Virtual Training For Fear Of Public Speaking – Design Of an Audience For Immersive Virtual Environments

Sandra Poeschl * Ilmenau University of Technology

Research Group Media Psychology & Media Design

Nicola Doering + Ilmenau University of Technology

Research Group Media Psychology & Media Design

ABSTRACT Virtual Reality technology offers great possibilities for Cognitive Behavioral Therapy on Fear of Public Speaking: Clients can be exposed to virtual fear-triggering stimuli (exposure) and are able to role-play in virtual environments, training social skills to overcome their fear. This poster deals with the design of a realistic virtual presentation scenario based on an observation of a real audience. INDEX TERMS: J.4 [Computer Applications]: Social and Behavioral Sciences—Psychology; I.3.7 [Computing Methodologies]: Three-Dimensional Graphics and Realism—Virtual Reality

1 INTRODUCTION Fear of Public Speaking (or glossophobia or speech anxiety) is a common psychological disorder and characterized by anxiety prior to or at the thought of having to communicate verbally with any group of people. It leads to avoidance of those situations, physical distress and even panic. Treatment typically involves Cognitive Behavioral Therapy (CBT), including exposure to fear-triggering stimuli (e. g., speaking in front of a group), social skills training and relaxation training [1].

Virtual Reality technology as a therapeutic or training tool offers great possibilities for CBT: Clients can be exposed to virtual fear-triggering stimuli (exposure) and are able to role-play in virtual and different scenarios, training social skills to overcome their fear [2]. Usually, prototypical (neutral, social and anti-social) audience behavior serves as stimulus in virtual training sessions, although there is significant lack of theoretical basis on typical and realistic audience behavior. Presence (a user’s subjective psychological response to a VR system) is related to the experience of emotions [3], like fear. Therefore, using prototypical behavior – instead of realistic audience behavior – may lead to lower experiences of presence and to lesser performance in VR training applications.

The work presented in this poster aims at overcoming this problem by designing an application including realistic audience behavior. We describe an explorative observation study on realistic audience behavior. The results obtained are used to design a virtual training scenario for fear of public speaking in a CAVE.

2 RELATED WORK Related work shows that virtual presentation environments can induce fearful experiences [4], especially when featuring negative

audience behavior [5]. Also, VR exposure for Fear of Public speaking is more effective than waiting list control groups [6, 7]. In the studies mentioned, prototypical audience behavior was simulated. However, to further enhance transfer from virtual training to real life performance, modeling realistic audience behavior may be a promising next step. Our explorative study deals with this problem by gathering observational data on natural nonverbal audience behavior.

3 METHOD A real audience (consisting of n = 18 men and women) in an undergraduate seminar was observed in a structured, non-participant overt observation. We used event samples of three frontal lecture sessions, taping the lectures on video and analyzing the video material (see figure 1).

Behavior frequency of four nonverbal dimensions (eye contact, facial expression, gesture, and posture; N = 5916 behavioral actions in total, coded into 35 categories) was rated by means of a quantitative content analysis, in regard to frequency and positioning across three rows of seats within the lecture room. Further, we analyzed the first, middle and last 15 minutes of the lecture sessions (with a duration of 90 min. each), as we assumed that nonverbal audience behavior may change over time (for example packing away things towards the end of a session).

Figure 1. Screenshot of analyzed video material on audience behavior

4 RESULTS Selected behavior patterns (facial expressions and gesture) are presented by means of excerpts of the accomplished design manual. The audience to be designed will show behavioral actions according to a database that includes mean frequencies for one person per row and per minute. These are used to present our findings. Due to the multitude of facial expressions and gestures, findings are highlighted by prototypical behavior actions.

4.1 Facial expression As table 1 shows, friendly and neutral face expressions are rather common. Also, they are closely related, as a joyful facial expression changes into a neutral one when coming to an end. Social facial expressions increase with distance to the presenter, maybe to establish a closer contact. Further, neutral and social expressions increase with time. This can be explained by the fact that at the end of a lecture session, more interactions and

* sandra.poeschl@tu-ilmenau.de + nicola.doering@tu-ilmenau.de

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IEEE Virtual Reality 20124-8 March, Orange County, CA, USA978-1-4673-1246-2/12/$31.00 ©2012 IEEE

discussions take place, as frontal presentations are already finished. The anti-social expression of anger was shown rather seldom, so a natural audience seems to be either social or neutral.

