The Effects of Ingroup Bias

on Public Speaking Anxiety in Virtual Reality

Lotte E. J. Biesmans, Pleun J. M. van Hees, Lisa E. Rombout, Maryam Alimardani and Eriko Fukuda

Department of Cognitive Science and Artiﬁcial Intelligence, Tilburg University, Warandelaan, Tilburg, The Netherlands

Keywords:

Virtual Reality, Virtual Agents, Social Agents, Ingroup Bias, Emotional Intelligence, Emotional Interaction,

Presence, Public Speaking Anxiety, Mental Health.

Abstract:

Virtual agents can be powerful elements in virtual reality (VR) applications, as their inﬂuence on user expe-

rience is governed by complex social mechanisms. Public speaking offers a relatively high-stakes situation

that involves interaction with virtual agents. We examined the effects of an ingroup versus outgroup virtual

audience on public speaking anxiety (PSA). Additionally, we looked at how emotional intelligence and VR

ecological validity modiﬁed these effects. Results indicated that the VR application succeeded in evoking

ingroup bias, and that in the ingroup condition, self-reported PSA was related to general PSA. Emotional

intelligence was also a signiﬁcant moderator. Additionally, audience type inﬂuenced the level of presence

experienced by the user: ingroup audiences result in a higher level of presence. This study identiﬁes potential

areas of interest for future research, approaches that could inﬂuence users in speciﬁc and measurable ways in

applications involving virtual social interaction, as well as the personalization of these virtual experiences.

1 INTRODUCTION

Virtual reality (VR) applications provide a rich en-

vironment for users with many different possibilities

for sensory experiences and interactive elements. The

design of the virtual agents that might inhabit these

spaces is one of the elements that can be used to in-

ﬂuence the player in complex yet measurable ways.

Since virtual humanoid characters are experienced by

the user on a social level, their interaction is governed

by higher-level social mechanisms and the personal-

ity characteristics of the user (Hamilton et al., 2016;

Daher et al., 2017). There are therefore many dimen-

sions to consider when designing virtual characters.

One potentially interesting dimension is that of in-

group/outgroup distinctions.

Deﬁning other people as ingroup or outgroup

(whether they belong to the same social group as the

person or not) is a common way to code and simplify

social interactions. It inﬂuences how others are per-

ceived and judged, and how they are behaved towards.

This often manifests in the form of a preference for,

or a more positive attitude towards, the ingroup mem-

bers, which is also known as ingroup bias (Paladino

and Castelli, 2008). Conversely, perception and be-

haviour tend to be more negative towards outgroup

members.

Judging whether someone is in the in- or outgroup

is an immediate and subconscious process based on

automatic stereotyping (Wittenbrink et al., 1997).

Grouping methods can intersect - for instance, some-

one can be in your ingroup with regards to race, but

in your outgroup with regards to gender. An indi-

vidual’s strength of group identiﬁcation determines

how strongly they experience the effects. The way

the behaviour of the other is perceived is affected

by their group status. For instance, people are gen-

erally more sensitive to criticism when it is made

by outgroup members as opposed to ingroup mem-

bers (Elder et al., 2005). In virtual reality, the in-

group/outgroup status of a virtual crowd can inﬂu-

ence presence (Kyriakou and Chrysanthou, 2018),

and other studies also show that ingroup bias extends

to virtual avatars (Guadagno et al., 2007). It should be

noted that ingroup/outgroup distinctions are a com-

mon narrative device, which is also known to affect

a VR experience (Troxler et al., 2018). Obviously,

an individual’s susceptibility to ingroup bias is depen-

dent on their actual identiﬁcation with the group, and

might be affected by individual characteristics, such

as their level of emotional intelligence.

Emotional intelligence (EI) is not a single trait or

ability, but can be deﬁned as the capacity to recog-

nize the meaning of emotions, to understand their in-

Biesmans, L., van Hees, P., Rombout, L., Alimardani, M. and Fukuda, E.

The Effects of Ingroup Bias on Public Speaking Anxiety in Virtual Reality.

