This research could be used to update training and design recommendations that are based upon the assumption that trust causes operator responses regardless of error bias.

With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity.

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Professional tasks such as air traffic control and search-and-rescue often involve visual scanning. A novel technology known as gaze-sharing is intended to help people to scan in teams by allowing each of them to see where the other is looking. However, evidence for the helpfulness of shared-gaze displays has been mixed (Brennan, Chen, Dickinson, Neider, & Zelinsky, 2008; Neider, Chen, Dickinson, Brennan, & Zelinsky, 2010). The present study used a novel mathematical analysis to measure the scanning efficiency of teammates linked by the shared-gaze technology in a 2-person visual search-and-consensus task. Results show that shared gaze helped the second searchers find and confirm the target after the first searcher had spotted it, but increased the time for the first searcher to detect the target. Results imply limits on the value of the shared-gaze technology as a way to improve real-world visual search.