The final publication is available at
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Do Synchronized Multiple Robots Exert Peer Pressure?
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ABSTRACTIn human-human interaction, peer pressure is a major social
influence on people’s thoughts, feelings, and behaviors. The
larger the group of people, the more social influence it
exerts. In this paper, we investigate whether multiple robots
and their synchronized behaviors exert peer pressure on
people, as in human groups. We developed a multiple robot
controller system that enables robots to perform precise
synchronization. In the experiment, we prepared a settingthat resembled previous experiments that investigated peerpressure between people and robots. The participants
answered questions after hearing the robots’ answers, only
some of which were incorrect. Our experiment results
showed that the influence of the synchronized multiple
robots increased the error rates of the participants, but wefound no significant effects toward conformity.
Author Keywords

Human-robot interaction; group interaction; peer pressure
ACM Classification Keywords

H.5.2. User Interfaces – Interaction styles
INTRODUCTION

Human decision making is strongly influenced by others. In
particular, the power of the many is one famous social
influence from others. For example, Sherif investigated aconformity effect in an ambiguous situation and showedthat participants willingly followed the opinions of others,
even though they admitted objections to them [1]. Aschexperimentally investigated the impact of the majority’s
influence and concluded that participants conformed toother people’s incorrect choices [2][3]. Asch also
investigated how peer pressure is strengthened by anincrease in the number of the sources of such influence.
Other literature identified the social influences ofrelationships among people’s thoughts, feelings andbehaviors [4, 5].

Can robots exert a social influence, like peer pressure, on
people? Even though people regard a robot as a different
being from humans, they do view it as a social being [6, 7].
This attitude is helpful for understanding how people areinfluenced by robots. Several researchers investigated theeffects of answering this question in the human-robot
interaction research field and showed that robots influencepeople’s decisions [8, 9]. Even though these research works
showed that people’s decision making is influenced byrobots, the power of such influence is relatively weakcompared to the effect from humans.
Another question must be confronted. Do multiple robots
exert more social influence? Since the practice of installinggroups of robots like Pepper in real shops continues toadvance worldwide, understanding their social influence iscritical. However, the answer to that question remains
unknown because how multiple social robots exert social
influence hasn’t been extensively addressed by the humanrobot interaction research field. Do people change theirdecisions if a number of robots oppose them? Are people
more likely to follow the opinions of unanimous robots?
This paper addressed these questions by investigating theeffects of peer pressure from multiple robots with arelatively large number of robots (Fig. 1). The power of
influence from robots is probably weaker than from humans,and past research reported that the size of the group isimportant to increase conformity [2]. We also investigatedthe effects of the synchronized behaviors of multiple robots,
which are designed to increase conformity by showing
unanimity because past research work reported that it
increases the power of social influence [2].

Fig. 1 Synchronized behaviors of multiple robots

The final publication is available at
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RELATED WORKS
Social influence from a single robot

Many robotics researchers have investigated the social
influence from a single robot in the human-robot interaction
research field with an approach that resembles humancomputer interaction research works [10] [11]. For example,
researchers unveiled the effects of a robot’s social
facilitation, which is the tendency for people to performdifferently in the presence of others (i.e., a robot) than when
they are alone. The existence of physical robots influenced
the performances of people in simple tasks, similar tohuman existence [12] [13].
Nagakawa et al. focused on social influence due to theunique feature of physical robots in the physical interaction
between a robot and people and reported that a robot’s
physical interaction motivates people during simple andmonotonous tasks [8, 9]. Shinozawa et al. clarified thatphysical presence affects human decision making more
greatly than a screen-agent in the real world [14]. Thesesocial influences of robots can be exploited for commercial
purposes, and real robots have already been installed foradvertisements in daily environments like shopping malls[15] [16].
Even though these research works provided rich knowledge
about the social influences of robots on people, they mainly
focused on a single robot effect, not a multiple robot effect.
It remains unknown whether using multiple robots
increases the social influence from robots to people. In thispaper, we unveil the effects of multiple robots under thecontext of social influence.

from robots. The main differences between our research
work and Brandstetter’s work is the number of robots andthe synchronized behaviors.
DESIGN
Task design

