Acceptability and Trustworthiness of Virtual Agents by Effects ofTheory of Mind and Social Skills Training
Hiroki Tanaka and Takeshi Saga and Kota Iwauchi and Satoshi Nakamura
Division of Information Science, Nara Institute of Science and Technology, Japan

Abstract— We constructed a social skills training systemusing virtual agents and developed a new training module forfour basic tasks: declining, requesting, praising, and listening.
Previous work demonstrated that a virtual agent’s theory ofmind inﬂuences the building of trust between agents andusers. The purpose of this study is to explore the effect oftrustworthiness, acceptability, familiarity, and likeability on theagents’ theory of mind and the social skills training contents.
In our experiment, 29 participants rated the trustworthinessand acceptability of the virtual agent after watching a video
that featured levels of theory of mind and social skills training.
Their ratings were obtained using self-evaluation measures at
each stage. We conﬁrmed that our users’ trust and acceptabilityof the virtual agent were signiﬁcantly changed depending on thelevel of the virtual agent’s theory of mind. We also conﬁrmedthat the users’ trust and acceptability in the trainer tended to
improve after the social skills training.

I. I NTRODUCTION
Social skills training (SST) is generally conducted by ahuman trainer to promote appropriate social interaction skills[18], [19]. The Bellack method (or a step-by-step SST),
which is a well-structured and widely used evidence-based
approach, is a cognitive behavioral approach to SST inspiredby the ﬁve core principles of social learning theory: modeling, shaping, reinforcement, overlearning, and generalization.
In clinical settings, SST is applied to people with autism
spectrum disorder, schizophrenia, or other social communication difﬁculties, usually conducted by human trainers in
re-work programs and day-care programs at medical institutions. The Bellack method deﬁnes the SST framework andits four basic skills: expressing positive feelings, listening toothers, making a request, and expressing unpleasant feelings(or declining) [6]. SST consists of 1) instruction and target
skills, 2) modeling, 3) role-playing, 4) feedback, and 5)
homework. If skills for these tasks are acquired, applications
to other advanced skills (e.g., continuing a conversation,
empathy, self-disclosure, and multi-party conversations) aresimpliﬁed. These skills are beneﬁcial for all people (not only
those with autism spectrum disorder or schizophrenia). In
particular, autism spectrum disorder is a spectrum condition,
meaning it has a broad range of characteristics, from mild
to severe [36].
The motivation to use virtual agents in SST reﬂects thefact that even though some people with autism experience
difﬁculty during social communication, they also show goodor even superior systemizing skills. Systemizing is the driveThis research was funded by JSPS KAKENHI grant number 22K12151,
JST CREST grant number JPMJCR19A5, and AIP challenge program.

to analyze or build systems and understand and predict
behavior in terms of underlying rules and regularities [4].
Systematic virtual training provides the following beneﬁts
for people who need to improve their social skills: 1) it uses
a computerized environment that is predictable, consistent,
and free from social demands; 2) users can work at their own
pace and level of understanding; 3) training can be repeateduntil the goal is achieved; and 4) interest and motivation canbe maintained through computerized rewards [7], [21]. Those
who suffer from social difﬁculties might beneﬁt more fromusing virtual agents than directly interacting with strangers[36]. A previous paper suggested that people with social
difﬁculties feel safer and more comfortable in virtual interactions than in interactions with actual people [30]. However,even though such populations feel more secure in virtualinteractions than in interactions with actual people, a big gap
remains between human training and training with virtual
agents.
We have been studying the automation of SST using
virtual agents and have developed a system that resembles
human trainers’ SST. We conﬁrmed our training’s effectiveness in children and in adults with autism spectrum
disorder [35] as well as members of the general population[36]. Our system models human behavior and provides realtime behavioral assessment as well as feedback for speaking and listening tasks [32], [33]. As its acceptability andtrustworthiness grow, the stronger becomes the therapeutic
alliance (trust between the therapist and the participant) that
we are building. We expect the training effect to improve[1]. Previous works investigated the acceptability and trustworthiness of virtual agents [12], [37], [13], [28]. We also
investigated the effects of a virtual agent’s appearance onuser’s acceptability and trustworthiness as well as the effecton autistic traits [34].
However, insufﬁcient research has addressed the designof virtual agents to investigate what function of virtual
agents and SST contents inﬂuences their trustworthiness andacceptability. In this study, we focus on the theory of mind
(ToM) [14] as a factor that affects the trustworthiness ofvirtual agents [3], [26]. ToM refers to the inference of themental states of others, including their feelings, thoughts,
and beliefs. ToM also incorporates the understanding that
others may have different mental states from ours [39]. Thissocial reasoning process develops in early childhood and isfundamental for successful social behavior. One measure for
estimating one’s ToM is using false-belief tasks [31], [38],such as the Sally and Anne test [16]. A false-belief task is

