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Lawrence J. Prinzel

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Publications by Lawrence J. Prinzel (bibliography)

2009

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Alexander, Amy L., Stelzer, Emily M., Kim, Sang-Hwan, Kaber, David B. and Prinzel, Lawrence J. (2009): Data and Knowledge as Predictors of Perceptions of Display Clutter, Subjective Workload and Pilot Performance. In: Proceedings of the Human Factors and Ergonomics Society 53rd Annual Meeting 2009. pp. 21-25.

“Display clutter is defined as an unintended effect of displaying visual imagery that may obscure or confuse other information, or that may be redundant or not relevant to the task at hand. There exists a limited amount of research that has explored both data-driven and knowledge-driven parameters as dual contributors to perceptions of clutter. In the present study, six pilots flew simulated approaches under varied workload conditions with synthetic and enhanced vision display configurations that represented "low," "medium," and "high" clutter. Results evinced that high clutter displays produced elevated reports of perceived clutter and workload due to density or redundant presentation of information, while low clutter displays were perceived as less cluttered but challenging to use because of a lack of information typically required for flight. Pilots identified both data-driven (bottom-up) and knowledge-driven (top-down) as contributors to clutter, and these challenges were mirrored in flight technical performance. Conclusions support the notion that design of advanced technologies must consider not only the physical appearance of data within the display, but also the utility of that information to tasks the displays are designed to support.”

1995

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Prinzel, Lawrence J., Hitt, James M., Scerbo, Mark W. and Freeman, Frederick G. (1995): Feedback Contingencies and Bio-Cybernetic Regulation of Operator Workload. In: Proceedings of the Human Factors and Ergonomics Society 39th Annual Meeting 1995. p. 947.

“Bio-cybernetic systems have been proposed for orchestrating changes among levels of automation when continuous measures of operator engagement are needed. In the present study, a bio-cybernetic, closed-loop system for regulating operator engagement under different feedback contingencies was examined. Subjects performed a compensatory tracking task while their EEG was continuously sampled from 4 cortical sites. An engagement index (20 β / (α + θ)) was derived from the EEG and used to switch between modes of automation. Under negative feedback conditions, the system switched to automatic mode when the engagement index reflected increases in arousal and manual mode for decreases in arousal. Under positive feedback, increases in arousal maintained the system in manual mode and decreases in arousal in the automatic mode. Subjects participated in two 16-min trials under each feedback condition and estimates of workload were obtained after each trial using the NASA-TLX. The results showed that the negative feedback condition resulted in better tracking performance coupled with moderate levels of workload while the positive feedback condition produced poorer performance and lower workload. Further, the system's performance was more stable under negative feedback conditions. These findings demonstrate the system's ability to moderate engagement through a closed-loop system driven by EEG. In addition, the cybernetic operation of the system to dynamically allocate tasks between the system and the operator as predicted by feedback control behavior was validated.”

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Prinzel, Lawrence J., Scerbo, Mark W., Freeman, Frederick G. and Mikulka, Peter J. (1995): A Bio-Cybernetic System for Adaptive Automation. In: Proceedings of the Human Factors and Ergonomics Society 39th Annual Meeting 1995. pp. 1365-1369.

“A bio-cybernetic, closed-loop system was validated for use in an adaptive automation environment. Subjects were asked to perform either a single task or multiple tasks from the Multi-Attribute Task Battery. EEG was continuously sampled while they performed the task(s) and an EEG index was derived (20 Beta/Alpha + Theta). The system switched between manual and automatic modes according to the level of operator engagement based upon the EEG index. The NASA-TLX was administered after each trial. The results of the study demonstrated that it was possible to moderate an operator's level of engagement through a closed-loop system driven by the operator's EEG. In addition, the system was sensitive to increases in task load. These findings show promise for designing adaptive automation technology around psychophysiological input.”

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