In this study, which resembles Brandstetter’s work [20], we
prepared visual judgment tasks along the lines of Asch’s
experiments [2][3] by considering aspects of the Sherif
experiment: ambiguous situations for conformity effects [1].
Fig. 2 shows an example of the line tasks in this study,
where the participants have to identify the correspondinglines from candidates A to C.
On the left side are displayed three lines whose lengths
differ from the correct line in a range of +/- 1 to 3 mm. Oneof the three lines corresponds to a line on the right sidelabeled “?”. The right side line has three different lengths:
50, 100, and 150 mm. Since these lines look ambiguous,
distinguishing them might be difficult for participants.
System design

We referred to both the Asch and Brandstetter experiments
to determine the number of robots for our study. Aschinvestigated the relationships between conformity andgroup size [21]. The former increased immediately when agroup had three people and did not appreciably increase byadding more people (Fig. 3). On the other hand,
Brandstetter’s experiment with four robots showed thatusing more robots over the saturation number in the humancase did not exert peer pressure on people, even in
ambiguous situations [20]. Therefore, at least four robots
were inadequate to create peer pressure from them.

Interaction with a group of robots

Similar to our motivation, Brandstetter et al. investigatedpeer pressure from multiple robots [20] in a reproduction of
Asch’s experiment with four robots or four humanexperimenters. They investigated the levels of pressure
from the robots in both non/ambiguous situations. As in theoriginal experiment, they confirmed peer pressure effects
with human experimenters, but the robots did not show any
significant effects of peer pressure. In this research work,
we also use multiple robots to investigate peer pressure

A

B

C

？

Fig. 2 Example of line task

Conformity

Some research works focus on using multiple robots inhuman-robot interaction contexts. Sakamoto et al.
developed a passive social medium using two robots andinvestigated its effectiveness for information-providing
services in open public environments [17]. Kory et al. used
two robots for a storytelling-task to improve children’s
language-learning [18]. Leite et al. also focused onstorytelling with multiple robots and reported thatinteractive narratives with multiple robots are a promising
approach for the development of children’s social-related
skills [19]. These research works showed the effectivenessof using multiple robots to transfer information to people.
However, since their focuses are different from peerpressure from robots, such research is different from ours.

024
Number of sources of influence

6

Fig. 3 Relationships between conformity and group size,
original graph [21]The final publication is available at
http://dl.acm.org/citation.cfm?id=2974808

User
interface

Timingcontroller

Robot
Robot
Robot
controller
Robot
controller
controller
Robot
controller
Robot
controller
Robot
controller
controller

Display
controller
Fig. 4 System overview

the scenario, and sends the command set and the starttiming to each controller. To avoid influence from the
network delay, each controller is connected to the same
NTP server to synchronize the clocks between PCs.
Robot controller

The robot controller manages a robot’s behavior, which ismotion and speech sets. In advance we prepared robot
behaviors and registered them to each robot controller,
which executes a registered behavior based on the timing
information from the timing controller. When a behavior is
executed, it controls each motor and starts to play a robot’s
sound.
Display controller

After considering these research works, we chose six robots,
which is two more than the saturation number in the humancase. To increase the power of many, we also investigatedthe effects of the synchronized behaviors of robots. Since
past work reported that unanimity increases the power ofsocial influence [2], we believe that showing unanimity bysynchronized behavior will increase the power.
Since the effects of such a large number of robots and theirsynchronized behaviors remain unknown under the context
of peer pressure, our design includes heuristic points. But
we believe that investigating these effects will be helpful tounderstand how people are influenced by interaction with agroup of robots. By considering both the task and systemdesign, we developed an experiment system for this study
and described its details in the next section.

SYSTEM CONFIGURATION

Figure 4 shows an overview of our multiple robot controller
system that consists of the following five components: userinterface, timing controller, robot controller, display
controller, and a robot. The total number of robots wasseven: an MC robot and six answerers. All of the systems
are connected through a wired/wireless LAN. The details ofeach component are described as follows.
User interface

To simultaneously control multiple robots, we developed asimple user interface with several buttons that are used tosend a scenario to the timing controller. Each scenario
includes information about when and which robot will
execute a behavior (motion and speech sets) and shows
images on the display. These scenarios are analyzed by thetiming controller and sent to both the robot and display
controllers. The operator controls the start timing of eachscenario by the user interface, but after sending a scenario,
each robot automatically behaves as defined by the scenario.
Timing controller

This module synchronizes each robot behavior andprecisely shows images on the display. When an operator
sends a scenario to the timing controller, it generates
command sets for each robot, displays images by analyzing

The display controller manages the displayed images andshows an image based on the timing controller’s signal.
Robot

We used “Sota,” which is an interactive humanoid robot
characterized by its humanlike physical expressions. It has
two DOFs in its arms, three in its head, and one in its waist.
Its hands are soft material to ensure safety. The robot is 280
mm tall and is equipped with a CCD camera andmicrophones. Since Sota’s CPU is Edison, users canconnect and control it by Wi-FI. We used a corpus-based
speech synthesis to generate speech [22].