DOI: 10.5220/0008951600170024

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 2: HUCAPP, pages

17-24

ISBN: 978-989-758-402-2; ISSN: 2184-4321

formation, and to manage them (Salovey and Sluyter,

1997). As expected, EI highly inﬂuences social inter-

action. People with low levels of EI generally expe-

rience more anxiety in social situations (Summerfeldt

et al., 2006; Jacobs et al., 2008) whereas high lev-

els correspond to lower communication anxiety (De-

waele et al., 2008). However, there is a gap in knowl-

edge on how an individual’s level of EI inﬂuences

their interaction with virtual agents, and whether this

is comparable to their interaction with real people.

We do know that people have varying reactions to vir-

tual characters and some people are quicker to accept

and trust avatars than others (Vinayagamoorthy et al.,

2006), which might be partly caused by characteris-

tics such as EI.

A quite common way of exploring the effects of

virtual character design is through the use of virtual

audiences in public speaking situations. This context

provides an interaction with a large group of people

in a relatively high-stakes situation. Virtual audiences

consisting of avatars that display clear human char-

acteristics are regarded as more realistic, competent,

credible, and attractive (Nowak and Rauh, 2005), and

strengthen the bond the VR user feels with the audi-

ence (Nowak et al., 2009). One study (Slater et al.,

1999) focused on how an outwardly positive or neg-

ative virtual audience affected public speaking anxi-

ety (PSA). They found that, similar to real life audi-

ences, positive virtual audience interest reduced PSA.

Additionally, they found that co-presence (the sense

of being in the same space as the virtual audience)

ampliﬁed this effect. A second study found that vir-

tual audiences with an outwardly neutral or positive

emotional state caused PSA to remain the same, while

those with a negative emotional state increased PSA

(Pertaub et al., 2002).

PSA is one of the most common social pho-

bias, estimated to affect around 20 to 34% of peo-

ple (Botella et al., 2010; Pull, 2012). People suffer-

ing from PSA experience physiological arousal such

as an increased heart rate or sweating, focus nega-

tively on how they come across, and show behav-

ioral concomitants like shaking or speaking gibberish

whenever they have to speak in front of an audience

(Bodie, 2010). PSA is usually treated with cognitive

behaviour therapy including exposure (Botella et al.,

2010). Exposure used to be either real-life or imag-

ined public speaking (Wallach et al., 2009), but is cur-

rently often addressed by VR mental health applica-

tions (Pull, 2012). Exposure therapy with VR can be

equally effective in reducing PSA as real-life expo-

sure (Wallach et al., 2009) and has long-term effects

(Anderson et al., 2005). VR hard- and software does

not need to be of the highest quality, and the exposure

does not need to happen in a therapy setting for it to

be effective (Lindner et al., 2019; Stupar-Rutenfrans

et al., 2017). Both self-reported PSA and heart rate

measurements reﬂect a reduction in PSA when VR is

used for exposure (Harris et al., 2002).

Because the public speaking experience provides

such a highly salient social interaction, with proven

effects of virtual audience manipulations on the level

of PSA, it seems an ideal setting to explore the in-

ﬂuence of other social mechanisms, like ingroup bias

and EI. Therefore, this study aimed to investigate

whether an ingroup vs. an outgroup virtual audience

can inﬂuence the amount of public speaking anxiety

that users experience. Since there are indications in

the literature that the behaviour of ingroup members is

immediately and automatically perceived more posi-

tively than the behaviour of outgroup members (Elder

et al., 2005), and more positive audiences are less anx-

iety inducing (Slater et al., 1999), we hypothesized

that participants would experience less public speak-

ing anxiety in front of the ingroup virtual audience

compared to the outgroup audience.

Additionally, we were interested in the effects of

an individual’s personal traits, such as EI and gen-

eral predisposition towards public speaking anxiety,

on the level of speciﬁc experienced PSA in the above

ingroup/outgroup scenario. Since previous research

shows that low levels of EI correspond to high levels

of social anxiety (Dewaele et al., 2008; Summerfeldt

et al., 2006), we expected that participants with low

levels of EI would generally experience more public

speaking anxiety than those with high levels of EI,

and that the level of EI could help explain the poten-

tial PSA difference between conditions.

