Matching Levels of Task Difficulty for Different Modes of Presentation in a VR Table Tennis Simulation by Using Assistance Functions and Regression Analysis

摘要

UX is often compared between different systems or iterations of the same system. Especially when investigating human perception processes in virtual tasks and associated effects, experimental manipulation allows for better control of confounders. When manipulating modes of presentation, such as stereoscopy or visual perspective, the quality and quantity of available sensory cues is manipulated as well, resulting not only in different user experiences, but also in modified task difficulty. Increased difficulty and lower user task performance may lead to negative attributions that spill over to the evaluation of the system as a whole (halo effect). To avoid this, the task difficulty should remain unaltered. In highly dynamic virtual environments, the modification of difficulty with Fitts' law may prove problematic, so an alternative is presented using curve fitting regression analyses of empirical data from a within-subjects experiment in a virtual table tennis simulation to calculate equal difficulty levels.