Removing the mask - do people over trust avatars reconstructed from video?

Campion, SP, Landowska, A, Duckworth, T, Moore, C, Eachus, P ORCID: and Roberts, DJ ORCID: 2017, Removing the mask - do people over trust avatars reconstructed from video? , in: 14th EuroVR International Conference, EuroVR 2017, December 12–14, 2017, Laval, France.

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This experiment compared the detection of deceit across video conferencing and a fixed viewpoint 3D video based computer graphic medium. The purpose was to determine if the process of 3D reconstruction influenced trust by reducing detail of facial expression. Comparison with the literature investigates the impact of facial expression on trust. Inspiration comes from previous studies in the natural and virtual world that suggest a stronger tendency to over trust a person when their facial expression is hidden. A virtual avatar that copies head and eye movement but not that of the face, could be argued as akin to a person wearing a mask. Thus, our opening research question is: Would a 3D medium that removed this mask result in a truth bias similar to video and therefore real world? Two confederates each gave a set of accounts of which half were true. These were captured and transmitted simultaneously in real time using 2D and full 3D video based communication mediums. Recordings of these sessions were later examined by two sets of participants. Twenty-one participants were asked to determine which accounts were true. Measures included: accuracy at detecting truth and deceit, and from this tendency to over trust and lastly cognitive effort in determining truthfulness. Results show that participants performed and worked to a similar degree in both mediums. Findings are of interest to those developing 3D telepresence technologies and virtual humans, and to those concerned with the trustworthiness of a medium.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Schools: Schools > School of the Built Environment
Journal or Publication Title: Natural Language Processing and Information Systems
Publisher: Springer
Series Name: Lecture Notes in Computer Science
ISBN: 9783319723228
ISSN: 0302-9743
Related URLs:
Depositing User: Dr. Simon Campion
Date Deposited: 15 Mar 2018 09:44
Last Modified: 15 Feb 2022 22:57

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