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Journal article
Stepping into the Right Shoes: The Effects of User-Matched Avatar Ethnicity and Gender on Sense of Embodiment in Virtual Reality
IEEE transactions on visualization and computer graphics, v 30(5), pp 2434-2443
01 May 2024
PMID: 38437125
Abstract
In many consumer virtual reality (VR) applications, users embody predefined characters that offer minimal customization options, frequently emphasizing storytelling over user choice. We explore whether matching a user's physical characteristics, specifically ethnicity and gender, with their virtual self-avatar affects their sense of embodiment in VR. We conducted a 2 x 2 within-subjects experiment (n = 32) with a diverse user population to explore the impact of matching or not matching a user's self-avatar to their ethnicity and gender on their sense of embodiment. Our results indicate that matching the ethnicity of the user and their self-avatar significantly enhances sense of embodiment regardless of gender, extending across various aspects, including appearance, response, and ownership. We also found that matching gender significantly enhanced ownership, suggesting that this aspect is influenced by matching both ethnicity and gender. Interestingly, we found that matching ethnicity specifically affects self-location while matching gender specifically affects one's body ownership.
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Details
- Title
- Stepping into the Right Shoes: The Effects of User-Matched Avatar Ethnicity and Gender on Sense of Embodiment in Virtual Reality
- Creators
- Tiffany D. Do - Univ Cent Florida, Orlando, FL 32816 USACamille Isabella Protko - Univ Cent Florida, Orlando, FL 32816 USARyan P. Mcmahan - Univ Cent Florida, Orlando, FL 32816 USA
- Publication Details
- IEEE transactions on visualization and computer graphics, v 30(5), pp 2434-2443
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- The University of Central Florida
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:001205832400073
- Scopus ID
- 2-s2.0-85187404334
- Other Identifier
- 991021916518104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Software Engineering