# Emotional Avatars: The Interplay between Affect and Ownership of a Virtual
Body

Aske Mottelson
Department of Psychology
University of Copenhagen
Copenhagen, Denmark
<EMAIL_ADDRESS>
Kasper Hornbæk
Department of Computer Science
University of Copenhagen
Copenhagen, Denmark
<EMAIL_ADDRESS>

###### Abstract

Human bodies influence the owners’ affect through posture, facial expressions,
and movement. It remains unclear whether similar links between virtual bodies
and affect exist. Such links could present design opportunities for virtual
environments and advance our understanding of fundamental concepts of embodied
VR.

An initial outside-the-lab between-subjects study using commodity equipment
presented 207 participants with seven avatar manipulations, related to
posture, facial expression, and speed. We conducted a lab-based between-
subjects study using high-end VR equipment with 41 subjects to clarify
affect’s impact on body ownership.

The results show that some avatar manipulations can subtly influence affect.
Study I found that facial manipulations emerged as most effective in this
regard, particularly for positive affect. Also, body ownership showed a
moderating influence on affect: in Study I body ownership varied with valence
but not with arousal, and Study II showed body ownership to vary with positive
but not with negative affect.

_K_ eywords Virtual Reality, Affect, Emotions, Body Ownership, Avatar

##  1 Introduction

The theory of embodied cognition suggests that _cognitive processes are deeply
rooted in the body’s interactions with the world_ [1], because of a relation
between bodily expression of emotion and the way in which emotional
information is attended to [2, 3]. Here, we investigate the extent to which
affective embodiment can be observed with illusory ownership of a virtual
body. Will a smiling virtual self cause joy and a frowning one prompt sadness?
We examine the causal relationship between virtually inducing embodied affect
and experiencing the relevant affective states.

Affect has been shown to be of great importance for many aspects of day-to-day
life, among them cognitive performance, general health and well-being,
creativity, decision-making processes, and social relationships [4]. Also,
studies have shown that affect may influence perhaps the most fundamental VR
concept: _presence_ , the _sense of being there_ [5].

_Body ownership_ , another key concept in VR, refers to the degree to which a
virtual body is experienced as one’s own body. Since bodies are demonstrably
connected to affect, it seems worthwhile to investigate the relationship
between virtual bodies and affect, and the link between affect and body
ownership. However, no previous studies have explored these relationships.

Through two user studies, this paper investigates the degree to which virtual
bodies can modify affective responses, in addition to uncovering the role of
affect for ownership of a virtual body. In summary, we present these findings
as contributions:

  * •

body-ownership illusions can influence affect (found in a large-scale user
study with VR),

  * •

manipulation of facial features was most effective in influencing affect,

  * •

body ownership is a key component for positive affect,

  * •

body ownership showed to vary with the valence component of SAM, and

  * •

higher positive PANAS responses significantly increased the probability of
high body ownership.

##  2 Overview

###  2.1 What Affect Is

The concepts of emotion, mood, and affect are sometimes mistakenly used
interchangeably. We proceed from a view of affect inspired by Ekkekakis’s
efforts to untangle the conceptualization and measurement of affect [6]. He
argued for distinguishing among three forms of affect: core affect, mood, and
emotion. The most fundamental of these is core affect, underpinning moods and
emotions. This is an evaluative feeling always available to the consciousness.
Pleasure offers a clear example. Emotion, in contrast, depends on appraisal,
and involves an object toward which the emotion is directed. Anger is one
example. Finally, moods are long-term affective states in comparison to
emotions; they are also less intense. Irritation is an apt example of a mood.
In addition to presenting these basic categories, Ekkekakis argued that
specific metrics employed in studies must operate from a particular view of
affect (i.e., the entity composed of core affect, mood, and emotion). In
particular, it must be clear whether the researcher is interested in
dimensional or in categorical measures of affect. Consequently, we later
explain the views of affect for both studies presented in the paper.

Both physiological and subjective measurements are commonly employed for
estimation of affective responses. Especially popular among the latter are the
Positive and Negative Affect Schedule (PANAS) [7] and the Self-Assessment
Manikin (SAM) [8]. The former considers positive and negative affect on
independent scales, producing two summary scores from participants’ rating of
their emotional fit on a five-point scale with 10 positive and 10 negative
words, while the SAM typically presents two scales, valence (pleasantness) and
arousal (activation), using a pictorial manikin for representing the scores.
There are versions with a five-, seven-, and nine-point scale (Figure 1 shows
a nine-point SAM).

Figure 1: The nine-point Self-Assessment Manikin, measuring valence
(unpleasant–pleasant, at the top) and arousal (deactivation–activation, at
bottom).

####  2.1.1 Manipulating Affect

The process of altering affect experimentally is known as affect manipulation.
It is sometimes colloquially referred to as mood induction or emotion
elicitation (although this informal use is undesirable). Affect manipulation
allows researchers to enforce specific affective responses from a sample than
would have arisen if subjects had merely reported on their current affective
state. Hence, this manipulation is an important psychological tool for
understanding how mood, emotion, and core affect modify human cognition and
behavior within the constraints of experimental scrutiny.