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Fig. 1. (I) Role-playing with a virtual agent and (II) Summary feedback
visualization. Texts are written in Japanese.

the understanding that an individual’s belief or representation
about the world might conﬂict with reality [5]. Prior researchargued that users’ trust in robots changes as a function of
their perception of the extent to which they possess high or
low levels of ToM, as measured by a modiﬁed Sally andAnne scenario. When a user watches a video of a dialogue
scenario during a robot’s false-belief task, the user’s belief inthe robot’s subsequent recommendations changes dependingon the ToM level [22].
Considering the above, we constructed an SST systemthat includes new tasks and conducted SST in the following
scenarios in which a virtual agent appeared: viewing avideo with an adjusted level of ToM, declining or making
a request, and investigating the user’s trustworthiness ineach stage using measures. We examined whether the user’s
acceptability changed signiﬁcantly depending on the levelof the virtual agent’s ToM. We also attempted to conﬁrm
whether the user’s trustworthiness improved after SST. This
paper aims to clarify the following two hypotheses using our
SST system:• H1 : The virtual agents’ ToM levels have effects on theiracceptability and trust as trainers.
• H2 : Distrust is weakened by the SST process.
II. SST S YSTEM I MPLEMENTATION
We built a fully-automated SST system using the Greta
platform [23] and a virtual agent named Rei (Fig. 1). Our
system is capable of speech recognition, response selection,and speech synthesis and can also generate facial expressions, gestures, and nods. Non-verbal behaviors are generated
in the speciﬁc command embedded in the dialogue response.
The connection among modules was done by ActiveMQ.
This system works in real-time as a Windows application.
The virtual agent’s appearance and gender can be changed.
We designed anime-type female characters and previously
investigated the acceptability and trustworthiness of theirappearance [34]. This system is applied to healthy people,
not only people with disorders.
A. Task extension
Our system’s previous version, which is available [32],was limited to one-way conversational skills like speaking.

After further discussions with psychiatrists, we created four
new tasks based on SST’s basic training model as well as
scenarios for them: declining a request, listening to others,
making a request, and expressing positive feelings. These
were selected from four basic tasks in the Bellack method.
Among these, declining a request is the most difﬁcult [6].
Declining and listening seem to be system-initiative roleplaying, and the other two seem to be user-initiative roleplaying in terms of dialogue. After a brief greeting, thevirtual agent explains to the participants the importance ofthe training task. An example scenario is shown in Table
I. We made seven role-play variations for each of the fourtasks by referring to the SST data in one-on-one and oneon-two situations conducted by psychiatrists and people with
autism spectrum disorders, schizophrenia, or healthy controlsthat were previously recorded [27]. The role-play variations
include the following topics: hospitals, home, school, theworkplace, and friends.
B. Analyzing multimodal behaviors
The system records the users’ voices and images by a pin
microphone and a webcam to sense the user behavior. During
role-plays, the system perceives the user’s utterances byspeech recognition and responds based on its keywords (yes/I
understand/I have, etc.) prepared by rule-based interaction
scenarios. We used Google Cloud Services for the speech
recognition and speech synthesis modules. If the keywords
were not directly included in the speech recognition output,
we used the Japanese version of the Bidirectional Encoder
Representations from the Transformers (BERT) model [11],[20] to calculate the cosine distance to the above keywords
at the sentence unit and chose the closest keywords. We
continued to improve our response selection module throughvalidation to reduce the number of dialogue selections andtiming failures.
We constructed a score evaluator from the role-playing
videos [27] and automatically predicted seven items: eyecontact, body orientation, facial expression, voice change,
clarity, ﬂuency, and social validity. All were rated by psychiatrists on a 5-point scale based on user behavioral indicators,
using multimodal features (Praat [8], Openface [2], Openpose [9]) and BERT similarity scores between utterances
spoken by the virtual agent and users, etc. Random forests
predicted these features. The ground truth of this evaluation was rated by two psychiatrists. Our detailed prediction
performance was previously reported [27], and the correlation coefﬁcient between the ground truth and predictedvalues was about 0.1 to 0.6. Depending on the evaluation
results, a radar chart, positive comments, and corrective
comments were presented on a screen with video clips, andthe comments were read aloud by the virtual agent. The radar
chart shows the evaluation values. Past training history canbe represented by a different color. Positive reinforcement
involves providing a valued or desired outcome following
a behavior (e.g., verbal praise, money). In SST, role-playrehearsals by participants are always immediately followedby positive feedback about what speciﬁcally a person did

