This report represents a committee summary of the current state of knowledge regarding aftereffects and sense of presence in virtual environments (VEs). The work presented in this article, and the proposed research agenda, are the result of a special session that was set up in the framework of the Seventh International Conference on Human Computer Interaction. Recommendations were made by the committee regarding research needs in aftereffects and sense of presence, and, where possible, priorities were suggested. The research needs were structured in terms of the short, medium, and long term and, if followed, should lead toward the effective use of VE technology. The 2 most critical research issues identified were (a) standardization and use of measurement approaches for aftereffects and (b) identification and prioritization of sensorimotor discordances that drive aftereffects. Identification of aftereffects countermeasures (i.e., techniques to assist users in readily transitioning between the real and virtual worlds), reduction of system response latencies, and improvements in tracking technology were also thought to be of critical importance.

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The integration of real-time electroencephalogram (EEG) workload indices into the man-machine interface could greatly enhance performance of complex tasks, transforming traditionally passive human-system interaction (HSI) into an active exchange where physiological indicators adjust the interaction to suit a user's engagement level. The envisioned outcome is a closed-loop system that utilizes EEG and other physiological indices for dynamic regulation and optimization of HSI in real-time. As a first step towards a closed-loop system, five individuals performed as identification supervisors (IDSs) in an Aegis command and control (C 2 ) simulated environment, a combat system with advanced, automatic detect-and-track, multi-function phased array radar. The Aegis task involved monitoring multiple data sources (i.e., missile-tracks, alerts, queries, resources), detecting required actions, responding appropriately, and ensuring system status remains within desired parameters. During task operation, a preliminary workload measure calculated in real-time for each second of EEG and was used to manipulate the Aegis task demands. In post-hoc analysis, the use of a five-level workload measure to detect cognitively challenging events was evaluated. Events in decreasing order of difficulty were track selection-identification, alert-responses, hooking-tracks, and queries. High/extreme EEG-workload occurred during high cognitive-load tasks with a detection efficiency approaching 100% for selection-identification and alert-responses, 77% for hooking-tracks and 70% for queries. Over 95% of high/extreme EEG-workload across participants occurred during high-difficulty events (false positive rate < 5%). The high/extreme workload occurred between 25-30% of time. These results suggest an intelligent closed-loop system incorporating EEGworkload measures could be designed to re-allocate tasks and aid in efficiently streamlining a user's cognitive workload. Such an approach could ensure the operator remains uninterrupted during high/extreme workload periods, thereby resulting in increased productivity and reduced errors.

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