There are several ways of manipulating affect (Westermann et al. give an
overview [9]). Among the most commonly employed are film, IAPS, and Velten.
These respectively involve showing affective movie clips, imagery, or asking
participants to immerse themselves in an emotional story.

###  2.2 Virtual Reality As an Affective Medium

Research covering virtual reality and affect is surprisingly scarce,
notwithstanding the general consensus on affect’s highly important influence
on cognition and behavior, and the recent proliferation of behavioral research
using virtual reality. That said, some scholars have attempted to manipulate
affect via virtual environments [10, 11, 12, 13, 14]. We review that work
below.

Baños et al. [10] created a virtual environment capable of eliciting four
discrete emotions. The environments depicted alterations of a city park that
incorporated a multitude of classical emotion-elicitation procedures, such as
Velten, IAPS, and movie clips. The full procedure, lasting 30 minutes, showed
effective for discrete emotion elicitation.

Another emotion-eliciting virtual park was created by Felnhofer et al. [11].
Five park scenarios were designed, each eliciting a specific emotion: joy,
anger, boredom, anxiety, or sadness. The scenarios differed in lighting,
coloring, and sound, along with their plant and animal types. The authors
concluded that virtual environments can be used to induce emotional states.

Riva et al. [13] constructed their own park scenarios to elicit emotions in
VR. Their three parks shared the same structure but differed in their aural
and visual experience. These researchers too confirmed the efficacy of VR as
an affective medium.

To our knowledge, only Jun et al. [14] have attempted to alter affect via
avatar manipulations. They found that facial expressions of a virtual avatar
can modulate emotions and that greater presence is associated with higher
valence.

###  2.3 Body-Ownership Illusions

Body-ownership illusions are a class of experiment in which the participants
are led to believe that they are owners of another body, or part thereof [15].
These illusions are persuasive when the replacement bodies are virtual avatars
made possible by VR [16]. An optimal body-ownership illusion induces a high
level of ownership of the virtual body, usually by means of visuo-motor
synchrony between the physical and virtual body, sometimes alongside tactile
feedback [17].

While there is ongoing scholarly debate on how far avatar manipulations can be
extended without distortions to body ownership, evidence thus far suggests
that ownership can be maintained even with substantial alterations to the
avatar, such as scaling the arms to three times the normal length [18],
rotating the body 15 degrees [19], or using a child’s body for the avatar
[20].

A large body of research in this area is focused on manipulating avatars to
study whether humans’ perception of the world can be influenced through body
alteration/replacement. For instance, owning a child avatar influences
estimates of objects’ size and expedites association of the self with
childlike attributes [20]; similarly, owning a different-skin-toned avatar
affects implicit racial bias [21].

The aforementioned body of work confirms the feasibility of changing body-
oriented perceptions when participants are manipulated to believe the virtual
body they see in VR is, in fact, their real body.

###  2.4 Limitations of Previous Work

While previous studies attest that VR scenarios can alter affect, this has
been confined largely to park scenarios. The conditions involved are time-
consuming, explicit in their purpose, and geared for a specific narrative.
Most importantly, they also neglect embodiment as a part of the immersion.
This renders it hard to evaluate the processes leading to affect manipulation
in embodied VR precisely. While some evidence suggests that one can influence
affect and presence by manipulating the avatar’s facial features [14], the
connection between affect and the illusion of owning a virtual body remains
unclear, as does whether other avatar manipulations may inform affect. Some
authors have shed light on affect’s role for presence [22]: the literature
suggests a positive correlation between presence and affect.

We provide evidence that affect can be influenced by only bodies truly thought
to be ours. Additionally, we present a first study showing interplay between
affect and body ownership. Our findings confirm the existence of a link
between (positive) affect and virtual bodies.

(a)

(d)

(e)

(f)

(g)

Figure 2: Experimental manipulations for Study I, as described in Table 1.
Manipulations (b) and (c) used the same body as the control condition (a),
with changes to speed of navigation.

##  3 Study I

We conducted an outside-the-lab VR study, as research has proposed this to be
a valid method for low-cost VR user studies with high power [23, 24]. Each
participant was given a commodity cardboard VR headset to use in combination
with his or her smartphone. This was deemed a cost-effective approach enabling
a large-scale user study that would allow us to try many experimental
conditions and, thereby, many avatar manipulations.

The overall purpose of this study was to i) validate the feasibility of
influencing affect with avatar manipulations in VR, ii) experiment with
several avatar manipulations with high power, and iii) identify specific
questions pertaining to the body-ownership–affect link for further
investigation via a laboratory study focusing on high internal validity.

With Study I, we were interested in core affect [6]. Since this is the
broadest class of non-reflective affective feelings available to the
consciousness, core affect is simpler to address than the underlying emotions
and moods. Because it was not clear in advance how the manipulations would
influence participants, we decided on a dimension-based conceptualization of
core affect, involving valence and arousal (similar to Russell’s circumplex
model of affect [6]).

###  3.1 Participants

In all, 207 undergraduate computer science students took part in the study.
Most were male (51 females participated), and the age range was 19–50 ($M=23,\
SD=3.8$). Smartphones with iOS were slightly more common than Android ones
among the participants, with 110 using iOS in the study. Participation was
counted toward the students’ credit for a compulsory introductory HCI course.