TABLE I
E XAMPLE OF AN SST SCENARIO : T RAINER : R EI , U SER : M R . TANAKA .
SpeakerTrainer
User
Trainer
User
Trainer

User
Trainer
User
Trainer
User
Trainer
User
Trainer
User
Trainer
User
TrainerTrainer

Response
Hello, I’m Rei. What’s your name?
Hello, I’m Hiroki Tanaka.
How are you doing?
I’m doing okay.
Good. Today we are going to learn some skills forpolitely declining a request from another person.
When someone asks you to do something or for afavor, you might not be able to help. You might be
too busy, you might not feel very conﬁdent unlikely
about being able to help, or perhaps you just aren’tinterested. Do you understand?
Yes, Yes, I do.
Let’s start an actual role-play. Are you ready?
Yes.
Excuse me, I’d like to ask you a favor, do you havea car?
No, I don’t, sorry.
No problem. Since the nearest station is so far away,
could you please ask someone to give me a ride to
the station?
Uh, I’m sorry, I don’t know anyone around here, so
I can’t help you.
Can you please do something?
Hmm, I’m sorry, I’m can’t. Sorry.
Okay. Sorry to bother you. I’ll have to ask someone
else. Thanks anyway.
No worries.
Okay, that is the end of our role-play. Please giveme a minute to calculate your score.
(Feedback screen presentation) Here is your feedback. (Reads it)

well. A genuinely positive aspect must be found in eventhe poorest role-play performance [6]. The virtual agent andfeedback are displayed in digital signage, and the feedback
is displayed on the full-screen after the calculation is over.
Our system is intended to build trust and clearly understanduser behaviors to effectively train social skills.
III. E XPERIMENTAL E VALUATION : R ATINGS WITHREGARD TO T HEORY OF M IND AND S OCIAL S KILLST RAINING
In this study, we aim to clarify the following two hypotheses using our SST system: (H1 ) the virtual agents’ ToM
levels have effects on their acceptability and trust as trainers,and (H2 ) distrust is weakened by the SST process.
A. Participants
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review
Board of the Nara Institute of Science and Technology (protocol code 2018-I-1). We collected data from 29 participantsfrom 22 to 35 years old (12 males and 17 females). All wereJapanese without any history of psychiatric disorders. The
experiment was conducted in person at the Nara Institute of
Science and Technology. All participants were informed ofthe experiment’s protocols in writing and their consent was
obtained.
The participants were asked to complete the following
three psychological assessments: the Social Responsiveness
Scale (SRS)-2 [10], Kikuchi’s scale of social skills (KISS)-18

Fig. 2.