EXPERIMENT
Hypotheses and Prediction

Based on human science literature, the power of math exists
in the context of social influence, e.g., peer pressure [2, 23].
These research works also report that the size of the groupand unanimity increases such social influences. On theother hand, in human-robot interaction research fields, otherkinds of social influences exist, such as social facilitation [8,9], but no peer pressure effect by multiple robots has been
observed yet [20]. To investigate whether multiple robots
can exert peer pressure, we employ more robots than past
research work [20] and synchronized behaviors to increase
unanimity, which is related to the power of peer pressure. If
our system effectively produces synchronized behaviors
among multiple robots, people will feel more pressure thanfrom just non-synchronized multiple robots. This pressure
will probably cause more errors during the experiments andcreate conformity to the robot answers; peer pressure fromrobots will be observed. Based on these considerations, wemade the following predictions:
Prediction 1: The synchronized behaviors of multiple
robots will produce more feelings of pressure during theinteractions than the non-synchronized multiple robots.
Prediction 2: People will make more mistakes and act inconformity with the robot answers when they interact withthe synchronized multiple robots than those who interact
with non-synchronized multiple robots or do not interact
with them at all.

The final publication is available at
http://dl.acm.org/citation.cfm?id=2974808

Environment

Procedure

We conducted an experiment in a laboratory room. Fig. 5
shows a map of the environment. Seven robots (one MCand six answerers) and a display were placed on the desk inroom A. The MC robot is on the right of the display, andthe other six robots are answerers. The operator is placed inroom B, sends start signals to the robots, and monitors theentire system. The participants were placed in front of thedisplay, and the distance between them and the display was
180 cm. We recorded all of the experiment’s data with twocameras and one microphone.

Before the first session, the participants were given a brief
description of our experiment procedure. Since theexperiment had a within-participant design, each participantjoined three sessions of different conditions. We
counterbalanced the order of the conditions, the answer
labels, and the basic lengths of the lines. The participantsfilled out questionnaires after each session.

Room A

Participants

Twenty people (10 women and 10 men, who averaged 35.5
years of age, S.D 9.7) participated in the experiment. In theexperiment, 18 participants made at least one error in all of
the conditions. We targeted these 18 individuals to evaluate
how the robot’s behaviors influenced their answers.
Conditions

We used a within-participant experiment design to evaluate
and compare the effects of multiple robots and theirsynchronization behaviors. In each condition, the number oftrials was 18 and errors on 12, as in Asch’s experiment.

Room B

P

A
A
A
A

A

O

M

A
Display

Sequence condition

This condition reproduces Asch’s experiment with multiple
robots. In the beginning, the MC robot requests that a
participant answer a question after the robot to theparticipant’s right answered it. Each robot looked at thenext answerer after responding to the question (Fig. 6, left;
the fifth robot is answering and the fourth robot is looking
at the fifth robot). Therefore, the five robots first answered
the question in order, then the participant answered it (Fig.
6, right; the participant is answering and the fifth robot islooking at the participant), and finally the sixth robot
answered, too. After the sixth robot answered, the MC robot
asked the next question. The answers of all the robots wereidentical, but they made errors on 12 of the 18 trials.

P Participant O Operator
A Answerer robot M MC robot
Fig. 5 Experiment environment

Synchronization condition

This condition investigated the effects of the synchronizedbehaviors of the robots. Here, the MC robot requested that a
participant answer a question after all the robots had
already answered. However, unlike the sequence condition,
all the robots simultaneously answered the question (Fig. 7,left); they looked at the participant after they answered thequestion in unison. Therefore, first the six robots
simultaneously answered the question, and then theparticipant answered it (Fig. 7, right). The answers of all therobots were the same; they again made errors on 12 of the
18 trials.

Fig. 6 Experiment scenes in sequence condition

Alone condition

This condition investigated the ratio of the correct answersof the participants without the robot’s behaviors. In thiscondition, the MC robot requested that a participant answer
a question alone, and then the MC robot immediately asked
the next question after the participant answered the robot.
During the condition, the other six robots did nothing.

Fig. 7 Experiment scenes in synchronization condition

The final publication is available at
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Measurements

In this experiment, we used a questionnaire to measure on a
1-to-7 point scale one subjective item: the feeling ofpressure from the robots. We also measured two objective
items: the ratio of correct answers when the robots mademistakes and the ratio of conformity to wrong answers.

the other hand, they probably felt less pressure from therobots under the alone condition because only the MC robot
was working. Therefore, we analyzed the correlation
between the perceived pressure and the ratios of the correct
answers to understand the details of their relationship.

RESULTS

7

Verification of Prediction 1

6

Figure 8 shows the questionnaire results of the degree ofpressure. We conducted a paired t-test and found asignificant difference among the conditions (t (1, 17) = 3.03,
p<.01, r=0.59. Prediction 1 was supported.

4
3

2
1

Sequence

Synchronization

Figure 8. The questionnaire result of the degree of pressure

Ratio of correct answers

We also conducted a paired t-test on the ratio of theparticipants’ conformity to the robots’ mistakes toinvestigate whether the participants conformed with them(Fig. 10). The results did not show a significant difference
among the conditions (t (1, 17) = 1.21, p=.24). Prediction 2
was partially supported.

*

5

Verification of Prediction 2

Figure 9 shows the ratios of correct answers when the
robots made mistakes in both the sequence andsynchronization conditions and the alone condition (i.e.,
without robots). First, we conducted a one-factor within
subject ANOVA of the ratios to investigate whether robotpressure caused more errors in the participants. The results
showed a significant difference among the conditions (F (2,34) = 3.26, p=.05, partial η2=.58). Multiple comparisonswith the Bonferroni method revealed a significant trend:
synchronization > alone (p=.07), but no significancebetween sequence and synchronization (p =.51) andsequence and alone (p =.74).

* : p < .01

100++ : p < .10

80
60
40

20
0
Sequence

Synchronization

Alone

Fig. 9 Ratios of correct answers in all conditions

DISCUSSION
Relationship between perceived pressure and mistakes

Here another question arises. Do perceived pressures
influence mistakes? In fact, the participants reported more
feelings of pressure from the robots under thesynchronization condition than the sequence condition. On

100
Ratio of conformation

Even though the experimental results did not show
conformity by synchronizing multiple robots, it showedinteresting phenomenon. Thus, participants make more
mistake under synchronization condition than alone
condition, but did not follow robots’ answers. These results
would suggest that the perceived pressure did not relate to
the power of peer pressure, or the total power of perceived
pressure is not enough to make peer pressure; because theaverage of the perceived pressure at the synchronization
condition is larger than sequence condition but it was lessthan middle (four). If the robots could make more pressures,the latter assumption, i.e., whether the total power ofperceived pressure related to conformity, can be unveiled;this is one of interesting future works.

80
60
40
20
0
Sequence

Synchronization

Fig. 10 Ratios of conformity in both sequence andsynchronization conditions

The final publication is available at
http://dl.acm.org/citation.cfm?id=2974808

Correlation analysis using the Pearson correlation did notreveal any statistical significance in both the sequence
condition (Pearson correlation = -.29, p = .24) and thesynchronization condition (Pearson correlation = -.05, p= .85). This result indicates that the perceived pressure did
not have any correlation to the ratio of the correct answers.
We must investigate other kinds of variables to explainthese phenomena, for example, why the participants mademore errors in the synchronization condition. Even though
our findings did not clearly explain what feelings are
related to the ratio of mistakes, this research provides one
implication. We need a careful behavior design about using
multiple robots because their synchronization behaviors
might cause incorrect human judgments, such as deceptive
advertising uses.

The participants only had limited interaction with desktopsized robots. Thus, the effect shown in the experimentmight be moderated if they interacted with a robot with adifferent appearance or size. Of course, the number ofinteracting robots would also influence the experimentresults; we only used six robots.
Only adults participated in our experiment. If children or
seniors were to interact with our robot, they might perceivea different level of peer pressure from the synchronizedmultiple robots. Also, the effects of pressure from multiple
robots are related to ethical issues. We conducted thisexperiment under an academic context, but we need tocarefully design multiple robot behaviors in real worldcontexts. These limitations will be tested in the future,
perhaps with different kinds of robots or societal contexts.

Why didn’t the participants conform to the robots?

More robots and their synchronization behaviors were stillinsufficient to create conformity among the participants in
our current settings. Here we discuss what factors are
essential to cause people to conform to robots.
First, people perceive robots as different from humans [6,7]; they do not treat them as humans. If a robot were toestablish a rapport or a social relationship with people, their
responses might be different. In this research work we did
not include any interaction design or context to create such
relationships between the robots and the participants.
Another perspective is the authority of the robots. If people
felt great respect for them, their opinions might change.
Geiskkovitch et al. reported that a robot’s authority
influenced people’s behavior [24]. They investigatedwhether participants continued to perform a tedious task
under a robot’s direction by controlling its authority andconcluded that its perceived authority status was morestrongly correlated to obedience.
Robot appearance and size are also related to the power ofpressure. When people face larger or stronger robots, theymight feel physical pressure. In this study, the participants
probably did not experience such pressure. After theexperiments,
severalparticipants
described
thesynchronized robots as cute. We also investigated their
feelings of enjoyment about their interaction with the robots,
and these results showed significant differences between
the sequence and synchronization conditions by paired ttest (p<.01). This results indicated that participants felt
more enjoyment under the synchronization condition than
the sequence condition.
Limitations

Since this study investigated the effects of peer pressure
from multiple robots, we cannot generalize about our
predictions from it. Even though this experiment showedthat pressure effects increased the mistakes of people bysynchronized multiple robots, it still did not show peerpressure. It was also conducted within the framework of an
academic study.

CONCLUSION

In this research work, we focused on the effects of peerpressure from multiple robots. Even though previous
research reported less peer pressure from multiple robots,
the effects of synchronized behaviors that increase pressure
remain unrevealed. To investigate the effects of peerpressure from synchronized multiple robots, we conducted
a within-subjects experiment in which multiple robots
provided incorrect answers under an ambiguous situation.
Our experimental results indicated that synchronizedmultiple robots exerted more pressure on participants, as we
assumed, and increased their error rate more than a situation
where these robots did not provide incorrect answers.
However, the peer pressure effects are not completely clear
from the experiment. We believe that our findings will
provide several implications to the development of robot
applications that simultaneously use multiple robots,
especially for commercial or advertising purposes.

ACKNOWLEDGMENTS

Omitted for anonymized review.

REFERENCES

1.

2.
3.

4.

5.

M. Sherif, “A study of some social factors inperception,” Archives of Psychology (Columbia
University), 1935.
S. E. Asch, “Opinions and social pressure,” Readings
about the social animal, vol. 193, pp. 17-26, 1955.
S. E. Asch, “Effects of group pressure upon themodification and distortion of judgments,” Groups,
leadership, and men. S, pp. 222-236, 1951.
R. W. Spencer and J. H. Huston, “Rational forecasts:
On confirming ambiguity as the mother of conformity,”
Journal of Economic Psychology, vol. 14, no. 4, pp.
697-709, 1993.
V. Griskevicius, N. J. Goldstein, C. R. Mortensen, R.
B. Cialdini, and D. T. Kenrick, “Going along versus