###  3.2 Apparatus

The participants used their smartphones within a Google cardboard VR headset
that had a head strap. A few students did not have a compatible device, so we
lent them one. The application was developed with the Google VR SDK for Unity
2017, deployed for Android and iOS and distributed via the relevant app
stores. We employed an inverse kinematics (IK) system for the humanoid avatar,
using the head as the only tracked point, with three degrees of freedom (DoF).

###  3.3 Design

We used three tasks in the design of our between-subjects experiment. The
independent variable was body manipulation, with seven conditions (see Table 1
and Figure 2): control, speed (slow/fast), face (smile/frown), and posture
(upright/hunched)

These specific avatar manipulations were chosen based on a review of
body–affect relations. Coombes et al. [25], for instance, show that speed of a
task varies as a function of affect, and Wallbott [26] presents an overview of
how postures are related to emotion. Finally, research has showed how facial
expressions can modulate affect [27, 2, 14].

The tasks were adapted from pen-and-paper ones in Strack et al.’s work [27] to
suit an embodied VR experience. In all three tasks, the subject selected
spheres in turn, on the basis of the number or letter printed on them. Each
sphere was initially white, turned red when the subject looked at it, and then
turned blue once the subject had been dwelling on it for two seconds. While
participants were performing the tasks, a large mirror was in front of them,
showing them their avatar (see Figure 3). Between tasks, the mirror served as
a screen displaying buttons with which participants answered questions, again
via two-second dwell times. The specifics of the tasks are described below.

####  3.3.1 Task I

Task I was designed to give familiarity with selecting targets by dwelling on
them via head position and to introduce Likert-scale use in VR. Participants
selected two spheres, numbered 1 and 2, by dwelling on each in turn.

####  3.3.2 Task II

In the second task too, the participants selected numbered spheres, this time
from 1 to 9.

####  3.3.3 Task III

For the final task, they were asked to select all spheres with vowels (“Y” was
optional) on them, in any order. A sphere was present for each letter of the
alphabet.

Table 1: The experimental manipulations in Study I (the hypotheses correspond to SAM responses). | Variable | Manipulation | Hypothesis
---|---|---|---
(a) | Control | None | Baseline
(b) | Rotation speed | Slow (-40%) | High affect
(c) | Rotation speed | Fast (+40%) | Low affect
(d) | Face | Smile | High affect
(e) | Face | Frown | Low affect
(f) | Posture | Upright | High affect
(g) | Posture | Hunched | Low affect

In the control condition (a), there were no manipulations. For the speed
conditions, we increased/reduced 40% rotational gain around the $y$-axis, such
that a 100-degree movement would result in rotation of 140 degrees for (b) and
60 degrees for (c). The face conditions manipulated facial features (using
built-in morphing), to produce smiling (d) and frowning (e). Postural
manipulations were implemented by adding artificial head and chest targets for
the IK model, resulting in either upright (f) or hunched (g) posture.

###  3.4 Measurements

The primary dependent variables were valence and arousal, measured with a
nine-point SAM (see Figure 1) [8] administered with the textual prompt “How do
you feel?” (Table 2 provides the full list of dependent variables). We chose
the SAM to measure the primary dependent variables because it requires few of
the cumbersome in-VR button selections, which would be prevalent using common
affect questionnaires (e.g., PANAS [7]). The dominance scale sometimes used as
a third dimension for the SAM instrument is more emotion- than core-affect-
oriented [6] so was not used in our study.

Table 2: The dependent variables for Study I. Measurement | Instrument | Category
---|---|---
Valence | SAM | Affect
Arousal | SAM | Affect
Difficulty | Likert scale | Post-task metric
Completion time | Clock | Performance
Error rate | Head orientation | Performance
Mirror time | Head orientation | Activity
Body ownership | Two questions | Questionnaire
VR experience | Four options | Questionnaire

Also, we measured task difficulty (subjective difficulty), speed (task
duration), and accuracy (error rate) for each task. In addition, we recorded
the time spent looking in the virtual mirror, as the number of frames for
which the participant’s point of view collided with the mirror.

###  3.5 Procedure

Participants entered a virtual room (see Figure 3) after installing and
opening the mobile application and placing their phone in the cardboard VR
headset. In this room, a standing sex-matched avatar could be seen in the
mirror. Participants’ coarse body movements were mapped in real time, with
head rotation used alongside IK to create a sense of body ownership over the
virtual character. Each participant was randomly assigned to one of the seven
experimental conditions.

Figure 3: Task II shown from a third-person perspective – the participant is
placed in front of a mirror in a virtual room, and numbered spheres are
selected in turn via dwelling.

Each subject performed, in all, three tasks, in a predefined order. In each
task, the participants looked for floating spheres with predefined locations.
To create the illusion of body ownership, the mirror showed the avatar moving
in visuo-motor synchrony with the participant.

The participants rated the difficulty of each task upon its completion. After
the third task, they filled out a SAM form that used a nine-point Likert scale
created for VR.

