No abstract

Two experiments are presented that examine alternative methods for invoking automation. In each experiment, participants were asked to perform simultaneously a monitoring task and a resource management task as well as a tracking task that changed between automatic and manual modes. The monitoring task required participants to detect failures of an automated system to correct aberrant conditions under either high or low system reliability. Performance on each task was assessed as well as situation awareness and subjective workload.&at used theii EEG signals to switch the tracking task between automatic and manual modes. The remaining participants were yoked to participants from the adaptive condition and received the same schedule of mode switches, but their EEG had no effect on the automation. Within each group, half of the participants were assigned to either the low or high reliability monitoring task. In addition, within each combination of automation invocation and system reliability, participants were separated into high and low complacency potential groups. The results revealed no significant effects of automation invocation on the performance measures; however, the high complacency individuals demonstrated better situation awareness when working with the adaptive automation system.The second experiment was the same as the first with one important exception. Automation was invoked manually. Thus, half of the participants pressed a button to invoke automation for 10 s. The remaining participants were yoked to participants from the adaptable condition and received the same schedule of mode switches, but they had no control over the automation. The results showed that participants who could invoke automation performed more poorly on the resource management task and reported higher levels of subjective workload. Further, those who invoked automation more frequently performed more poorly on the tracking task and reported higher levels of subjective workload. and the adaptable condition in the second experiment revealed only one sigaificant difference: the subjective workload was higher in the adaptable condition. Overall, the results show that a brain-based, adaptive automation system may facilitate situation awareness for those individuals who are more complacent toward automation. By contrast, requiring operators to invoke automation manually may have some detrimental impact on performance but does appear to increases subjective workload relative to an adaptive system. Ir? the first experhent, half of Lhe participants worked with a brain-based system A comparison of participants from the adaptive condition in the first experiment https://ntrs.nasa.gov/search.jsp?R=20040084079 2018-05-10T11:05:30+00:00Z

No abstract

The present study describes a Virtual Environment (VE) designed to train individuals to perform the role of a military checkpoint guard. Participants stood guard at a fictitious base in which simulated drivers in vehicles would approach seeking entrance. Participants were asked to inspect each vehicle, interact with the drivers, verify their identification, and make a decision to allow the driver to enter the base, to detain the vehicle, or to ask the driver to turn around and leave. The experiment was conducted in a CAVETM with stereoscopic visual and auditory displays, participant tracking, and voice recognition. The results showed that participants were able to learn quite effectively in the VE with the biggest performance improvements seen in the areas of proper protocol and social influence. These findings suggest that VE technology holds promise for activities that are more like experience-based training and which place a greater emphasis on social interaction skills.