Do the Number of Robots and the Participant’s Gender Influence
Conformity Effect from Multiple Robots?
Masahiro Shiomia* and Norihiro Hagitaaa

ATR-IRC, Kyoto, Japan;*corresponding author: Masahiro Shiomi, m-shiomi@atr.jp
Masahiro Shiomi received M. Eng. and Ph.D. degrees in engineering from Osaka University in2004 and 2007. During that same time, he was an intern researcher at the Intelligent Robotics
and Communication Laboratories (IRC). He is currently a group leader in the Agent Interaction
Design department at IRC and at the Advanced Telecommunications Research InstituteInternational (ATR). His research interests include human-robot interaction, social touch,robotics for childcare, networked robots, and field trials.
Norihiro Hagita received B.S., M.S., and Ph.D. degrees in electrical engineering from Keio
University, Japan in 1976, 1978, and 1986. From 1978 to 2001, he was with the Nippon
Telegraph and Telephone Corporation (NTT). He joined the Advanced Telecommunications
Research Institute International (ATR) in 2001 and in 2002 established the ATR MediaInformation Science Laboratories and the ATR Intelligent Robotics and Communication
Laboratories. His current research interests include communication robots, networked robot
systems, interaction media, and pattern recognition. He is a fellow of the Institute ofElectronics, Information, and Communication Engineers, Japan as well as a member of the
Robotics Society of Japan, the Information Processing Society of Japan, and The JapaneseSociety for Artificial Intelligence. He is also a co-chair for the IEEE Technical Committee on
Networked Robots.

Do the Number of Robots and Participant’s Gender Influence
Conformity Effect from Multiple Robots?
The conformity effect is a critical social phenomenon through which one
individual or a particular group influences the thoughts, feelings, or behaviors ofanother person or group. We focused on two essential factors that influence thepower of the conformity effect: the number of people and gender. In this paper,
we investigated whether the number of robots and the gender of the interacting
partner change the conformity effect. We experimentally compared conformity
effects by considering these two factors while participants answered questions
after listening to incorrect answers from two, four, or six robots. The conformity
ratios were significantly different between the six robots and two/four robots andbetween female and male participants.
Keywords: Human-Robot Interaction, Conformity Effect

1. Introduction
Human decision-making is generally influenced by the opinions of others. One
common social influence from others is the power of the many. For example, Sherif
investigated the conformity effect in ambiguous situations and showed that participants
followed the choices of others, even though they admitted having objections to them[1]. Asch reported that participants obeyed other people’s incorrect choices, and thenumber of the others strengthened the peer pressure that indicates conforming to themajority [2, 3]. Spencer et al. and Griskevicius et al. investigated the relationships
between the power of peer pressure and ambiguity and concluded that more of the latter
increased peer pressure [4, 5]. The participant’s gender also influences the conformity
effect in face-to-face situations. Several research works reported that females conform
more than males in face-to-face groups [6, 7], but no such gender effects appeared insituations without face-to-face aspects such as internet-based surveys [8].
In the human-robot interaction research field, researchers have also focused onthe conformity effects caused by multiple robots. For example, Midden et al.
investigated the persuasion of a group of virtual agents with Asch’s paradigm and founda weaker conformity effect under high task difficulties [9], but they did not focus on
physical agents, i.e., robots. Brandstetter et al. investigated the conformity effect frommultiple robots [10] and found no significant effects from a group of four robots, evenfor difficult tasks. Salomons et al. argued that trust relationships between robots andpeople increased the conformity effects from robots [11]. Recently, Vollmer et al.
reported that children conform more than adults to multiple robots [12].
However, even though robotics researchers have broadly investigated suchconformity effects, they have less focused on the effects of the number of robots and theparticipant gender, which are essential factors for conformity in human cases. For
instance, past studies used three or four robots to investigate the effects of the numberof robots but they did not focus on the effects of that number [10-12], even though it isan essential factor in conformity effects [2, 3]. Moreover, these studies did not focus on
the influence of the partner’s gender to conformity effects, even though past studies
suggested that females conform more than malesin face-to-face situations [6, 7].
Due to increased use of multiple robots in real environments, understanding
these influences toward conformity effects will contribute to knowledge building inhuman-robot interaction contexts and how people interact with multiple robots. To
investigate the influence on conformity effects, we prepared a multiple-robot controller
system and prepared visual judgment tasks that reflect past studies that focused on theconformity effect in both human-human [1-3] and human-robot interactions [10, 12]. In
this study, we address the following research questions:- Does the number of robots influence the ratio of conformity?
- Does the participant’s gender influence the ratio of conformity?

