This study explores how VR controller interfaces affect how participants hold a virtual push broom in VR. We aim to understand how the affordances available with current VR controllers and a custom broom VR controller impact user hand placement in a visual VR broom task. We compare hand placement in two VR conditions against hand placement holding a real push broom. Our goal is to understand the roles that controllers have on recreating physically accurate actions in VR training scenarios. The results from this initial pilot show an effect of the broom controller condition but also that the order in which some of the conditions were presented to subjects affected the way subjects held the VR and real push brooms in subsequent actions. Future work will continue to explore how controller affordance may impact the role of training in VR.

Augment: To make (something already developed or well under way) greater, as in size, extent, or quantity; e.g., Continuing rains augmented the floodwaters. (American Heritage Dictionary of the Eng...

Queueing Network-Model Human Processor (QN-MHP) is a computational architecture that integrates two complementary approaches to cognitive modeling: the queueing network approach and the symbolic approach (exemplified by the MHP/GOMS family of models, ACT-R, EPIC, and SOAR). Queueing networks are particularly suited for modeling parallel activities and complex structures. Symbolic models have particular strength in generating a person's actions in specific task situations. By integrating the two approaches, QN-MHP offers an architecture for mathematical modeling and real-time generation of concurrent activities in a truly concurrent manner. QN-MHP expands the three discrete serial stages of MHP, of perceptual, cognitive, and motor processing, into three continuous-transmission subnetworks of servers, each performing distinct psychological functions specified with a GOMS-style language. Multitask performance emerges as the behavior of multiple streams of information flowing through a network, with no need to devise complex, task-specific procedures to either interleave production rules into a serial program (ACT-R), or for an executive process to interactively control task processes (EPIC). Using QN-MHP, a driver performance model was created and interfaced with a driving simulator to perform a vehicle steering, and a map reading task concurrently and in real time. The performance data of the model are similar to human subjects performing the same tasks.