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Jennifer M. Riley

Researcher at Mississippi State University

Publications -  23
Citations -  779

Jennifer M. Riley is an academic researcher from Mississippi State University. The author has contributed to research in topics: Situation awareness & Task (project management). The author has an hindex of 12, co-authored 23 publications receiving 722 citations.

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On the Design of Adaptive Automation for Complex Systems

TL;DR: A constrained review of human factors issues relevant to adaptive automation, including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems are presented.
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Adaptive Automation of a Dynamic Control Task Based on Secondary Task Workload Measurement

TL;DR: This study further explored the psychophysical assessment approach by using a secondary task measure of workload to facilitate control allocations in a complex primary task.
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Situation Awareness Oriented Design: From User's Cognitive Requirements to Creating Effective Supporting Technologies

TL;DR: The development of tool suites for supporting high levels of situation awareness in military command and control are presented to illustrate the use of the SA-Oriented Design process for translating the results of cognitive task analyses into to user-centered system designs.
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Collaborative planning and situation awareness in Army command and control.

TL;DR: This paper summarizes several critical human factors issues associated with planning in a rapidly evolving environment, as identified in the investigation, and describes system design concepts aimed at addressing these challenges to distributed collaborative planning of C2 activities.
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Effects of Visual Interface Design, and Control Mode and Latency on Performance, Telepresence and Workload in a Teleoperation Task:

TL;DR: Evaluating the influence of interface design configuration, control mode and latency on teleoperation performance, telepresence, and workload in a pick-and-place task demonstrated significant benefits of using VR in conjunction with video feedback to control the telerobot.