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Comparing Mixed Reality Agent Representations: Studies in the Lab and in the Wild

Congdon, Ben J; Park, Gun Woo Warren; Zhang, Jingyi; Steed, Anthony; (2023) Comparing Mixed Reality Agent Representations: Studies in the Lab and in the Wild. In: 29th ACM Symposium on Virtual Reality Software and Technology. (pp. p. 26). ACM: Christchurch, New Zealand. Green open access

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Abstract

Mixed-reality systems provide a number of different ways of representing users to each other in collaborative scenarios. There is an obvious tension between using media such as video for remote users compared to representations as avatars. This paper includes two experiments (total n = 80) on user trust when exposed to two of three different user representations in an immersive virtual reality environment that also acts as a simulation of typical augmented reality simulations: full body video, head and shoulder video and an animated 3D model. These representations acted as advisors in a trivia quiz. By evaluating trust through advisor selection and self-report, we found only minor differences between representations, but a strong effect of perceived advisor expertise. Unlike prior work, we did not find the 3D model scored poorly on trust, perhaps as a result of greater congruence within an immersive context.

Type: Proceedings paper
Title: Comparing Mixed Reality Agent Representations: Studies in the Lab and in the Wild
Event: VRST 2023: 29th ACM Symposium on Virtual Reality Software and Technology
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3611659.3615719
Publisher version: https://doi.org/10.1145/3611659.3615719
Language: English
Additional information: This work is licensed under a Creative Commons Attribution International 4.0 License.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10179705
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