####  3.5.1 Debriefing

After completing the VR part of the study, participants completed a brief Web-
based questionnaire, providing data on body ownership and prior VR experience.
We defined their body ownership as the maximum value reported for these two
questions on body ownership used by Bakakou and colleagues [20]):

  * •

How much did you feel that the virtual body you saw when you looked down at
yourself was your own body?

  * •

How much did you feel that the virtual body you saw when you looked at
yourself in the mirror was your own body?

Additionally, we asked the participants to guess the purpose of the study, for
exclusion of anyone who suspected its true purpose (no one did). Each
participant included the unique ID generated by the app for linking the app’s
study log with the post-experiment questionnaire. A week later, we revealed
the purpose of the study to the student participants.

###  3.6 Results

We tested the responses for normality, and a Shapiro–Wilk normality test
showed that body-ownership, valence, and arousal responses did not follow a
normal distribution. Therefore, our reporting of results in the following
section refers to non-parametric statistics. Kruskal–Wallis tests were used,
with $\chi^{2}$ test results reported in this section. For normally
distributed data, $F$-scores from ANOVA tests are given.

####  3.6.1 Data

Most participants had only a little or some prior experience with VR; 21% had
never tried it before, 55% had a few times, and 24% had considerable VR
experience. We excluded two of the 207 participants for taking too long (>30
minutes), and 37 were removed from the sample for not experiencing body
ownership (BO) with the virtual avatar (BO $\leq 2$). Data from 168
participants remained.

The average time for completing the study was 297 s ($SD=197$). No
participants were excluded for guessing the purpose of the study; the vast
majority responded with variations of “I don’t know,” with some speculating
that the aim was to understand usability aspects of VR navigation. A little
less than half of the time, 119 sec. ($SD=37$), was spent looking in the
mirror. The mean difficulty (from 1 to 9) for each respective task was 1.8,
2.9, and 4.4, showing participants’ ease with completing the tasks.

####  3.6.2 Affect

The control group (without avatar manipulations) reported, on average, a score
of 5.3 for valence and 3.3 for arousal. Figure 4 shows this group’s
differences in means for both valence and arousal; it is evident that the
differences in valence between conditions are negligible (with means between
5.0 for frowning and 5.8 for smiling), while differences in arousal are more
pronounced (means range from 3.0 for hunched posture to 4.8 for smiling).

Figure 4: Difference in means from the six conditions, normalized in terms of
the control condition. Blue ellipses represent conditions with hypotheses of
high affect, and red ones represent conditions with hypotheses of low affect.
The ellipses are fitted to represent 95% CIs.

We did find a significant effect of experimental condition on arousal:
$\chi_{6}^{2}=14.61,\ p=.02$. A Bonferroni-adjusted _post hoc_ Dunn’s test
showed that smile and hunched differed significantly, with $z=-3.32,\ p<0.01$.
We found no significant effect of experimental condition on valence:
$\chi_{6}^{2}=2.75,\ p=.84$.

The condition smile showed itself to be the most effective manipulation for
causing positive affect. Non-intuitively, its counterpart, frown, did not
emerge as the most effective contributor to negative affect; rather, it seems
that this and other manipulations hypothesized to induce negative affect had
little to no effect.

We noticed that positive avatar manipulations (smile, slow, and upright) led
to increased arousal relative to the control (see Figure 4). Comparing
positive and negative manipulation reveals significance for arousal:
$\chi^{2}_{1}=7.51,\ p<.01$.

Conversely, we observed negative manipulations (frown, fast, and hunched) to
have limited effect, with hunched being the most effective. We noted that
other researchers too have found positive affect easier to induce than
negative affect; e.g., Schaefer et al. [28] collected affect measurements for
64 movie clips and consistently found stronger induction of positive affect
than creation of negative affect with these stimuli.

####  3.6.3 Body ownership

With regard to body ownership – the degree to which sensory cues coalesce in
the perception that a virtual body is “my body” [16] – we expected to find
consistent levels across all the manipulations. Therefore, we checked for an
effect of experimental condition on level of body ownership. Indeed, we found
no such significance: $\chi_{6}^{2}=6.61,\ p=.36$.

The results from Study I showed that body ownership varied significantly with
the time spent looking in the mirror: $\chi_{8}^{2}=20.8,\ p=.008$. That is,
the level of reported belief in the avatar being the participants’ own body
rose with the amount of time looking at the avatar in the mirror in front of
them. This shows the importance of mirrors in body-ownership illusions, and it
confirms that cardboard VR systems, modest fidelity notwithstanding, can
induce those illusions for most participants (as suggested in earlier work
[24, 23]).

A Kruskal–Wallis test showed that valence varied significantly with body
ownership: $\chi_{7}^{2}=21.3,\ p<.01$. A Spearman’s $\rho=.29$ showed that
body ownership correlates somewhat with valence; the same was not true for
body ownership and arousal, $\rho=.08$ (see Figure 5).

Figure 5: Plots showing the relationship of body ownership with (a) valence
and (b) arousal, where the size of the dots indicate frequency and the dashed
lines represent the trend lines for positive (blue) and negative (red)
manipulations.