Table 1. Means of frequency of facial expressions for one person per row and per minute

Facial expression

First part of session

Middle part of session

Last part of session

Pleasure First row 0.11 0.23 0.44 Middle

row 0.16 0.17 0.34

Last row 0.24 0.24 0.41 Neutral First row 0.12 0.17 0.24 Middle

row 0.17 0.2 0.32

Last row 0.24 0.17 0.32 Anger First row 0.03 0.02 0.04 Middle

row 0.03 0.02 0.01

Last row 0.01 0 0.01

4.2 Gesture Considering gestures, self-grooming is rather common (see table 2). It happens constantly and rather often. Interestingly, neither very social gestures (like nodding) or very anti-social gestures (like head shaking) happen very often. Nodding increases towards the end of a lecture, but not for the last row. This could hint at the last row not taking part in interactions at the end of the lecture. Natural audience behavior also seems to be rather friendly in regard to gestures. Maybe social norms of not openly showing disapproval lead to the low incidence of shaking one’s head.

Table 2. Means of frequency of gestures for one person per row and per minute

Gesture First part of

session

Middle part of session

Last part of session

Nodding First row 0 0.01 0.19 Middle

row 0.05 0.03 0.20

Last row 0.01 < 0.01 0.01 Self-grooming

First row 0.22 0.28 0.3

Middle row 0.36 0.37 0.34

Last row 0.24 0.23 0.26 Head shaking

First row 0 0 0

Middle row < 0.01 < 0.01 < 0.01

Last row 0 < 0.01 0

5 DISCUSSION Findings show that audiences tend to be social, but neutral and anti-social behavior is also shown seldom but constantly. Secondly, behavior differs between points of time during a lecture session and between different rows of seats.

However, the work presented has certain limitations. First of all, an undergraduate student audience at a university lecture session in Germany was observed. Results cannot be transferred to other audiences without caution. Secondly, the audience consisted mostly of women. Gender-balanced audiences could show different behavioral patterns. Thirdly, this being an

explorative study, aspects like interactions between presenter and audience, interaction between audience members themselves or exact duration of behavior actions were not considered. Future studies should aim at replicating and supplementing the obtained findings with different kinds of audiences and examine how different behavior actions are interpreted and reacted to by presenters. Still, in light of the lack of theoretical background on realistic audience behavior, the study can serve as a starting point for future research and as a first guideline for audience design.

6 CONCLUSION AND FUTURE PROSPECTS The results of this study serve to design a virtual audience in a training application for fear of public speaking in a CAVE. Currently, a concept on how to transfer our findings into audience design is developed. The next step will be the development of a prototype of the mentioned virtual training application. It will be formatively evaluated and redesigned in an iterative process according to the framework of Bowman and Hodges [8], including studies on valence of social, neutral and anti-social audience behavior, presence as well as experienced fear of public speaking during training.

ACKNOWLEDGEMENTS The authors thank Thomas Jung-Boehmcker for his support and contribution to the data collection, data preparation and analysis.

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[3] M. Slater. (2003). A Note on Presence Terminology. 11.06.2010. Available: http://presence.cs.ucl.ac.uk/presenceconnect/articles/Jan2003/melslaterJan27200391557/melslaterJan27200391557.html

[4] M. Slater, D.-P. Pertaub, C. Barker, and D. M. Clark, "An experimental study on fear of public speaking using a virtual environment.," Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on behavior and society, vol. 9, pp. 627-633, 2006.

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[8] D. A. Bowman and L. F. Hodges, "Formalizing the Design, Evaluation, and Application of Interaction Techniques for Immersive Virtual Environments," Journal of Visual Languages and Computing, vol. 10, pp. 37-53, 1999.

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