Snapshot of ToM video[17], and the new version of The State-Trait Anxiety Inventory (STAI) [29], obtained in a social skills analysis framework [15]. SRS-2 has social awareness subscales: (Aware.),social cognition (Cog.), social communication (Com.), social
motivation (Mot.), and restricted and repetitive behaviors(Rep.). STAI is divided into state and trait anxieties. These
assessments were collected at the beginning of the experiment by an online Google form (SRS and KISS-18) or a
printed paper form (STAI). We also obtained data on eye
movement measurements and feedback evaluations of the
SST system, although they are not included in this paper.
Video and other data obtained in this study can be requested
from the ﬁrst author.
B. Theory of mind levels
Our ﬁrst hypothesis is whether the agent’s ToM levels
affected its acceptability and trust as a social skills trainer.
A previous study conﬁrmed that a user’s trust was reduced
by watching a ToM video where the agent fails the modiﬁed
Sally and Anne test [22]. In this study, referring to speciﬁc
scenarios, we conducted a three-person dialogue (a trainer
and two users) and recorded videos of cases where thevirtual agents failed the false-belief task (Table II). We
also recorded a scenario where the virtual agents passed
a task where the trainer says when she was asked: ”Um,since Mr. Iwauchi did not know that the toy bucket was
moved, I think he’s looking for it in the green cup.” Thetiming, speech, and nodding of the virtual agents weremanipulated by the Wizard of Oz (WoZ) paradigm (Fig.
2). The videos prepared for the participants included subtitles to simplify understanding. The videos can be foundat https://drive.google.com/drive/folders/
1ebcfM2euwFV8LIUJ1MZG0nJYeWqnta_C.
C. Measures
Based on our previous study [34] and the works ofothers [13], [12], we obtained subjective ratings from theparticipants of the acceptability, trustworthiness, familiarity,and likability of the trainers. The participants rated each
item on a 5-point Likert scale (1: completely disagree, 5:strongly agree). The questionnaire can be found in Tanaka
and Nakamura [34].

TABLE IIS CENARIO WHERE VIRTUAL AGENT FAILS THE MODIFIED S ALLY AND
A NNE TEST:. T RAINER : R EI , U SER A: M R . TANAKA , U SER B: M R .

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1,2345#+#56()* 78)945#+#56()**#$/0%#/..(I WAUCHI .
SpeakerTrainer
User A
User B
User B
User A
Trainer
User B
User A
Trainer
User B
User A
Trainer
User A
Trainer
User A
User B
User B

Response
Nice to meet you. Hello, Mr. Tanaka and Mr.
Iwauchi.
Nice to meet you, Rei, Hello.
Hello.
Now I’d like to put this little yellow toy bucket in
the green cup.
Okay.
I understand.
Excuse me, I just remembered something that I
forgot to take care of, so I have to leave for a few
minutes.
Okay, see you later.
Yes, later.
(Leaves the room)Now since Mr. Iwauchi has left, I want to move thistoy bucket from the green cup to the red one.
Okay.
By the way, which cup do you think Mr. Iwauchi
will look for when he returns?
Um, there is a toy bucket in the red cup, so I think
he’ll look for it in the red cup.
I see.
(returning to the room) I’m sorry to have kept you
waiting.
(Looking inside the green cup) Oh, the toy bucket isgone.

D. Procedure
Figure 3 shows a schematic ﬂow of our experiment. The
participants were randomly divided into groups so that their
number was in the same proportion according to their ToM
levels (N=15 and N=14) and SST tasks. There is no large
difference between the two groups in terms of gender andage. Before starting the experiment, we carefully explained
the purpose of the SST system and the trainer’s so that ourparticipants clearly understand the role of our virtual agent.
After answering the three assessments described above,
the participants interacted with the SST trainer. First, they
faced the SST system and watched the virtual agent start:”Hello, let’s practice some communication” (greeting). Thenthe users answered the measures (the appearance stage).
Next they watched a video on ToM, which was different
for each group (high-level ToM or low-level ToM). Thenthey answered the measures (the ToM video stage). Next
with the SST system, each group either declined or madea request in a role-play and watched the feedback. Finally,
they answered the measures (the SST stage). We found no
signiﬁcant difference between the two SST tasks (requestingor declining) regardless of the ToM levels in all the measures(Wilcoxon ranks sum test, p > 0.05). Nor we did not ﬁnd
any relationship between the ToM levels and the assessments(Spearman’s correlation coefﬁcient, p > 0.05).
E. Statistical analysis
Spearman’s correlation coefﬁcient was calculated between
the question ratings at the appearance stage and the assessments obtained before interactions.

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Fig. 3.