####  3.6.4 Performance

Coombes et al. [25] described a link between task performance
(speed/precision) and affect; specifically, they reported that lower affect
leads to higher speed and error rate in a motor task. Therefore, we were
interested in whether task performance would vary as a function of affect in
this study.

Participants completed the study most quickly in the frown condition ($M=250$
sec.) and most slowly with smile ($M=353$ sec.); however, the speed difference
attributable to avatar manipulation was not significant: $F(6,161)=.61,\
p=.72$.

For error rate, we used the mean error rate over the three tasks, calculated
thus: for each task, we computed the Levenshtein distance,
$lev(\alpha,\beta)$, between the optimal sequence of actions, $\alpha$ (e.g.,
Task II’s $\alpha=$“$123456789$”), and the participant’s sequence of actions,
$\beta$. Error rate was, similarly to speed, not found to vary significantly
with avatar manipulation: $F(6,161)=1.22,\ p=.30$.

####  3.6.5 Summary

The three avatar manipulations hypothesized to induce negative affect (fast,
frown, and hunched) did produce a lower average affect score than each of the
manipulations hypothesized to induce positive affect (slow, smile, and
upright). This effect was statistically significant for arousal when the
positive- and negative-condition groups were compared. Also, an omnibus
Kruskal–Wallis test showed significance for arousal, and a Bonferroni-
corrected _post hoc_ test found the distributions to differ significantly for
the pairing hunched and smile.

Body ownership significantly varied with valence; this was not the case for
arousal. We did not find evidence suggesting that body ownership is influenced
by avatar manipulation.

Neither did we find evidence for speed or error rate being influenced by the
avatars’ manipulation.

The results of Study I suggest that facial manipulations to avatars do alter
affective responses, with posture manipulations having a similar but less
pronounced effect. Manipulating speed does not seem to alter affective
responses. Finally, our findings point to manipulations for positive affect as
more efficient than those intended to induce negative affect.

####  3.6.6 Discussion

The mean score from the body-ownership reports (scale: 1–9) was 4.36
($SD=1.91$). While this is not unusually low, it does suggest that something
in the circumstances of Study I limited the body-ownership scores. We believe
the factors might include i) the commodity VR equipment with only 3 DoF; ii)
the short study duration (five minutes, with two minutes of mirror time); and
iii) the lack of internal control in outside-the-lab experimentation.

While outside-the-lab experimentation allows for rapid implementation of
large-scale user studies at low cost, it imposes design constraints and
creates practical limitations to the experiment. In particular, lengthy
studies work poorly outside a laboratory setting, and adherence to the
protocol (e.g., standing up and using a head strap) is hard to confirm.
Additionally, it is rendered difficult to obtain reliable measurements of
bodily aspects, such as posture and locomotion. We addressed these concerns by
conducting a lab-based study, with higher internal validity and technical
fidelity.

##  4 Study II

The purpose of Study II was to address unresolved questions from Study I about
i) the interplay between body ownership and affect and ii) the effect of
posture changes on affect.

Since valence varied with body ownership in Study I, we wanted Study II to
cast more light on the connection between body ownership and affect, through
measurements with high internal validity. While Study I showed only a non-
significant difference between the hunched and upright conditions in terms of
affect, that study was limited in a number of respects. With Study II, we
hoped to ascertain whether a more elaborate setup and rigorous study protocol
would reveal differences in affective responses between posture changes to the
avatar.

###  4.1 Participants

We had 42 participants in this study, with an age range of 21–34 ($M=26.3,\
SD=3.4$). We recruited people to take part via an internal mailing list. The
first participant was excluded on account of technical errors, so we report on
analysis performed for 41 people (22 of whom were female). All participants
were given a gift worth the equivalent of $20 USD for their time. Participants
signed a consent form before the experiment commenced. No one who took part in
Study I took part in Study II.

###  4.2 Apparatus

For this study, we used an HTC VIVE system (6 DoF) in combination with an
OptiTrack motion-capture system for state-of-the art body-tracking. We
employed a Unity scene similar to that developed for Study I, using a desktop
PC (2.8 GHz Intel i7, 12 GB RAM, NVIDIA GTX 980), running Windows 10 Pro.
Tracking was performed with Motive, using eight Prime 13 cameras at 120 Hz
(the same frame rate used for HTC VIVE lighthouses), positioned in a
semicircle (see Figure 6). We tracked the hands, elbows, feet, chest, and
shoulders. Retroreflective markers were attached to the head-mounted display
for SteamVR–Motive alignment. The chest and shoulders were not attached to the
IK system but were tracked for later analysis.

Figure 6: The view of the participant immersed in the VR world.

###  4.3 Design

Study II employed a between-subjects design with avatar manipulation as the
independent variable (there were three groups, with 14 people in each).
Because the manipulations of avatar posture in Study I exhibited no clear
effect, we were interested in seeing whether this was due to the lack of
technical fidelity (e.g., DoF, latency, and screen-resolution issues) and
experimental control (e.g., issues with supervision, whether subject were
standing up, adherence to procedure, and interruptions). On this basis, these
three conditions were chosen for the independent variable: hunched, a control,
and upright. The last was added halfway through the study without the
experimenter’s knowledge; this was done since the analysis in Study I showed
little difference between the control and the hunched-condition group.

