This article presents the results from a Spatial Augmented Reality (SAR) study which evaluated the cognitive cost of several predictive cues. Participants performed a validated procedural button pressing task, where the predictive cue annotations guided them to the upcoming task. While existing research has evaluated predictive cues based on their performance and self-rated mental effort, actual cognitive cost has yet to be investigated. To measure the user's brain activity, this study utilized electroencephalogram (EEG) recordings. Cognitive load was evaluated by measuring brain responses for a secondary auditory oddball task, with reduced brain responses to oddball tones expected when cognitive load in the primary task is highest. A simple monitor n-back task and procedural task comparing monitor versus SAR were conducted, followed by a version of the procedural task comparing the SAR predictive cues. Results from the brain responses were able to distinguish between performance enhancing cues with a high and low cognitive load. Electrical brain responses also revealed that having an arc or arrow guide towards the upcoming task required the least amount of mental effort.

We explore Spatial Augmented Reality (SAR) precues (predictive cues) for procedural tasks within and between workspaces and for visualizing multiple upcoming steps in advance. We designed precues based on several factors: cue type, color transparency, and multi-level (number of precues). Precues were evaluated in a procedural task requiring the user to press buttons in three surrounding workspaces. Participants performed fastest in conditions where tasks were linked with line cues with different levels of color transparency. Precue performance was also affected by whether the next task was in the same workspace or a different one.

Spatial Augmented Reality (SAR) is a useful tool for procedural tasks as virtual instructions can be co-located with the physical task. Predictive cue annotations have been utilized to further enhance user performance through SAR. These predictive cues have only been measured under ordinary circumstances. This paper aims to investigate predictive cues under a sub-optimal scenario by depriving users of sleep. Sleep deprivation is a common form of fatigue, which is known to have serious detriments on user performance. Based upon existing sleep literature, we expected predictive cue performance to degrade over time with sharp declines during the early hours of the morning. Despite these drops in performance, we hypothesized that having a predictive cue would benefit user performance in comparison to no cue. Results from a 62-hour sleep deprivation experiment indicated that providing SAR predictive cues was beneficial throughout sleep deprivation. Furthermore, having no predictive cue caused accuracy to decline earlier in the sleep deprivation period. From the predictive cues outlined in this paper, the line cue maintained the fastest response time and was least impacted by early morning performance declines.

This paper presents development on a conceptual method to remap supernumerary limbs using Virtual Reality (VR) as a platform for experimentation. Our VR system allows users to control a third arm through their own limbs such as their head, arms, and feet with the ability to switch between them. To realize and experiment with our remapping method, we used the Oculus Rift in conjunction with OptiTrack to track users in a room-scaled virtual environment. We present some initial findings from a small pilot study and conclude with suggestions for future work.

Robot assistance can reduce the user's workload of a task. However, the robot needs to be programmed or trained on how to assist the user. Virtual Reality (VR) can be used to train and validate the actions of the robot in a safer and cheaper environment. In this paper, we examine how a robotic arm can be trained using Coloured Petri Nets (CPN) and Partial Least Squares Regression (PLSR). Based upon these algorithms, we discuss the concept of using the user's acceleration and rotation as a sufficient means to train a robotic arm for a procedural task in VR. We present a work-in-progress system for training robotic limbs using VR as a cost effective and safe medium for experimentation. Additionally, we propose PLSR data that could be considered for training data analysis.

Previous research has demonstrated that Augmented Reality can reduce a user's task response time and mental effort when completing a procedural task. This paper investigates techniques to improve user performance and reduce mental effort by providing projector-based Spatial Augmented Reality predictive cues for future responses. The objective of the two experiments conducted in this study was to isolate the performance and mental effort differences from several different annotation cueing techniques for simple (Experiment 1) and complex (Experiment 2) button-pressing tasks. Comporting with existing cognitive neuroscience literature on prediction, attentional orienting, and interference, we hypothesized that for both simple procedural tasks and complex search-based tasks, having a visual cue guiding to the next task's location would positively impact performance relative to a baseline, no-cue condition. Additionally, we predicted that direction-based cues would provide a more significant positive impact than target-based cues. The results indicated that providing a line to the next task was the most effective technique for improving the users' task time and mental effort in both the simple and complex tasks.