Modeling Human Attention And Performance In Automated Environments With Low Task Loading
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Modeling Human Attention and Performance in Automated Environments with Low Task Loading
Automation has the benefit of reducing human operators' workload. By leveraging the power of computers and information technology, the work of human operators is becoming easier. However, when the workload is too low but the human is required to be present either by regulation or due to limitations of automation, human performance can be negatively affected. Negative consequences such as distraction, mind wandering, and inattention have been reported across many high risk settings including unmanned aerial vehicle operation, process control plant supervision, train engineers, and anesthesiologists. Because of the move towards more automated systems in the future, a better understanding is needed to enable intervention and mitigation of possible negative impacts. The objectives of this research are to systematically investigate the attention and performance of human operators when they interact with automated systems under low task load, build a dynamic model and use it to facilitate system design. A systems-based framework, called the Boredom Influence Diagram, was proposed to better understand the relationships between the various influences and outcomes of low task loading. A System Dynamics model, named the Performance and Attention with Low-task-loading (PAL) Model, was built based on this framework. The PAL model captures the dynamic changes of task load, attention, and performance over time in long duration low task loading automated environments. In order to evaluate the replication and prediction capability of the model, three dynamic hypotheses were proposed and tested using data from three experiments. The first hypothesis stated that attention decreases under low task load. This was supported by comparing model outputs with data from an experiment of target searching using unmanned vehicles. Building on Hypothesis 1, the second and third hypotheses examined the impact of decreased attention on performance in responding to an emergency event. Hypothesis 2 was examined by comparing model outputs with data from an experiment of accident response in nuclear power plant monitoring. Results showed that performance is worse with lower attention levels. Hypothesis 3 was tested by comparing model outputs with data from an experiment of defensive target tracking. The results showed that the impact of decreased attention on performance was larger when the task was difficult. The process of testing these three hypotheses shows that the PAL model is a generalized theory that could explain behaviors under low task load in different supervisory control settings. Finally, benefits, limitations, generalizability and applications of the PAL model were evaluated. Further research is needed to improve and extend the PAL model, investigate individual differences to facilitate personnel selection, and develop system and task designs to mitigate negative consequences.
Human Performance in Automated and Autonomous Systems
This book examines recent advances in theories, models, and methods relevant to automated and autonomous systems. The following chapters provide perspectives on modern autonomous systems, such as self-driving cars and unmanned aerial systems, directly from the professionals working with and studying them. Current theories surrounding topics such as vigilance, trust, and fatigue are examined throughout as predictors of human performance in the operation of automated systems. The challenges related to attention and effort in autonomous vehicles described within give credence to still-developing methods of training and selecting operators of such unmanned systems. The book further recognizes the need for human-centered approaches to design; a carefully crafted automated technology that places the "human user" in the center of that design process. Features Combines scientific theories with real-world applications where automated technologies are implemented Disseminates new understanding as to how automation is now transitioning to autonomy Highlights the role of individual and team characteristics in the piloting of unmanned systems and how models of human performance are applied in system design Discusses methods for selecting and training individuals to succeed in an age of increasingly complex human-machine systems Provides explicit benchmark comparisons of progress across the last few decades, and identifies future prognostications and the constraints that impinge upon these lines of progress Human Performance in Automated and Autonomous Systems: Current Theory and Methods illustrates the modern scientific theories and methods to be applied in real-world automated technologies.
Automation and Human Performance
There is perhaps no facet of modern society where the influence of computer automation has not been felt. Flight management systems for pilots, diagnostic and surgical aids for physicians, navigational displays for drivers, and decision-aiding systems for air-traffic controllers, represent only a few of the numerous domains in which powerful new automation technologies have been introduced. The benefits that have been reaped from this technological revolution have been many. At the same time, automation has not always worked as planned by designers, and many problems have arisen--from minor inefficiencies of operation to large-scale, catastrophic accidents. Understanding how humans interact with automation is vital for the successful design of new automated systems that are both safe and efficient. The influence of automation technology on human performance has often been investigated in a fragmentary, isolated manner, with investigators conducting disconnected studies in different domains. There has been little contact between these endeavors, although principles gleaned from one domain may have implications for another. Also, with a few exceptions, the research has tended to be empirical and only theory-driven. In recent years, however, various groups of investigators have begun to examine human performance in automated systems in general and to develop theories of human interaction with automation technology. This book presents the current theories and assesses the impact of automation on different aspects of human performance. Both basic and applied research is presented to highlight the general principles of human-computer interaction in several domains where automation technologies are widely implemented. The major premise is that a broad-based, theory-driven approach will have significant implications for the effective design of both current and future automation technologies. This volume will be of considerable value to researchers in human