Finally, as the comparison between the two con-

ditions were done in a virtual environment, we spec-

ulated that the ecological validity of the VR simula-

tion could inﬂuence the level of PSA that users expe-

rienced in each condition. This includes the level of

presence that participants felt in the virtual room, and

their actual ingroup/outgroup identiﬁcation. In this

context, we deﬁne ecological validity as a measure

of how effective the virtual environment is in terms

of making the participants feel like they are in a real

space, interacting with real people.

To summarize, three research questions (RQ) were

formulated:

RQ1: What is the effect of an ingroup vs. an out-

group virtual audience on the amount of PSA that

users experience during a virtual public speaking

simulation?

RQ2: How do individual traits such as emotional

intelligence and general predisposition towards pub-

lic speaking anxiety regulate the experienced PSA

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

level in each condition?

RQ3: How does VR ecological validity, including

presence and actual ingroup identiﬁcation of the par-

ticipants, regulate the experienced PSA level in each

condition?

2 METHODS

2.1 Participants and Study Design

Forty university students (23 female) were recruited

using convenience sampling. They were all native

Dutch speakers and aged between 18 and 28 (M =

22.55, SD = 2.43). The participants were quite com-

fortable speaking English (M = 4.93, SD = 1.51 on

a 7-point Likert scale). They had a medium experi-

ence with VR technologies (M = 3.75, SD = 1.81 on

a 7-point Likert scale). No pre-selection was made on

general PSA levels or presence of social anxiety. The

study was approved by the research ethics and data

management committee of Tilburg University.

We used a between-subjects design. Participants

were randomly assigned to one of the two condi-

tions: Ingroup (N = 20) or Outgroup (N = 20). In

the Ingroup condition, participants were told to give

a 2-minute presentation in English about their current

study for a group of freshman students, who wanted

to know what they could expect. In the Outgroup con-

dition, participants were also told to give a 2-minute

presentation in English about their current study, but

here it would be for a group of professors.

2.2 Materials

The virtual environment was created using Unity 3D.

It consisted of a simple room with a wooden ﬂoor

and white walls, with one big window through which

a static landscape view of a small city was visible.

Nine chairs were placed in a three by three arrange-

ment in front of the participant point of view. The

virtual audience was placed on these chairs. The vir-

tual avatars were created using the MakeHuman soft-

ware, and animated with basic breathing movements

from the Mixamo repository. Some general ambient

sounds, such as someone shifting in their chair, were

also added to the virtual environment.

The only difference between the conditions was

the physical appearance of the virtual audience. In the

Ingroup condition the avatars were physically smaller,

and they wore bright colours and informal clothes.

The avatars in the outgroup condition were bigger and

wore toned down colours and formal clothes (see ﬁg-

ure 1).

(a) The Ingroup condition

(b) The Outgroup condition

Figure 1: The virtual characters.

The environment was presented to the partici-

pants through the Oculus Rift head-mounted display

(HMD), tracking only the head movements. Thus par-

ticipants were able to look around the room but unable

to further interact physically with objects in the envi-

ronment. Furthermore, a Grove ear-clip optic pulse

sensor by Seeedstudio was used to measure the heart

rate of the participants.

2.3 Measurements

2.3.1 Public Speaking Anxiety Scale

To measure subjective PSA, the Public Speaking

Anxiety Scale (PSAS) (Bartholomay and Houlihan,

2016) was used, consisting of 17 items on a 7 point

Likert scale. Before the VR experience, the partic-

ipants’ general predisposition towards PSA (general

PSA) was measured with the PSAS. After the pre-

sentation in VR, state PSA during the experience was

measured using the altered version of the PSAS ques-

tionnaire speciﬁcally targeted towards the recent pub-

lic speaking experience (state PSA) - so instead of

’I am conﬁdent when I give a speech’ the statement

reads ’I was conﬁdent when I gave the speech’.

2.3.2 Heart Rate

For an objective measure of PSA, mean heart rate

(HR) and heart rate variability (HRV) were collected,

both as a baseline and during the public speaking task.

These are both indicators of stress (Kim et al., 2018)

and complement each other (Kusserow et al., 2008).

