Somatic Practices for Understanding Real, Imagined, and Virtual Realities
January 11, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Lisa May Thomas, Helen M. Deeks, Alex J. Jones, Oussama Metatla, David R. Glowacki
arXiv ID
1901.03536
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.MM
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In most VR experiences, the visual sense dominates other modes of sensory input, encouraging non-visual senses to respond as if the visual were real. The simulated visual world thus becomes a sort of felt actuality, where the 'actual' physical body and environment can 'drop away', opening up possibilities for designing entirely new kinds of experience. Most VR experiences place visual sensory input (of the simulated environment) in the perceptual foreground, and the physical body in the background. In what follows, we discuss methods for resolving the apparent tension which arises from VR's prioritization of visual perception. We specifically aim to understand how somatic techniques encouraging participants to 'attend to their attention' enable them to access more subtle aspects of sensory phenomena in a VR experience, bound neither by rigid definitions of vision-based virtuality nor body-based corporeality. During a series of workshops, we implemented experimental somatic-dance practices to better understand perceptual and imaginative subtleties that arise for participants whilst they are embedded in a multi-person VR framework. Our preliminary observations suggest that somatic methods can be used to design VR experiences which enable (i) a tactile quality or felt sense of phenomena in the virtual environment (VE), (ii) lingering impacts on participant imagination even after the VR headset is taken off, and (iii) an expansion of imaginative potential.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted