HAI シンポジウム 2022
Human-Agent Interaction Symposium 2022

P-6

バーチャルエージェントの視点取得による
向社会的行動の促進
Perspective taking of virtual agents for promoting pro-social behaviors
杭陳琳 1,3 小野哲雄 2 山田誠二 1,3
Chenlin Hang1,3, Tetsuo Ono2, Seiji Yamada1,3
1

1

総合研究大学院大学
The Graduate University for Advanced Studies, SOKENDAI
2
北海道大学
2
Hokkaido University3
国立情報学研究所
3
National Institute of Informatics

Abstract: Perspective taking, which allows people to imagine another's thinking and goals, is known to bean effective method for promoting pro-social behaviors in human-computer interactions. However, most of
the previous studies have focused on simulating human-human interactions in the real world by offering
participants experiences related to various moral tasks through the use of human-like virtual agents. In thisstudy we investigated whether taking the perspective of a different robot in a robot-altruistic task would
influence the social behaviors of participants in a dictator game. Our findings showed that participants who
watched the help-receiver view exhibited more altruistic behaviors toward a certain robot than those who
watched the help-provider view. We also found that, after watching two different views of robots in the
robot-altruistic task, participants did not change their behavior with regard to the other participant or to a
specific robot.

1.Introduction
Technological artefacts such as autonomous cars, vacuum
cleaners, smartphones, virtual agents, and robots that act
autonomously in social environments are fast becoming areality and are expected to increasingly interact withhumans in a social way [1]. Many researchers have beenfascinated with how these artefacts can persuade people toengage in pro-social behavior [2-7]. Empathy, which playsa central role in human social relationships and isconsidered a major element in human social interaction,
has been shown to increase understanding and motivate
pro-social behaviors [8-11]. Paiva et al. mentioned two
possible systems for empathy: (1) a basic emotional andunconscious one and (2) a more advanced cognitive
perspective-taking system [12]. As the basic system
includes unconscious elements, which may lead to ethicalproblems, we focus on the perspective-taking system inthis work. Extensive research has shown that taking theperspective of others (i.e., imagining what it would feellike to be the other person) can be a valid way of
promoting pro-social behaviors [11]. Imagine-self
perspective-taking tasks have typically resulted in better
performances than the imagine-other approach for
maximizing the motivation to help someone [13]. The
image-self task, in which participants are instructed toimagine how they would feel if they were in someone
else’s situation, is somewhat different from the imageother task, in which participants imagine how someone
else feels about a specific situation. With the developmentof technology, mediated perspective -taking tasks (e.g.,
online role-playing games, videos, immersive virtual
realities) that provide additional information toparticipants or users instead of relying only on the user's
imagination have also shown good potential for promoting
pro-social behaviors [14-17].

In the context of mediated perspective-taking tasks forpromoting pro-social behaviors, most researchers havefocused on simulating a specific situation based on ahuman-human interaction, and the virtual agents they use
are almost always human-like appearances [16-19].
Considering the harmonious relation between humans androbots, we need to examine whether taking the perspectiveof robots in a robot task can influence people's pro-social
behaviors to a human or a robot. Among the variety ofelements that make up the pro-social task, in this work wefocus on altruism, which forms a major part of pro-social
behavior and plays a central role in our evolutionary
origins, social relations, and societal organization [20].
Based on the definition of altruism, i.e., the behavior of aperson who helps others at his or her own expense [21],the altruistic task should contain at least two objects (thehelp provider and the help receiver). Therefore, we
conducted an experiment to determine whether
participants who took the perspective of different robots
in a robot-altruistic task would be influenced in their social
behaviors.
The between-participants experiment was conducted byshowing different perspective views of robots engaged ina robot-altruistic task. One of the groups watched the helpprovider-view video and the other group watched helpreceiver-view video. The task settings are based on thework by Hang et al. [22], who applied nudge mechanisms
in video stimulus utilizing robot-like virtual agents forpromoting human altruistic behaviors. They conducted atask in which two robots each had to arrange a meeting
room, where one robot was lower-charged (treated as thereceiver in our study) and the other was fully charged(treated as the provider in our study). The idea was that theprovider would share its battery with the receiver when thereceiver ran out of power. We measured the behavior
changes through a dictator game, which is a simple
economic game typically used to measure altruistic
attitudes. Our motivation was to determine not only the
behavior changes to human but also to a certain robot andthe specific robot that appeared in the video stimulus. The
results showed that there was no difference in the behavior
changes of participants to human and a certain robot.
However, participants had significant differences inbehavior change after watching the different robot's
perspective to a certain robot.

Figure.1 Workspace of each robot

Figure.2 The view of different robot in the same frame

2.Experiment2.1. Video stimulus
To imitate a real-life situation, Hang et al. [22] usedvirtual agents with a robot-like appearance in the video
stimulus. Based on the definition of altruism (i.e., thebehavior of a person who helps others at their own expense[21]), with the expense of the robots symbolized by theirbatteries, they investigated a scenario involving two
robots doing the same task (organizing tables and chairs ina meeting room) (see Figure.1), where one of the robots
suddenly stops working because its battery runs out. The
battery of one robot was near 3% while the other was fully
charged, so the intent was to have the lower-charged robot
stop working soon and this would lead to altruistic
behavior from the other robot. A beep sounded when thelower-charged robot's battery died, and its eyes and ears
flashed a red light. After hearing the alarm and seeing the
flashing lights, the fully charged robot moved towards thelower-charged robot and shared its battery power byplacing a hand on its shoulder.
In our study, we set the view of the lower-charged robot
as the help-receiver's view and that of the fully charged
robot as the provider's view. We used MikuMikuDance, acontent edits tooling for 3DCG videos. To imitate the firstperson perspective, we set the view of the video at theposition of each robot's head. As there are two robots inthe setting, we take one video for each robot. Figure.2
shows the view of the two robots at the same frame, withthat of the blue robot (lower-charged) on the left and thatof the green robot (fully charged) on the right.

2.2. Participants
Before collecting data for the experiment, we conducteda power analysis to determine the best sample size. A G*
Power 3.1.9.7 analysis [23] (effect size f = 0.25, α= 0.05,
and 1 -β= 0.80) suggested an initial target sample size of
N = 128. A total of 176 participants (116 male, 60 female)ranging in age from 16 to 87 years (M = 45.06, SD = 12.91)
took part in the experiment online. The participants wererecruited through a crowd sourcing service provided by
Yahoo! Japan. Regarding online experiments in general,
Crump et al. [24] have suggested that data collected online
using a web browser seem mostly in line with laboratory
results, so long as the experiment methods are solid.
Thirty-three participants were excluded due to a failure
to answer comprehension questions on the video stimulus.
Then, using the calculation of G*Power 3.1.9.7 analysis[23] as a basis, 64 participants were analyzed under eachof the two conditions in our experiment in chronologicalorder. The final sample of participants was composed of
128 (male = 84, female = 44, M = 45.70, SD = 12.59).

2.3. Procedure
We first asked the participants to read an introductionof the experiment and then watch the video stimulus. Next,
to ensure they had watched the video completely, twocomprehension questions were asked. They were thenasked to play the dictator game with three other players(another participant, a certain robot, and the lowercharged robot in the video stimulus (see Figure. 3)) andstate how much money they would give their fellow
players if they had 1,000 yen. These three situations ofdictator game were shown in a random order to avoid theeffect of order bias (i.e., practice effect [25]). Finally, afree description question was asked to obtain commentsfrom the participants at the end of the experiments.

Figure.3 Virtual agent in Dictator game

Figure.4 The table of results

2.4. Measurement
We used the dictator game to measure the altruistic
attitudes of participants as the dependent variable. In thebasic structure of this game, A (the dictator) freely decides
how much x of an endowment to give to B (the recipient).
B has no veto power; that is, she cannot react to A's
decision. Based on this structure, we set three different
recipients (the other participant, a certain robot, and thelower-charged robot in the video stimulus). The reason we
only chose the lower-charged robot was to forbid
reciprocal thinking on the part of the green robot.
A real-life example of the dictator game would be one
where players decide about making donations to a publiccharity but considering that robots do not have any publiccharity, we set the questions based on the original structure.
The questions were asked as follows. "Now you are given
1,000 yen (about US$8.70). The money needs to be sharedwith the other participant/ a certain robot/ the lowercharged robot in the video stimulus. How much money
you would give to the other participant/ a certain robot/ thelower-charged robot in the video stimulus?" The answer
was provided using free input ranging from 0 to 1,000 yen.