HR is a general measure of arousal, whereas HRV

is linked to anxiety and emotional strain speciﬁcally.

The Effects of Ingroup Bias on Public Speaking Anxiety in Virtual Reality

Although both measures can of course respond to the

general VR experience also (especially in the case of

a novelty effect), we only consider the differences be-

tween conditions and not the raw values. Both HR

and HRV were derived from the normal-to-normal in-

terval of consecutive heart beats, with the variance

of the inter beat intervals (functionally, the SDNN)

used as the measure for HRV. To ensure that HR and

HRV were obtained from a stable signal, the ﬁrst and

last ten heartbeats of each recording were excluded

from the analysis. Skin conductance was considered

as a similar measurement but proved less reliable and

more restrictive in the pilots.

2.3.3 Emotional Intelligence

For this study we focused on trait EI, referring

to emotion-related trait and self-perceived abilities

(Petrides and Furnham, 2006). To measure trait EI,

the Trait Emotional Intelligence Questionnaire - Short

Form (Petrides, 2009) was used. This questionnaire

consists of 30 items on a 7 point Likert scale, giving

a global trait EI score.

2.3.4 Ingroup Identiﬁcation and Presence

As a measure of the effectiveness of changing the VR

avatars for the Ingroup/Outgroup manipulation, the

actual ingroup identiﬁcation was measured with the

Group Identiﬁcation Scale (GIS) (Sani et al., 2015),

which consists of 4 items on a 7 point Likert scale.

Additionally, the short version of the Presence Ques-

tionnaire by Witmer and Singer (Witmer and Singer,

1998) was used to control for the level of presence ex-

perienced in the VR environment. This version con-

sists of 6 items on a 7 point Likert scale.

2.4 Procedure

The experiments were conducted in a neutral ofﬁce

room. Participants were welcomed and read the in-

formation letter. After signing the informed consent,

they ﬁlled in the pretest questionnaire (demograph-

ics, English proﬁciency and VR experience questions,

general PSA, and EI). A baseline heart rate recording

was made for one minute while the participant sat still

and looked at a blank wall.

Then participants put on the HMD and got ac-

customed to the hardware and VR by experiencing

the virtual room without the audience in it for one

minute. They then took off the HMD and were in-

structed about the task. They were told that the exper-

imenter would leave the room and that the presenta-

tion would not be recorded. Following that, the par-

Figure 2: State PSA, GIS and Presence per condition.

Figure 3: HR and HRV increase per condition.

ticipants put on their HMD again and the heart rate

sensor started recording the speaking session.

After two minutes had passed, the experimenter

re-entered the room and announced that the partic-

ipant could stop talking and take off the HMD and

heart rate sensor. The participants then ﬁlled out

the post-test questionnaire (state PSA, GIS, and pres-

ence). Finally, the participants were thanked for their

participation and the experiment was ended.

3 RESULTS

3.1 Public Speaking Anxiety

3.1.1 State PSA

The post-test PSAS (to measure state PSA during

the VR experience) had good scale reliability (Cron-

bach’s α = 0.88 ). As shown in Figure 2, participants

in the Ingroup condition (M = 2.39, SD = 0.87) re-

ported slightly less PSA during the speech task than

participants in the Outgroup condition (M = 2.72, SD

= 0.74). The Levene’s test showed that the variances

in the two groups were homogeneous, F(1,19) = 0.71,

p = 0.46. The difference between the two conditions

was not signiﬁcant, Mdif = 0.32, t(38) = 1.25, p =

0.22.

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

3.1.2 Heart Rate

Due to malfunctioning of the heart rate sensor (>15%

of measurements compromised in either baseline

or speaking measurement), eight participants (1 In-

group, 7 Outgroup) were removed from the analy-

sis of HR data. There was a signiﬁcant general in-

crease in mean HR from the baseline to the public

speaking phase for all participants, regardless of con-

dition (Mdif = -17.41, t(31) = -6.38, p < 0.001). For

each participant, the difference between the baseline

and the speaking HR and the difference between the

baseline HRV and speaking HRV were calculated and

used for further statistical testing.

Participants in the Outgroup condition (M = 19.55,

SD = 18.10) had a higher mean HR increase than par-

ticipants in the Ingroup condition (M = 15.94, SD =

13.65), as can be seen in Figure 3. The variances in

the two groups were homogeneous, F(1,12) = 1.76,

p = 0.27. The difference between the two conditions

was not signiﬁcant, Mdif = 3.61, t(30) = 0.64, p =

0.52.

Participants in the Ingroup condition (M = 358.11,

SD = 2445.14) had a lower increase of HRV during

the speaking task than participants in the Outgroup

condition (M = 637.71, SD = 1891.60), as can be seen

in Figure 3. The variances in the two groups were ho-

mogeneous, F(1,12) = 0.60, p = 0.37. The difference

between the two conditions was not signiﬁcant, Mdif

= 279.60, t(30) = 0.35, p = 0.73.

3.2 Individual Traits

3.2.1 General PSA

The pre-test PSAS (to measure general PSA) had

good scale reliability (Cronbach’s α = 0.94). Partici-

pants in the Ingroup condition (M = 3.42, SD = 1.05)

reported slightly less general PSA than participants in

the Outgroup condition (M = 3.84, SD = 0.81), but

this difference was not signiﬁcant. Because the level

of general PSA differed greatly (M = 3.63, SD = 1.33)

within all participants and cutoff scores are generally

used to categorize PSA (Bartholomay and Houlihan,

2016), we ﬁrst split them into two groups, one with

low general PSA and one with high general PSA, with

a cutoff point of 4. The low general PSA group (10

Ingroup condition, 9 Outgroup condition) had an av-

erage of 3.05 (SD = 0.76), and the high general PSA

group had an average of 4.51 (SD = 0.31).

On average, participants with low general PSA

also experienced less state PSA during the experi-

ment (M = 2.17, SD = 0.63) than participants with

high general PSA (M = 3.13, SD = 0.72). Levene’s

test showed that the variances in the two groups were

homogeneous, F(1,36) = 1.04, p = 0.39. The differ-

ence in state PSA between the two groups was signif-

icant (two-way ANOVA, controlling for condition),

F(1,36) = 18.69, p < 0.001, with a large-sized effect

of d = 1.4. However, there was no signiﬁcant interac-

tion effect with the Ingroup/Outgroup conditions.

3.2.2 Regression Model - EI & General PSA

The EI demonstrated good internal consistency relia-

bility (Cronbach’s α = .88). Participants in the Out-

group condition (M = 5.01, SD = 0.58) reported a

slightly lower mean than those in the Ingroup con-

dition (M = 5.26, SD = 0.66).

A moderated regression analysis was performed to

examine further whether the effects of EI and general

PSA (non-categorized values) on state PSA differed

by condition. The model summary for the regression

analysis was as follows: F(5, 34) = 9.84, p < .001,

= .59. Condition signiﬁcantly moderated both the

effect of EI on state PSA and the effect of general PSA

on state PSA (see Table 1).

A post-hoc simple slopes analysis found a signif-

icant effect of EI on state PSA, after controlling for

general PSA, in the Outgroup condition (β = -.90, SE

= .30, t = -3.04, p = .005) but not in the Ingroup condi-

tion (β = .08, SE = .26, t = .30, p = .76). Conversely,

the simple slope of general PSA on state PSA, after

controlling for EI, was signiﬁcant in the Ingroup con-

dition (β = .68, SE = .16, t = 4.21, p < .001) but not

in the Outgroup condition (β = .10, SE = .21, t = .48,

p = .64).

Table 1: Moderated regression model - the effect of individ-

ual traits EI and general PSA on state PSA.

β SE t p

gPSA .10 .21 .48 .637

Condition -.11 .19 -.58 .566

EI -.90 .30 -3.04 .005

EI*Condition .98 .40 2.47 .019

gPSA*Condition .58 .26 2.21 .034

3.3 VR Ecological Validity

3.3.1 GIS

The GIS had good scale reliability (Cronbach’s α =

0.91 ). As shown in Figure 2, participants in the

Ingroup condition (M = 3.33, SD = 1.54) identiﬁed

more with their virtual audience than participants in

the Outgroup condition (M = 2.45, SD = 0.94). Lev-

ene’s test showed that the variances in the two groups

were not homogeneous, F(1,19) = 0.37, p = 0.04. The

The Effects of Ingroup Bias on Public Speaking Anxiety in Virtual Reality

difference between the two conditions was signiﬁcant

(independent samples t-test), Mdif = -0.88, t(31.4) =

-2.17 p = 0.04, with a moderate effect size of d = -

0.67.

3.3.2 Presence

On average, the level of presence experienced by the

participants was 5.2 (SD = 1.0). The presence ques-

tionnaire had sufﬁcient scale reliability (Cronbach’s

α = 0.68). As shown in Figure 2, participants in the

Ingroup condition (M = 5.23, SD = 0.68) felt more

present than participants in the Outgroup condition

(M = 4.36, SD = 1.04). Levene’s test showed that

the variances in the two groups were homogeneous,

F(1,19) = 2.36, p = 0.07. The difference between the

two conditions was signiﬁcant (independent samples

t-test), Mdif = -0.87, t(38) = -3.3, p = 0.002, with a

large-sized effect, d = -1.05.

3.3.3 Regression Model - GIS & Presence

A moderated regression analysis was performed to ex-

amine whether the effect of participants’ level of GIS

and presence on state PSA differed by condition. The

model summary for the regression analysis was as fol-

lows: F(5, 34) = 1.22, p = .32, R

= .85. Neither the

GIS nor the presence effects were signiﬁcantly mod-

erated by condition (see Table 2). The model was re-

run without the interaction terms to allow estimation

of the partial effects of GIS and presence. Reducing

the model did not signiﬁcantly decrease the propor-

tion of explained variation (∆R

= .02, F(2, 36) =

4.34, p = .65), but neither the partial effect of GIS nor

the partial effect of presence were statistically signiﬁ-

cant (GIS: β = .20, SE = .12, t = 1.75, p = .089; Pres-

ence: β = -.19, SE = .17, t = -1.10, p = .28).

Table 2: Moderated regression model - the effect of VR

ecological validity GIS and presence on state PSA.

β SE t p

GIS .04 .25 .14 .888

Presence -.05 .23 -.21 .837

Condition -.33 .32 -1.05 .299

GIS*Condition .22 .28 .77 .447

Presence*Condition -.30 .38 -.80 .430

4 DISCUSSION

This study examined the effect of ingroup bias on

the level of public speaking anxiety that users expe-

rienced in VR, and how their personal characteristics

and the VR ecological validity regulated that experi-

ence. Our results showed that the Ingroup/Outgroup

conditions did not signiﬁcantly affect the level of PSA

that participants experienced during the public speak-

ing task, answering RQ1 in the negative. The eco-

logical validity did not seem to inﬂuence PSA either

(RQ3). However, there were signiﬁcant effects of per-

sonal traits such as emotional intelligence and general

public speaking anxiety (RQ2).

There was an interaction between EI and condi-

tion, where a higher EI predicted a lower state PSA

score, speciﬁcally in the Outgroup condition. This re-

sult suggests that EI had a modulating effect on state

PSA when presenting to an outgroup audience, but

not when presenting to an ingroup audience. It could

be interesting to explore this effect further in future

research, to see whether a high emotional intelligence

allows a person to be more comfortable with virtual

characters that they do not necessarily identify with.

If this is the case, virtual public speaking training

could be improved by adding emotional intelligence

training as well.

Not unexpectedly, state PSA was signiﬁcantly af-

fected by general PSA. This means that participants

who experience more anxiety towards public speak-

ing in general were also more anxious during this

speciﬁc virtual public speaking experience, regardless

of the condition. The regression showed that there

was also an interaction effect with condition overall,

meaning that general PSA predicted state PSA in the

Ingroup condition. The more predisposed someone is

to public speaking anxiety, the more anxious they are

when they have to present in front of an audience that

they consider a part of their ingroup, whereas gen-

eral PSA was not signiﬁcantly related to state anxiety

when presenting to outgroup members.

