Cognitive transfer of spatial awareness states from immersive virtual environments to reality
ACM Transactions on Applied Perception (TAP)
Perceptually-motivated graphics, visualization and 3D displays
ACM SIGGRAPH 2010 Courses
Perception in graphics, visualization, virtual environments and animation
SIGGRAPH Asia 2011 Courses
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This dissertation describes three experiments that were conducted to explore the influence of lighting in virtual environments. The first experiment (Pit Experiment), involving 55 participants, took place in a stressful, virtual pit environment. The purpose of the experiment was to determine if the level of lighting quality and degree of texture resolution increased the participants' sense of presence as measured by physiological responses. Findings indicated that, as participants moved from a low-stress environment to an adjacent high-stress environment, there were significant increases in all physiological measurements. The experiment did not discriminate between conditions. In the second experiment (Gallery Experiment), 63 participants experienced a non-stressful virtual art gallery. This experiment studied the influence of lighting quality, position, and intensity on movement and attention. Participants occupied spaces lit with higher intensities for longer periods of time and gazed longer at objects that were displayed under higher lighting contrast conditions. This experiment successfully utilized a new technique, attention mapping, for measuring behavior in a three-dimensional virtual environment. Attention mapping provides an objective record of viewing times. Viewing times were used to examine and compare the relative importance of different components in the environment. Experiment 3 (Knot Experiment) utilized 101 participants to investigate the influence of three lighting models (ambient, local, and global) on object recognition accuracy and speed. Participants looked at an object rendered with one lighting model and then searched for that object among distractor objects rendered with the same or different lighting model. Accuracy scores were significantly lower when there were larger differences in the lighting model between the search object and searched set of objects. Search objects rendered in global or ambient illumination took significantly longer to identify than those rendered in a local illumination model.