The final publication is available at
http://dl.acm.org/citation.cfm?id=2974808

going alone: when fundamental motives facilitate
strategic (non) conformity,” Journal of personality andsocial psychology, vol. 91, no. 2, pp. 281, 2006.
6. P. H. Kahn Jr, T. Kanda, H. Ishiguro, N. G. Freier, R.
L. Severson, B. T. Gill, J. H. Ruckert, and S. Shen,““Robovie, you'll have to go into the closet now”:
Children's social and moral relationships with ahumanoid robot,” Developmental psychology, vol. 48,
no. 2, pp. 303, 2012.
7. K. Hayashi, M. Shiomi, T. Kanda, and N. Hagita, “AreRobotsAppropriate
forTroublesome
andCommunicative Tasks in a City Environment?,” IEEE
Transactions on Autonomous Mental Development,
vol. 4, no. 2, pp. 150-160, 2012.
8. M. Shiomi, K. Nakagawa, K. Shinozawa, R.
Matsumura, H. Ishiguro, and N. Hagita, “Does A
Robot’s Touch Encourage Human Effort?,”International Journal of Social Robotics, pp. 1-11,
2016.
9. K. Nakagawa, M. Shiomi, K. Shinozawa, R.
Matsumura, H. Ishiguro, and N. Hagita, “Effect of
Robot’s Whispering Behavior on People’s Motivation,”International Journal of Social Robotics, vol. 5, no. 1,
pp. 5-16, 2012.
10. B. Reeves and C. Nass, How people treat computers,
television, and new media like real people and places:
CSLI Publications and Cambridge university press,
1996.
11. B. J. Fogg, “Persuasive technology: using computers tochange what we think and do,” Ubiquity, vol. 2002, no.
December, pp. 5, 2002.
12. S. Woods, K. Dautenhahn, and C. Kaouri, “Is someone
watching me?-consideration of social facilitation
effects in human-robot interaction experiments,” inComputational Intelligence in Robotics andAutomation, 2005. CIRA 2005. Proceedings. 2005
IEEE International Symposium on, pp. 53-60, 2005.
13. N. Riether, F. Hegel, B. Wrede, and G. Horstmann,“Social facilitation with social robots?,” in HumanRobot Interaction (HRI), 2012 7th ACM/IEEEInternational Conference on, pp. 41-47, 2012.
14. K. Shinozawa, F. Naya, J. Yamato, and K. Kogure,“Differences in effect of robot and screen agent
recommendations on human decision-making,”
International Journal of Human-Computer Studies,vol. 62, no. 2, pp. 267-279, 2005.
15. H.-M. Gross, H.-J. Böhme, C. Schröter, S. Mueller, A.
König, C. Martin, M. Merten, and A. Bley, “Shopbot:
Progress in developing an interactive mobile shopping
assistant for everyday use,” in Systems, Man andCybernetics, 2008. SMC 2008. IEEE International
Conference on, pp. 3471-3478, 2008.
16. M. Shiomi, D. Sakamoto, T. Kanda, C. T. Ishi, H.
Ishiguro, and N. Hagita, “Field Trial of a Networked
Robot at a Train Station,” International Journal ofSocial Robotics, vol. 3, no. 1, pp. 27-40, 2010.

17. D. Sakamoto, K. Hayashi, T. Kanda, M. Shiomi, S.
Koizumi, H. Ishiguro, T. Ogasawara, and N. Hagita,“Humanoid Robots as a Broadcasting Communication
Medium in Open Public Spaces,” International Journalof Social Robotics, vol. 1, no. 2, pp. 157-169, 2009.
18. J. Kory and C. Breazeal, “Storytelling with robots:
Learning companions for preschool children's language
development,” in Robot and Human Interactive
Communication, 2014 RO-MAN: The 23rd IEEE
International Symposium on, pp. 643-648, 2014.
19. I. Leite, M. McCoy, M. Lohani, D. Ullman, N.
Salomons, C. K. Stokes, S. Rivers, and B. Scassellati,“Emotional Storytelling in the Classroom: Individual
versus Group Interaction between Children andRobots,” in HRI, pp. 75-82, 2015.
20. J. Brandstetter, P. Racz, C. Beckner, E. B. Sandoval, J.
Hay, and C. Bartneck, “A peer pressure experiment:
Recreation of the Asch conformity experiment withrobots,” in Intelligent Robots and Systems (IROS
2014), 2014 IEEE/RSJ International Conference on,
pp. 1335-1340, 2014.
21. D. Forsyth, Group dynamics: Cengage Learning, 2009.
22. H. Kawai, T. Toda, J. Ni, M. Tsuzaki, and K. Tokuda,“XIMERA: A new TTS from ATR based on corpusbased technologies,” in Fifth ISCA Workshop on
Speech Synthesis, 2004.
23. S. E. Asch, “Effects of group pressure upon themodification and distortion of judgments.” Groups,
leadership, and men S. pp. 222-236, 1951.
24. D. Y. Geiskkovitch, D. Cormier, S. H. Seo, and J. E.
Young, “Please Continue, We Need More Data: An
Exploration of Obedience to Robots,” Journal of
Human-Robot Interaction, vol. 5, no. 1, pp. 82-99,
2015.