We chose PANAS results for the dependent variable of affect – this measure has
consistently been shown to have high validity [29]. The instrument offers a
broader conceptualization of affect than in Study I, in that it features items
related to emotion and mood, not only core affect [6]. Thereby we hoped to
gain a more nuanced picture of the influence of our manipulations while
retaining a dimensional view of affect. The PANAS instrument was administered
on a computer alongside the textual prompt “Please indicate to what extent you
feel this way right now”.

We chose a task similar to those in Study I, although this one was longer and
required full-body movements instead of only head orientation and dwelling.
Again, a mirror was present, in which the visuo-motor synchronous, sex-matched
avatar was visible. The virtual room where participants were immersed was a
replica of the physical room in which the experiment took place (see Figure
6). The experiment was conducted by someone aware of neither the study’s
purpose nor of Study I.
In summary, in comparison to Study I, Study II had

  * •

higher VR fidelity (HTC VIVE instead of cardboard),

  * •

full-body tracking (8 $\times$ OptiTrack Prime 13),

  * •

longer duration (17 min., as opposed to 5 min.),

  * •

an extensive construct for affect (PANAS, not SAM),

  * •

fewer conditions (three instead of seven), and

  * •

fewer participants (42 instead of 207).

###  4.4 Procedure

After calibration in which participants’ bodies were aligned with their
virtual avatar, the study proper began. The study progressed with a series of
floating 3D objects (spheres, cubes, and icosahedra) that disappeared when the
participant tapped them with either hand. The experiment ended once the
participant had tapped 200 objects. All objects were spawned in random
locations between the participant and the mirror, such that the participant
faced in the same direction with respect to the mirror throughout the study
(see Figure 6). Thus, each subject was required to glance around, move about,
and tap objects close to both the floor and the ceiling. This movement was
reflected in the mirror placed in front of the participant. After finishing
the VR task, participants filled out a computer-administered post-experiment
questionnaire in which i) PANAS results, ii) body-ownership data, and iii)
demographic details were collected.

###  4.5 Results

####  4.5.1 Data

Participants used, on average, 17.6 minutes ($SD=2.4$) for the study, with 14
minutes spent looking in the mirror ($SD=1.7$). The median score from the
body-ownership reports (scale: 1–9) was 6 ($IQR=4,7$). Most subjects had
little prior experience with VR: 38% had never tried it before, 38% had tried
it a few times, and 24% had considerable VR experience.

####  4.5.2 Affect

The PANAS instrument covers two components, positive and negative affect,
generating a score between 10 and 50 for each. These are considered two
independent measures of affect. Figure 7 shows the negligible difference in
affective responses between conditions. We were unable to find any significant
effects of avatar manipulations on either PANAS component: for the positive
one, $\chi^{2}_{2}=.25,\ p=.88$; for the negative one, $\chi^{2}_{2}=.09,\
p=.95$.

Figure 7: Mean PANAS scores. Error bars show 95% CIs.

####  4.5.3 Body ownership

We did not find an effect of avatar manipulation on body ownership:
$\chi^{2}_{2}=2.93,\ p=.23$.

Since valence was found to vary with body ownership in Study I, we hoped to
gain a better sense of the body-ownership–affect relationship with this study.
We tested whether the PANAS components varied with body ownership and found a
significant effect of body ownership on the positive one:
$\chi^{2}_{7}=14.92,\ p=.04$. Significance was not found for the negative
component: $\chi^{2}_{7}=8.33,\ p=.3$.

Inspired by Kilteni et al. [16], we treated body ownership as an ordered
categorical value and in a combination with the numerical PANAS positive
component we performed an ordinal logistic regression of body ownership. This
yielded a fit value with a positive coefficient (the higher the positive-
component response, the greater the likelihood of a high level of body
ownership at the time). Figure 8 shows the estimated probabilities from the
logistic fit $P($_Body ownership_ $|$_Pos. PANAS_ $)$. The probability of high
ownership increases as PANAS values rise. For instance, the estimated
probability of the ownership score being $\geq 7$ is $.76$ for a PANAS value
of $42$ but only $.05$ for a PANAS figure of $16$. Ownership scores below 3
are most likely to be seen with PANAS scores under $16$, with an estimated
probability of $.62$.

Figure 8: The probabilities estimated from the fitted values of an ordinal
logistic regression of body ownership on the positive PANAS-response
component.

Study I found valence to vary significantly with body ownership, while the
foregoing analysis shows that the probability of high ownership increases with
higher positive-affect scores. Together, these findings provide evidence that
body ownership is an important factor in controlling the valence of affect in
embodied VR experiences, particularly for inducing positive affect.

We did not find a link between negative affect and body ownership, and we
speculate that the low variance between subjects for the negative PANAS
component reflects this: negative-component scores varied within the range
10–26 ($SD=3.7$), while positive-component ones ranged from 16 to 42
($SD=6.4$).