*$<,&26$6%#=0#/;67>#'5,&,&2Experiment ﬂow

Statistical comparisons were made between groups in theabove three stages. We calculated the differences between theappearance and ToM video stages and the ToM video and
SST stages according to the ToM levels. Since we did not
conﬁrm equal variance and normality in some cases, comparisons between groups in the ToM video stage regarding
the ToM levels were made by a Wilcoxon ranks sum test
(H1 ). We also tested whether the distrust caused by the lowlevel ToM video was weakened by the Wilcoxon rank-sum
test (H2 ). We did not perform any corrections for multiple
comparisons. The signiﬁcance levels in this study were all
set at 5%.
F. Results
Table III indicates the Spearman’s correlation coefﬁcient
between measures at the greeting stage and the assessments
obtained before the interactions. We found a maximum
signiﬁcant negative correlation between familiarity and theSRS-2 social communication subscale (ρ = −0.48), indicating that higher communication skills tended to rate the virtual
agent as familiar (Figure 4). A similar correlation was also
shown for familiarity and KISS-18 (ρ = 0.46).
We conﬁrmed that the ratings at the appearance stage
were not signiﬁcantly different between the ToM groups inall the measures (p > 0.05). We also conﬁrmed no large
difference compared to our previous work [34] regarding all
the measures in the appearance stage: acceptability (mean:
3.55, SD: 1.09), trustworthiness (mean: 3.24, SD: 1.09),
familiarity (mean: 3.07, SD: 1.07), and likeability (mean:
2.93, SD: 0.96).
Figure 5 shows 5-point scores of acceptability, trustworthiness, familiarity, and likeability at each stage. For trustworthiness, we also conﬁrmed a larger effect size for a virtual
agent’s theory of mind (Cohen’s d=2.01) than two different
agents’ appearance (Cohen’s d=0.37) (Figure 2 on [34]). Figure 6 shows boxplots of the measures at ﬁrst two stages andlast two stages of acceptability, trustworthiness, familiarity,and likeability to represent the distribution. Left-hand side
explains subtraction of 5-point scores in the appearance stage
from those in the ToM video stage. Right-hand side explainssubtraction of 5-point scores in the ToM video stage fromthose in the SST stage. Our results show that the ToM level(high-level ToM or low-level ToM) signiﬁcantly affected theacceptability (p < 0.001) and the trustworthiness of the
trainers (p = 0.005), although not the familiarity (p > 0.05)
or likeability (p > 0.05) between the appearance and ToM

Trustworthiness5

5

Acceptability
Low-level ToM!

Low-level ToM

High-level ToM

34

1
ToM Video

SST

0

1
0

3

Appearance

Appearance

ToM Video

SST

Likeablity

5

5

2

Familiarity
Low-level ToM

Low-level ToM!

High-level ToM

!

2

2

0

3

3

4

4

High-level ToM

1Participant rating

4

2

2

5

3

Familiarity!

4

High-level ToM

30

40

0

SRS-2 social communication subscale

120

Appearance

ToM Video

SST

0

10

1

0

Appearance

ToM Video

SST

Fig. 4. Negative correlation between familiarity and the SRS-2 social
communication subscale. There are two same points on the familiarity of 4
and SRS-2 social communication subscale of 6.

Fig. 5. Barplots with regard to 5-point scores of acceptability, trustworthiness, familiarity, and likeability. Error bars represent standard error. *:p < 0.05 by the Wilcoxon ranks sum test.

video stages. The ﬁgure also shows that the ToM level(high-level ToM or low-level ToM) signiﬁcantly affected
acceptability (p = 0.003), trustworthiness (p < 0.001),
and likeability (p = 0.026), although it did not affect thefamiliarity (p > 0.05) between the ToM video and SST
stages.