Fig. 1 Do multiple robots exert conformity effects?
2. RELATED WORKS
2.1 Conformity Effect in Human-Computer Interaction
A past study reported that people tend to treat computers as social beings [13].
Human decision-making is influenced not only by other people but also by computer
systems and computer agents [12]. Based on this context, Midden et al. compared theconformity effect from multiple computer agents on a human experimenter [9]. Theirexperimental results identified the effects of peer pressure with human experimenters(as in Asch’s experiments), but they reported that computer agents only exerted aconformity effect under high task difficulty. Berns et al. also showed such effects withcomputer agents under the same condition [14]. Rosander et al. concluded that
increasing the task difficulty in computer-mediated communication increased theconformity effect [8].
These studies provided rich knowledge about the conformity effect fromcomputer agents. Based on them, we investigated it from multiple robots with high taskdifficulty.

2.2 Conformity Effect in Human-Robot Interaction
Similar to human-computer interaction research fields, robotics researchers haveinvestigated the conformity effect from multiple robots. For example, Brandstetter et al.
investigated it from multiple robots [10] (in their reproduction of Asch’s experiment)
and thoroughly compared the power of conformity effects between robots and humans
with both low/high task difficulties. As in Asch’s experiment, their experimental results
showed peer pressure effects with human confederates in both task difficulties, although
in their study the robots did not cause any significant conformity effect in either low orhigh task difficulties [10].
However, recent related works argued that robots show a significant conformity
effect in specific situations. For example, Salomons et al. investigated the effects oftrust between robots and adults toward conformity effects and concluded that people
conform more to robots when they trust them [11]. Another study investigatedconformity effects with children and reported that they conform more than adults tomultiple robots [12].
These studies provided valuable knowledge about conformity effects frommultiple robots, but several essential factors (i.e., the number of robots and interacting
partner’s gender) remain insufficiently investigated.

3. Experiment Design
3.1 Task Design
In this study, we prepared visual judgment tasks based on Asch’s experiments[2, 3]. However, unlike Asch’s task, we considered aspects of Sherif’s experiment, i.e.,ambiguous situations for conformity effects [1], similar to past studies that focused on
the conformity effect from multiple computer agents or robots [9, 10]. Fig. 2 shows anexample of the line tasks in this study, where the participants identified thecorresponding line from candidates A to C from a single candidate.
On the left are displayed three lines whose lengths differ from the correct line
(labeled “?”) in a range of +/- 1 to 3 mm. Three different choices of correct line lengths
are offered: 50, 100, and 150 mm. Note that the difference of the line length was 1 inch
(254 mm) in the original experiment. Therefore, since the line differences in our study
are more ambiguous, distinguishing among them is often more difficult than in theoriginal settings. The number of total trials was 18, which is identical to the original
experiments [2, 3]. We used different line lengths because past studies [9, 10] alreadyshowed that using multiple robots and the same line lengths from the original study (aslow task difficulties) did not cause conformity effects; using different and ambiguousline lengths (as high task difficulties) might also cause conformity effects. Therefore, toinvestigate the conformity effects with multiple robots, we used different line lengths
than in the original study. Since the past study [10] did not provide any details of its
ambiguous line settings, we heuristically adjusted the line lengths to increase the taskdifficulty.