###  4.6 Summary

Study II did not reveal differences in affective responses between avatar
conditions (control, upright, and hunched), just as Study I revealed no
differences in affect attributable to full-body avatar manipulations. Hence,
it is likely that posture changes do not have any influence on affect.

We found that body ownership varies significantly with positive affect. With
higher affect scores, there was greater likelihood of high ownership values
being reported: a positive-component PANAS score of $42$ (out of $50$) yields
an estimated $.76$ probability of an ownership score of at least 7 (out of 9);
a positive-component score of $16$ yields an estimated $.05$ probability of an
ownership score of 7 or above.

##  5 Discussion

Baños et al. [22] found that affective content has an impact on presence in
virtual environments: they reported that in non-affective environments
presence depended mainly on immersion, while the relationship proved more
complex for environments with affective content. Jun et al. [14] too reported
on presence and affect: with a body-ownership illusion they found presence to
be positively correlated with valence. Specifically, the authors showed that
owning an avatar with a happy face leads to higher presence estimates.

The study reported upon here expands our understanding of this interplay
between virtual selves and perception. Firstly, we found that facial features
are, in fact, efficient at influencing affect, while a weaker effect of this
sort was found for upper-torso manipulations and movement speed. Secondly, we
showed that positive affect is a good predictor of the likelihood of high body
ownership. Our results suggest that valence is positively correlated with body
ownership, while arousal is not. The results reveal, in addition, that
positive affect is an important factor in body ownership. These findings are
important for research considering affect and VR. For instance, mood-induction
procedures in VR are likely to interfere with body ownership; study designs
should be sensitive to this issue.

Our examination of the link between virtual bodies and users’ affect was
driven by a desire to inform design. We believe that the studies speak to this
goal well – they may fruitfully inform the design of avatars and aid in
creating a more solid foundation for subtle mood-induction techniques for
virtual reality. For the domain of avatar design, our work suggests that some
manipulations may influence affect, though not necessarily in great magnitude.
As hinted at in prior work on VR [14], the most promising route for
influencing affect seems to lie in manipulating facial expressions, especially
smiling. It is far from clear that such manipulations work well for negative
affect, however. Furthermore, indirect manipulations of affect seem more
difficult to achieve; movement speed and posture produce unclear results.
Further utility of the findings can be found in mood induction, as noted
above. Existing mood-induction procedures for VR change the entire environment
[10, 11, 13]. Our results show that less blatant manipulations might be
feasible, although the interplay with body ownership suggests that their
design might be difficult.

Study II found only the positive PANAS component, not the negative one, to
vary with body ownership. While this may seem odd or counter-intuitive, the
two components should be considered wholly independent. In a similar vein,
Study I’s manipulations intended to induce positive affect (smile, slow, and
upright) were more effective than their negative counterparts (frown, fast,
and hunched). Also, in Study I only valence (not arousal) was found to respond
to the condition. Both studies suggest that avatar manipulations are effective
primarily for influencing positive affect.

##  6 Conclusion

We examined whether it is possible to influence affective responses by
altering avatars in virtual reality. Results from an outside-the-lab study
with 207 participants showed that this indeed is feasible. Manipulations to
the avatars’ facial features proved effective in modulating valence responses.

Moreover, we tackled the seemingly harder question of how affect interacts
with the illusion of owning a virtual body. Our results show that positive
affect is of great importance for body ownership: in Study I, valence varied
with body ownership, with positive affect being found to follow body
ownership. Our analyses show that high positive-affect responses increase the
probability of high body-ownership responses. Together, these findings
contribute substantially to our understanding of how emotion information
influences fundamental VR constructs.

