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2020 | OriginalPaper | Chapter

Do Distant or Colocated Audiences Affect User Activity in VR?

Authors : Romain Terrier, Nicolas Martin, Jeremy Lacoche, Valerie Gouranton, Bruno Arnaldi

Published in: Transactions on Computational Science XXXVII

Publisher: Springer Berlin Heidelberg

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Abstract

We explore the impact of distant or colocated real audiences on social inhibition through a user study in virtual reality (VR). The study investigates, in an application, the differences among two multi-user configurations (i.e., the local and distant conditions) and one control condition where the user is alone (i.e., the alone condition). In the local condition, a single user and a real audience share the same real room. Conversely, in the distant condition, the user and the audience are separated into two different real rooms. The user performed a categorization of numbers task in VR, for which the users’ performance results (i.e., type and answering time) are extracted as subjective feelings and perceptions (i.e., perceptions of others, stress, cognitive workload, presence). The differences between the local and distant configurations are explored. Furthermore, we investigate any gender biases in the objective and subjective results. During the local and distant conditions, the presence of a real audience affects the user’s performance due to social inhibition. The users are even more influenced when the audience does not share the same room, despite the audience being less directly perceived in this condition.

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next chapter Physical Environment Reconstruction Beyond Light Polarization for Coherent Augmented Reality Scene on Mobile Devices
Footnotes
3
M0 is the null model only with the random effect of the participant:
\(M0=AT\sim (1|Participant)\).
 
4
M1 is the model with the random effect of the participant and the effect of the condition: \(M1=AT\sim Condi+(1|Participant)\).
 
5
M2 is the model with the random effect of the participant and the effect of the trial:
\(M2=AT\sim Trial+(1|Participant)\).
 
6
M3 is the model with the random effect of the participant plus the effect of the condition and the effect of the trial: \(M3=AT\sim Condi+Trial+(1|Participant)\).
 
7
M4 is the model with the random effect of the participant plus the effect of the condition and the effect of the trial and their interaction:
\(M4=AT\sim Condi*Trial+(1|Participant)\).
 
8
Equation to obtain the value used of the HR for analyses: \(DHR_{block_i}=HR_{block_i}-HR_{baseline}\).
 
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Metadata
Title
Do Distant or Colocated Audiences Affect User Activity in VR?
Authors
Romain Terrier
Nicolas Martin
Jeremy Lacoche
Valerie Gouranton
Bruno Arnaldi
Copyright Year
2020
Publisher
Springer Berlin Heidelberg
Book
Transactions on Computational Science XXXVII

Print ISBN: 978-3-662-61982-7

Electronic ISBN: 978-3-662-61983-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-662-61983-4_1