Besides the main research questions, a few other

noteworthy interactions were found. A signiﬁcant in-

crease in heart rate was observed overall. This could

be caused by the novelty effect of the VR technology,

as many participants did not have a lot of previous

experience with VR. The 1-minute acclimatization to

the VR environment might not have been enough to

overcome this. If this is the case, the novelty effect

might have occluded any smaller effects of the con-

ditions, and it could be interesting to repeat the ex-

periment with more experienced participants or over

a longer period of time.

Participants in the Ingroup condition identiﬁed

signiﬁcantly more with the virtual audience than par-

ticipants in the Outgroup condition. This is exactly as

intended, and indicates that it is likely that a type of

ingroup bias was triggered by the exposure to these

different audiences. Unexpectedly, the audience type

had a signiﬁcant effect on presence, where partici-

pants in the Ingroup condition felt more present in

HUCAPP 2020 - 4th International Conference on Human Computer Interaction Theory and Applications

the virtual environment than those in the Outgroup

condition. This warrants further research, as it could

potentially be utilized in a wide variety of virtual ap-

plications to improve the user interaction. Although

it of course already makes sense from a narrative de-

sign point of view to improve user identiﬁcation with

the virtual characters they encounter, this result shows

that it might have measurable and far-reaching effects

on how the medium itself is experienced.

The ingroup audience does not seem to lead to

enough of a positive association with the virtual char-

acters to signiﬁcantly affect PSA. It is however not

unlikely that the increased presence and group identi-

ﬁcation have effects on other factors, which could be

explored in future research. As several studies have

shown, the valence of audience feedback affects the

user (Slater et al., 1999; Pertaub et al., 2002). Interac-

tion effects in this regard might be a worthwhile area

of study, since positive or negative audience feedback

could be perceived differently depending on whether

the virtual audience is a part of the participant’s in-

group or outgroup (Elder et al., 2005).

It should be noted that, as increased identiﬁcation

with the ingroup audience was established not only

through the use of visuals, but also through means

of a narrative (ﬁrst-year students who want to know

what to expect from their studies), it is possible that

this by itself explains the increased level of presence

(Troxler et al., 2018). As previous research shows that

co-presence in particular can amplify positive effects

of virtual audiences (Pertaub et al., 2002), it might be

interesting for future research to look at co-presence

speciﬁcally. Co-presence could also be induced more

strongly with several techniques, such as social prim-

ing (Daher et al., 2017).

One of the limitations of this study is that we

only looked at one type of in- and outgroup, namely

students and professors. As is often the case, these

groups hold a clear power differential in regard to how

they relate to the participant. This could have inﬂu-

enced the results. Different types of groupings (for

instance on gender, race or social class) could poten-

tially lead to different effects. Additionally, although

many VR studies currently use similar or even smaller

sample sizes to the one used here, in-depth analyses

of the interaction effects require a more robust sample

size which could lead to deeper insights in the com-

plex mechanisms behind social VR experiences.

As the technology and its applications advance,

VR involves more of the interacting variables that are

also present in real life, especially on a social level

(Hamilton et al., 2016). Uncovering the social pro-

cesses that govern the user interaction with the vir-

tual world allows us to design more effective VR ap-

plications. This study has shown that while ingroup

versus outgroup virtual audiences can affect group

identiﬁcation, which can signiﬁcantly inﬂuence pres-

ence, this does not have a signiﬁcant inﬂuence on the

level of PSA that is experienced by users while prac-

tising public speaking in VR. It does however affect

the relationship between general and state PSA. Fur-

thermore, the effects on state PSA are moderated by

the individual characteristics (EI) of the player, poten-

tially allowing for personalization of the experience.

The overall results of this study indicate that ingroup

bias can be evoked in virtual environments and that

it could be an interesting method of inﬂuencing the

feelings and behaviour of users in speciﬁc ways. Al-

though this study focused on PSA, the effects of in-

group bias in VR could potentially be extended to-

wards any VR application that involves social interac-

tions, and might prove useful in a variety of contexts.

ACKNOWLEDGEMENTS

The authors would like to thank Lisa Neve for her

technical assistance with the creation of the virtual en-

vironment used in this study.

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