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Part of book or chapter of book . 2010 . Peer-reviewed
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Compelling Self-Motion Through Virtual Environments Without Actual Self-Motion – Using Self-Motion Illusions ('Vection') to Improve VR User Experience

Authors: Bernhard E.;

Compelling Self-Motion Through Virtual Environments Without Actual Self-Motion – Using Self-Motion Illusions ('Vection') to Improve VR User Experience

Abstract

While modern computer graphics and virtual reality (VR) simulations can have stunning photorealism, they are often unable to provide a life-like and compelling sensation of moving through the simulated world. This is in stark contrast to our real-world experience, where locomotion through the environment is naturally accompanied by the embodied sensation of self-motion, even when we are not actively walking but using other transportation devices like bicycles, cars, or buses. This fundamental difference in which we perceive simulated versus actual motions might negatively impact the perceived realism, behavioural effectiveness, user acceptance, and commercial success of virtual reality technology and applications. In this chapter, I propose and discuss how investigating, utilizing, and optimizing self-motion illusions (“vection”) might be a lean and elegant way to overcome such shortcomings and provide a truly “moving experience” in computermediated environments without the need to physically move, thus reducing overall cost and effort. The aim of this chapter is to provide an overview of the state of the art in research on visually-induced self-motion illusions in real and virtual environments. Specific focus will be on a topic that is of particular interest in the context of VR but has not been thoroughly reviewed before: Namely how self-motion illusions are not only affected by physical stimulus parameters themselves via bottom-up perceptual processes (as discussed in section 3), but also by the way we look at, perceive, and interpret the stimulus, how it is integrated into the overall display setup, and whether or not actual motion might be possible (see section 4). Knowledge of these factors can not only deepen our understanding of the complex processes underlying self-motion perception, but might also be of particular interest for VR simulations and other immersive/multi-media applications like gaming or movies, as these factors can often be manipulated with relatively little effort. Section 5 will provide a brief overview on recent studies on multi-modal contributions and interactions for vection. These indicate significant cross-modal benefits, which could, together with the results presented in earlier sections, be employed to design more effective-yet-affordable VR interfaces, as will be discussed in the final section and throughout this chapter. Possible side-effects of vection in VR are discussed in section 6.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Average
Average
Green
hybrid