acceptability and trustworthiness of an agent with a lowlevel ToM signiﬁcantly decreased. But we found signiﬁcant improvement in the acceptability, trustworthiness, andlikeability after SST by observing the differences between
the ToM video and SST stages. We also found that higher
communication skills (as measured by SRS-2) tended to rate
the virtual agent as familiar. These results supported thevalidity of I1 and I2 : the effect of the ToM level on theacceptability and reliability of the trainers, and distrust was
weakened by the SST process.
One limitation of this study is that the SST and ToM
videos might be perceived as independent by the participants.
In addition, we did not examine which SST factors improved
the subjective ratings. Incorporating ToM features into virtual
agents during SST [26], [38] is a possible step to examine
the direct effect of ToM levels on the SST. Such possible
integration is a future challenge. We did not show that
our results could be directly generalized to the other agent
identities. We did not conﬁrm that participants perceived thatthe agent had a high or low theory of mind. Although the two
ToM scenarios differed in only one utterance, the reported
effects might be related to some other feature or perceptionof the participants.
Based on the ﬁndings of this study, in the future, we
believe each module of an SST system must be improved,
including ToM, and SST must be designed to enhance thefunctionality of other modules for building alliances withusers. For example, playing games or engaging in small talkbefore starting the SSTs [25] is one critical step. In addition,
since people with social difﬁculties struggle to understand
ToM, incorporating such social cognition training into SST
as role-playing is crucial [24].

IV. D ISCUSSION AND C ONCLUSIONSIn this study, we developed a new SST system for fourbasic tasks: declining, making a request, expressing positive
feelings, and listening to others. This is an extension of
our previous work regarding two-way dialogues. During
role-plays, the system recognized user utterances by speech
recognition, responded, and provided visual feedback basedon the sensed behavioral signals.
Our experimental evaluations aimed to clarify the following two research hypotheses: (H1 ) the virtual agents’ToM levels have effects on their acceptability and trust as
trainers, and (H2 ) distrust was weakened by the SST process.
This study’s purpose was to use the system to explore
the effect of trustworthiness, acceptability, familiarity, andlikeability on the agents’ ToM and the SST procedure. 29
participants rated the stage of appearance by watching avideo with ToM levels and SST. Their subjective ratings ofthe system’s acceptability, trustworthiness, familiarity, andlikeability were rated using measures at three stages. Prior
to the experiment, we also obtained assessments for theparticipants’ autistic traits, anxiety, and general social skills.
Our experiment results conﬁrmed that the users’ trust andacceptability signiﬁcantly changed depending on the levelof the virtual agent’s theory of mind. In particular, theTABLE III
S PEARMAN ’ S CORRELATIONS BETWEEN MEASURES AND ASSESSMENTS AT APPEARANCE STAGE *: p < 0.05 IN COMPARISON WITHOUTCORRELATIONS .

Accept.
Trust.
Famil.
Like.

State
-0.05
-0.07
-0.16
-0.18

Trait
-0.12
-0.17
-0.26
-0.36 *SRS-Total
-0.25
-0.28
-0.41 *
-0.29

Aware.
-0.35 *
-0.22
-0.26
-0.27 *

Cog.
-0.2
-0.3
-0.3
-0.34

Com.
-0.24
-0.28
-0.48 *
-0.29 *

Mot.
-0.08
-0.06
-0.33
-0.2

Res.
-0.37
-0.41 *
-0.34
-0.12

KISS-18
0.16
0.23
0.46 *
0.31

R EFERENCES

!

-4

-4

-2

SST - ToM video
02

ToM video - Appearance
-2
0
2

4

Acceptability!

4

Acceptability

High-level ToM

Low-level ToM

!

4

4

Trustworthiness

!

-4

-4

-2

SST - ToM video
02

ToM video - Appearance-20
2High-level ToM

Low-level ToM

Trustworthiness

Low-level ToM

High-level ToM

Familiarity

-4

-4

-2

SST - ToM video
02

ToM video - Appearance
-2
0
2

4

Familiarity

4

High-level ToM

Low-level ToM

Low-level ToM

High-level ToM

Likeability

44

Likeability

!

-4

-4

-2

SST - ToM video
02

ToM video - Appearance-20
2High-level ToM

Low-level ToM

Low-level ToM

High-level ToM

Low-level ToM

High-level ToM

Fig. 6. Boxplots with regard to ratings of acceptability, trustworthiness,familiarity, and likeability: Left-hand side explains subtraction of 5-pointscores in the appearance stage from those in the ToM video stage. Righthand side explains subtraction of 5-point scores in the ToM video stage
from those in the SST stage. *: p < 0.05 by the Wilcoxon ranks sum test.

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