Enhancing Social Buffering in VR: The Effect of Group Size

Assigned Human-Computer Interaction Bachelor Thesis No Affiliation

Motivation

Anxiety disorders are the most common of all mental disorders, with an estimated 301 million people affected globally in 2019 [6]. A common way to treat anxiety disorders is with exposure therapy. In exposure therapy, patients are gradually exposed to the situations that trigger their anxiety, with the goal of reducing their fear response over time [11]. Virtual reality (VR) exposition therapy is becoming increasingly more popular, as it is more cost-effective, but not less efficient than traditional in-vivo therapy [3]. The so-called social buffering effect may facilitate exposition therapy by reducing the patients stress levels and anxiety [7]. Social buffering is a phenomenon in which individuals stress response to aversive stimuli can be mitigated by the mere presence of a conspecific or cues associated with it [7]. This was observed among various animals species like rodents [7], birds [7], fish [4], and was successfully replicated in humans [10]. It also has been successfully replicated in VR environments, demonstrating that virtual agents can elicit similar stress-mitigating effects as in real life [10]. By further understanding and researching the parameters which enhance the social buffering effect, exposition therapy could be optimized, to become more effective in reducing anxiety and stress.

One possible factor is the number of present conspecifics. Several animal studies investigated this topic. Experiments with rats showed that the effect of social buffering can be enhanced with more conspecifics [9]. Conversely, research involving zebrafish indicates that the social buffering effect remains consistent regardless of shoal size [4]. To our knowledge, there has not been any research, regarding humans.. Therefore the aim of this study is to investigate, if more conspecifics will enhance the effect of social buffering on humans in VR. To test this, this study will further build on the research done by @sb_in_vr_2021. Accordingly, our research question is whether the presence of more virtual conspecifics can significantly enhance the stress-mitigating effect on human participants.

Method

To investigate the influence of multiple virtual agents on social buffering, we will replicate and extend the procedure of [10]. Their virtual environment featured a small room where an agent and the participant sat back-to-back, with the agent facing a computer and the participant facing the opposite direction [10]. We will extend this scenario with more agents using Unreal Engine (4.24.3) and present it via the Meta Quest 3. Participants will be randomly assigned to one of two experimental conditions, featuring either two or three virtual agents. The experimental procedure consists of three blocks with 18 trials each. In each trial, participants will:

1. View a cue on a virtual screen
2. Experience a matching neutral or aversive sound (50% each)
3. Rate their subjective emotional response

The participants will be together in a room with these agents, but not interact with them. Based on power analysis (medium effect size [2], $\alpha = 0.05$, 80% power), at least 44 participants will be required. To compensate for potential unusable measurements (due to the user’s skin conductance being unmeasurable), or other failures, we will recruit 60 participants (30 participants / group).

Our main measures include skin conductance responses (SCR) for autonomic fear responses and subjective ratings of their emotional state. The results will be compared to the single-agent condition in @sb_in_vr_2021. Additionally, we will collect several other measures: the Depression Short Scale [1] to exclude individuals at risk, the Positive and Negative Affect Schedule (PANAS) questionnaire [12] before and after the experiment to assess changes in emotions (ideally, the emotions will be more negative at the end as we aim to induce anxiety), the Simulator Sickness Questionnaire [8], the Networked Minds Measure of Social Presence (NMMSP) [5] measure to assess participants’ perception of the virtual agent’s presence, and the Uncanny Valley Index.

Milestones

References

• [1] Maja Bailer, Martin Hautzinger, Dirk Hofmeister, and Ferdinand Keller. 2012. Allgemeine Depressionsskala (ADS). Hogrefe. https://digitalcollection.zhaw.ch/items/8a06ba30-483f-4570-8136-ec1288f2f346/full
• [2] Jacob Cohen. 1992. A Power Primer. Tutorials in Quantitative Methods for Psychology 112 (07 1992). https://doi.org/10.1037/0033-2909.112.1.155
• [3] Paul Emmelkamp. 2008. Virtual reality exposure therapy for anxiety disorders: A meta-analysis. Journal of anxiety disorders 22 (02 2008), 561–9. https://doi.org/10.1016/j.janxdis.2007.04.
• [4] Ana l. Faustino, André Tacão-Monteiro, and Rui F. Oliveira. 2017. Mechanisms of social buffering of fear in zebrafish. Scientific Reports 7 (2017).
• [5] Chad Harms and Jennifer Gregg. 2001. The Networked Minds Measure of Social Presence: Pilot Test of the Factor Structure and Concurrent Validity. 4th annual International Workshop on Presence, Philadelphia (01 2001).
• [6] Institute for Health Metrics and Evaluation. 2019. GBD Results Tool. https://vizhub.healthdata.org/gbd- results?params=gbd- api- 2019- permalink/716f37e05d94046d6a06c1194a8eb0c9. Accessed: 2024-11-04.
• [7] Takefumi Kikusui, James Winslow, and Yuji Mori. 2006. Social buffering: Relief from stress and anxiety. Philosophical transactions of the Royal Society of London.SeriesB,Biologicalsciences361(112006),2215-28. https://doi.org/10.1098/rstb.2006.1941
• [8] Hyun K. Kim, Jaehyun Park, Yeongcheol Choi, and Mungyeong Choe. 2018. Virtual reality sickness questionnaire (VRSQ): Motion sickness measurement index in a virtual reality environment. Applied Ergonomics 69 (2018), 66-73. https://doi.org/10.1016/j.apergo.2017.12.016
• [9] Yasushi Kiyokawa, Kazuma Kawai, and Yukari Takeuchi. 2018. The benefits of social buffering are maintained regardless of the stress level of the subject rat and enhanced by more conspecific. Physiology & Behavior 194 (2018), 177-183.
• [10] Yanyan Qi, Dorothée Bruch, Philipp Krop, Martin J. Herrman, Marc E. Latoschik, Jürgen Deckert, and Grit Hein. 2021. Social buffering of human fear is shaped by gender, social concern, and the presence of real vs virtual agents. Translational Psychiatry 11 (2021), 641.
• [11] Thomas L Rodebaugh, Holaway Robert M, and Heimberg Richard G. 2004. The treatment of social anxiety disorder. Clin Psychol Rev. 24, 7 (2004), 883-908.
• [12] David Watson, Lee Clark, and Auke Tellegen. 1988. Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales. JournalofPersonalityandSocialPsychology54(061988),1063-1070. https://doi.org/10.1037/0022-3514.54.6.1063

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