Human-computer interaction design principles largely focus on static representations and have yet to fully incorporate theories of perception appropriate for the dynamic multimodal interactions inherent to virtual environment (VE) interaction. Theories of direct perception, in particular affordance theory, may prove particularly relevant to enhancing VE interaction design. The present research constructs a conceptual model of how affordances are realized in the natural world and how lack of sensory stimuli may lead to realization failures in virtual environments. Implications of the model were empirically investigated by examining three affordances: passability, catchability, and flyability. The experimental design involved four factors for each of the three affordances and was implemented as a 2 [subscript IV] [superscript 4-1] fractional factorial design. The results demonstrated that providing affording cues led to behavior closely in-line with real-world behavior. More specifically, when given affording cues participants tended to rotate their virtual bodies when entering narrow passageways, accurately judge balls as catchable, and fly when conditions warranted it. The results support the conceptual model and demonstrate 1) that substituting designed cues via sensory stimuli in available sensory modalities for absent or impoverished modalities may enable the perception of affordances in VEs; 2) that sensory stimuli substitutions provide potential approaches for enabling the perception of affordances in a VE which in the real world are cross-modal; and 3) that affordances relating to specific action capabilities may be enabled by designed sensory stimuli. This research lays an empirical foundation for a science of VE design based on choosing and implementing design properties so as to evoke targeted user behavior
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-1093 |
Date | 01 January 2004 |
Creators | Gross, David Charles |
Publisher | STARS |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Electronic Theses and Dissertations |
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