Fysh, Matthew C., Trifonova, Iliyana Venelinova, Allen, John et al. (3 more authors) (2022) Avatars with faces of real people: A construction method for scientific experiments in virtual reality. Behavior research methods. pp. 1461-1475. ISSN 1554-351X
Abstract
Experimental psychology research typically employs methods that greatly simplify the real-world conditions within which cognition occurs. This approach has been successful for isolating cognitive processes, but cannot adequately capture how perception operates in complex environments. In turn, real-world environments rarely afford the access and control required for rigorous scientific experimentation. In recent years, technology has advanced to provide a solution to these problems, through the development of affordable high-capability virtual reality (VR) equipment. The application of VR is now increasing rapidly in psychology, but the realism of its avatars, and the extent to which they visually represent real people, is captured poorly in current VR experiments. Here, we demonstrate a user-friendly method for creating photo-realistic avatars of real people and provide a series of studies to demonstrate their psychological characteristics. We show that avatar faces of familiar people are recognised with high accuracy (Study 1), replicate the familiarity advantage typically observed in real-world face matching (Study 2), and show that these avatars produce a similarity-space that corresponds closely with real photographs of the same faces (Study 3). These studies open the way to conducting psychological experiments on visual perception and social cognition with increased realism in VR.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2021 |
Keywords: | face,avatar,virtual reality,recognition,matching,face-space |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 10 Sep 2021 10:40 |
Last Modified: | 03 Nov 2024 01:27 |
Published Version: | https://doi.org/10.3758/s13428-021-01676-5 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3758/s13428-021-01676-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178070 |
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