2.5. Result
We analyzed the results of the dictator game for eachplayer (the other participant, a certain robot, and thelower-charged robot in the video stimulus) separately withindependent sample t-test (see Figure.4).
The results of the other participant showed that there wasno significant difference (t(126) = 1.83, p = 0.07) between
the group that watched the green robot's view and thegroup that watched the blue robot's view. In contrast, theresults of a certain robot showed that there was significant
difference (t(126) = 2.20, p = 0.029) between the twogroups. Moreover, participants who watched the bluerobot's view tended to give more money to a certain robot.
As for the lower-charged robot in the video stimulus, theresults showed that there was no significant difference(t(126) = 1.45, p = 0.14) between the two groups.

3.Discussion
We conducted this experiment to investigate whether
experiencing two different views of virtual robots in thesame altruistic task could promote human altruistic
behavior. Our analysis of the data obtained from theexperiment showed that, for the other participant and thelower-charged robot in the video stimulus, participants did
not exhibit much difference in their altruistic behavior
after watching the videos of the blue robot’s view and thegreen robot’s view. However, for a certain robot,
participants showed more altruistic behavior after
watching the blue robot’s view than the green robot’s one.
As for the results of the other participants, although there
was no significant difference in altruistic behavior after
watching the blue or green robots’ videos, the commentsof the participants revealed that many of them felt ahuman-like quality in the robots when they performed
altruistic behaviors (sharing battery power with the lowercharged robot), and this prompted them to recall their own
coworkers or even to reflect on their daily behavior. Many
previous works have shown that interacting with humanlike agents actually enhances the pro-social behavior ofparticipants [16-19]. The significant difference between
our work and previous research is not only the appearanceof the virtual agent but also the task that is shown in thevideo stimulus. Therefore, it would be interesting toinvestigate the relation between the task and appearancesthat can not only promote human pro-social behavior but
also push the relation between human-robot and humanagent interactions. In addition, we plan to conduct
additional experiments to determine whether there would
be more of an influence when we use various equipmentthat enables participants to receive physical feedback. For
example, by extending our video-based experiment to theimmersive virtual reality, participants will be able to reallyfeel what it is like when the battery runs out, such as bymaking their body stuck.
The results for the lower-charged robot also showed that
there was no significant difference in altruistic behavior
after watching the videos of either robot. This suggeststhat the exposure of the lower-charged robot is not
sufficient. Specifically, in the blue-robot-view video, theparticipants could not see what happened to theappearance of the blue robot when it ran out of battery.
Similarly, in the green-robot-view video, the participants
could only see the lower-charged robot from the time thegreen robot walked toward to it to the time it finishedsharing the battery, which was only the final 20 seconds ofa video that was two-and-a-half minutes long.
The results for a certain robot showed that participants
who watched the blue-robot-view video gave more money
to a specific robot than those who watched the greenrobot-view video. This demonstrates that taking theperspective of the help-receive robot will cause people togive more money to a certain robot, which is similar to theexperiment done by Baston et al. [9] in which participants
who took the perspective of a member of a stigmatizedgroup reported more positive attitudes even toward thestigmatized group as a whole.
Finally, the biggest difference between these two video
stimuli was the period where the blue robot ran out ofbattery: the view of it was static until the green robot
shared its battery with it. We conjecture that the static
period of the blue robot might have influenced theparticipants a lot.
The results of our study demonstrate the positive effectof perspective taking on altruistic behavior in acooperation task. However, Pierce et al. have shown thattaking the perspective of a competitor might led to moreunethical behaviors [26]. The influence of watching thedifferent robot's view in a more competitive robot task
would be an interesting avenue for future work.

4.Conclusion
In this paper, we investigated how taking the perspectiveof different robots in a robot-altruistic task affected thesocial behavior of participants. Our findings showed thatparticipants watching the help-receiver view exhibited
more altruistic behaviors than those who watched thehelp-provider view to a certain robot. We also found thatwatching two different views of robots in the robotaltruistic task did not result in any behavior changes withregard to the other participant or a specific robot. In future
work, we will apply our study to immersive virtual reality
and clarify the effects of different tasks and relationships.

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