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The design of virtual audiences: Noticeable and recognizable behavioral styles

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Author: Kang, N. · Brinkman, W.P. · Birna Van Riemsdijk, M. · Neerincx, M.
Type:article
Date:2016
Publisher: Elsevier Ltd
Source:Computers in Human Behavior, 55, 680-694
Identifier: 529729
doi: doi:10.1016/j.chb.2015.10.008
Keywords: Psychology · Bodily expression recognition · Expressive behavior · Simulated audience settings · Virtual audience · Technology transfer · Attitude parameter · Behavioral model · Design experiments · Expression recognition · Expressive behavior · Scientific researches · Validation study · Virtual audience · Behavioral research · Human & Operational Modelling · PCS - Perceptual and Cognitive Systems · ELSS - Earth, Life and Social Sciences

Abstract

Expressive virtual audiences are used in scientific research, psychotherapy, and training. To create an expressive virtual audience, developers need to know how specific audience behaviors are associated with certain characteristics of an audience, such as attitude, and how well people can recognize these characteristics. To examine this, four studies were conducted on a virtual audience and its behavioral models: (I) a perception study of a virtual audience showed that people (n = 24) could perceive changes in some of the mood, personality, and attitude parameters of the virtual audience; (II) a design experiment whereby individuals (n = 24) constructed 23 different audience scenarios indicated that the understanding of audience styles was consistent across individuals, and the clustering of similar settings of the virtual audience parameters revealed five distinct generic audience styles; (III) a perception validation study of these five audience styles showed that people (n = 100) could differentiate between some of the styles, and the audience's attentiveness was the most dominating audience characteristic that people perceived; (IV) the examination of the behavioral model of the virtual audience identified several typical audience behaviors for each style. We anticipate that future developers can use these findings to create distinct virtual audiences with recognizable behaviors. © 2015 Elsevier Ltd.