varying levels of traffic, probability of assistance, and working memory capacity in a complex networked environment.
TI - Modeling Human-Automation Team Performance in Networked Systems: Individual Differences in Working Memory Count
JF - Proceedings of the Human Factors and Ergonomics Society Annual Meeting
DO - 10.1177/154193121005401408
DA - 2010-09-01
UR - https://www.deepdyve.com/lp/sage/modeling-human-automation-team-performance-in-networked-systems-TEH5FKgJKx
SP - 1087
EP - 1091
VL - 54
IS - 14
DP - DeepDyve
ER -