## References

  * [1] Margaret Wilson.  Six views of embodied cognition.  Psychonomic Bulletin & Review, 9(4):625–636, 2002.
  * [2] Paula M. Niedenthal.  Embodying emotion.  Science, 316(5827):1002–1005, 2007.
  * [3] Paula M. Niedenthal, L. W. Barsalou, P. Winkielman, S. Krauth-Gruber, and F. Ric.  Embodiment in attitudes, social perception, and emotion.  Personality and Social Psychology Review, 9(3):184–211, 2005.
  * [4] Rafael A. Calvo, Sidney K. D’Mello, Jonathan Gratch, and Arvid Kappas.  The Oxford Handbook of Affective Computing.  Oxford University Press, 1st edition, 2015.
  * [5] J. Diemer, G. W. Alpers, H. M. Peperkorn, Y. Shiban, and A. Muhlberger.  The impact of perception and presence on emotional reactions: A review of research in virtual reality.  Frontiers in Psychology, 6:26, 2015.
  * [6] Panteleimon Ekkekakis.  The Measurement of Affect, Mood, and Emotion: A Guide for Health-Behavioral Research.  Cambridge University Press, 2013.
  * [7] D. Watson, L. A. Clark, and A. Tellegen.  Development and validation of brief measures of positive and negative affect: The panas scales.  Journal of Personality and Social Psychology, 54(6):1063–1070, June 1988.
  * [8] M. M. Bradley and P. J. Lang.  Measuring emotion: The Self-Assessment manikin and the semantic differential.  Journal of Behavior Therapy & Experimental Psychiatry, 25:49–59, 1994.
  * [9] Rainer Westermann, Kordelia Spies, Gunter Stahl, and Friedrich W. Hesse.  Relative effectiveness and validity of mood induction procedures: A meta-analysis.  European Journal of Social Psychology, 26:557–580, 1996.
  * [10] Rosa María Baños, Víctor Liaño, Cristina Botella, Mariano Alcañiz, Belén Guerrero, and Beatriz Rey.  Changing induced moods via virtual reality, pages 7–15.  Springer, Berlin, 2006.
  * [11] Anna Felnhofer, Oswald D. Kothgassner, Mareike Schmidt, Anna-Katharina Heinzle, Leon Beutl, Helmut Hlavacs, and Ilse Kryspin-Exner.  Is virtual reality emotionally arousing? investigating five emotion inducing virtual park scenarios.  International Journal of Human–Computer Studies, 82(C):48–56, October 2015.
  * [12] A. Toet, M. van Welie, and J. Houtkamp.  Is a dark virtual environment scary?  CyberPsychology & Behavior, 12(4):363–371, August 2009.
  * [13] Giuseppe Riva, Fabrizia Mantovani, Claret Samantha Capideville, Alessandra Preziosa, Francesca Morganti, Daniela Villani, Andrea Gaggioli, Cristina Botella, and Mariano Alcañiz.  Affective interactions using virtual reality: The link between presence and emotions.  CyberPsychology & Behavior, 10(1):45–56, February 2007.
  * [14] Joohee Jun, Myeongul Jung, So-Yeon Kim, and Kwanguk (Kenny) Kim.  Full-body ownership illusion can change our emotion.  In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18, pages 601:1–601:11, New York, 2018. ACM.
  * [15] Valeria I. Petkova and H. Henrik Ehrsson.  If i were you: Perceptual illusion of body swapping.  PLOS One, 3(12):1–9, 2008.
  * [16] Konstantina Kilteni, Antonella Maselli, Konrad P. Kording, and Mel Slater.  Over my fake body: Body ownership illusions for studying the multisensory basis of own-body perception.  Frontiers in Human Neuroscience, 9:141, 2015.
  * [17] Jean-Marie Normand, Elias Giannopoulos, Bernhard Spanlang, and Mel Slater.  Multisensory stimulation can induce an illusion of larger belly size in immersive virtual reality.  PLOS One, 6(1):1–11, 2011.
  * [18] Konstantina Kilteni, Jean-Marie Normand, Maria V. Sanchez-Vives, and Mel Slater.  Extending body space in immersive virtual reality: A very long arm illusion.  PLOS One, 7(7):1–15, 2012.
  * [19] Kristopher J. Blom, Jorge Arroyo-Palacios, and Mel Slater.  The effects of rotating the self out of the body in the full virtual body ownership illusion.  Perception, 43(4):275–294, 2014.
  * [20] Domna Banakou, Raphaela Groten, and Mel Slater.  Illusory ownership of a virtual child body causes overestimation of object sizes and implicit attitude changes.  PNAS, 110(31):12846–12851, 2013.
  * [21] Tabitha C. Peck, Sofia Seinfeld, Salvatore M. Aglioti, and Mel Slater.  Putting yourself in the skin of a black avatar reduces implicit racial bias.  Consciousness and Cognition, 22(3):779–787, 2013.
  * [22] R. M. Baños, C. Botella, M. Alcañiz, V. Liaño, B. Guerrero, and B. Rey.  Immersion and emotion: Their impact on the sense of presence.  CyberPsychology & Behavior, 7(6):734–741, 2004.
  * [23] Anthony Steed, Sebastian Friston, Maria M. López, Jason Drummond, Ye Pan, and David Swapp.  An “in the wild” experiment on presence and embodiment using consumer virtual reality equipment.  IEEE Transactions on Visualization and Computer Graphics, 22(4):1406–1414, April 2016.
  * [24] Aske Mottelson and Kasper Hornbæk.  Virtual reality studies outside the laboratory.  In Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, VRST ’17, pages 9:1–9:10, New York, 2017. ACM.
  * [25] Stephen A. Coombes, Christopher M. Janelle, and Aaron R. Duley.  Emotion and motor control: Movement attributes following affective picture processing.  Journal of Motor Behavior, 37(6):425–436, 2005.
  * [26] Harald G. Wallbott.  Bodily expression of emotion.  European Journal of Social Psychology, 28(6):879–896, 1998.
  * [27] F. Strack, L. L. Martin, and S. Stepper.  Inhibiting and facilitating conditions of the human smile: A nonobtrusive test of the facial feedback hypothesis.  Journal of Personality and Social Psychology, 54(5):768–777, May 1988.
  * [28] Alexandre Schaefer, Frédéric Nils, Xavier Sanchez, and Pierre Philippot.  Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers.  Cognition and Emotion, 24(7):1153–1172, 2010.
  * [29] John R. Crawford and Julie D. Henry.  The positive and negative affect schedule (panas): Construct validity, measurement properties and normative data in a large non-clinical sample.  British Journal of Clinical Psychology, 43(3):245–265, 2004.