3.2 System Design
To investigate the conformity effect from multiple robots, we must decide thenumber of robots for our experiment. In human-human interaction literature, Aschinvestigated the relationships between conformity and group size [15] and found thatthe conformity effect increased when a group had three people and did not appreciably
increase by adding more people. However, in human-robot interaction literature,
Brandstetter et al. argued that four robots did not exert a conformity effect on people,
even in high task difficulties [10]. Based on these considerations, we heuristically chosetwo, four, and six robots as responders. By considering both task and system design, wedeveloped an experimental system and describe its details in the next section.

A

B

？

C
Fig. 2 Example of line task
User
interface

Timingcontroller

Robot
controller

Display
controller

Fig. 3 System overview
3.3 System Configuration
Figure 3 overviews our multiple-robot controller system that consists of thefollowing five components: a user interface to control multiple robots, a timing
controller for each robot behavior, a robot controller that manages the robot’s motions
and utterances, a display controller to show images, and a robot. All of the systems wereconnected through a wired LAN. To avoid network delays, each system was connectedto the same NTP server to synchronize the clocks among the PCs.
For this study, we used Sota, a table-top-sized interactive humanoid robot,
which has eight DOFs: three in its head, one for each shoulder and each elbow, and one
for its base. It is 28 cm tall and has a voice synthesis. Each robot has a slightly different
voice for simpler differentiation; the LED on its mouth blinks to indicate speaking
based on the sound level.

4. Experiment
4.1 Hypotheses and Predictions
Human decision-making is often influenced by a conformity effect that isexerted by others [2, 16]. To investigate whether multiple robots can exert it, we used a
different number of robots compared to a past study [10] to investigate whether
additional robots might intensify the conformity effect and create conformity to theanswers from the robots.
Moreover, in face-to-face situations, participant gender is an essential factor inconformity effects. Past studies reported that females conform more than males in faceto-face situations [7] [8]. Interaction with robots in the same physical environment
might cause similar reactions from female. Based on these considerations, we made thefollowing predictions:
Prediction 1: The ratio of conformity will increase based on the number ofrobots.
Prediction 2: Female participants will conform more to the robots than maleparticipants.

4.2 Environment
We conducted our experiment in a laboratory room (Fig. 4). A maximum ofseven robots (one facilitator robot and six respondent robots) and a display were placedon a desk in room A. The facilitator robot is on the display’s right, and the placement of
the six respondent robots is shown in the figure. From room B, the operator sends startsignals to the robots. The participants sat 180 cm in front of the display. We managed
the experiment through the facilitator robot and recorded all of the experiment’s datawith two cameras and two microphones.

4.3 Conditions
We prepared three conditions to evaluate and compare the effects of multiple
robots: two-, four- and six-robot conditions. In all the conditions, we replicated Asch’s
experiment with multiple robots. In the beginning, the facilitator robot requested that a
participant answer a question after an answer was given by the robot to the participant’s
right. After responding to the question, each robot looked at the next respondent (Fig. 5,
left, where the fifth robot is answering and the fourth is looking at it in the six-robot
condition). The second to the last robot answered the question, and then the participant
answered (Fig. 5, right, where the participant is answering and the fifth robot is looking
at the participant in the six-robot condition), and finally the last robot answered, too.
After all the robots have answered, the facilitator robot asked the next question. The
answers of all the robots were identical, but they provided incorrect answers in 12 of 18
trials.
Note that in the two-robot condition we only used the 5th and 6th robots as
respondents in Fig. 4, and in the four-robot condition we only used the 1st, 3rd, 5th, and6th robots as respondents. We removed the rest of robots in the two- and four-robot
conditions.
Room A

Room B

P
R 5th
R 4th
R 3rd
R 2nd
R 1st

6th R

O

F

Display

P Participant

O Operator

R Respondent F Facilitator

Fig. 4 Experimental environment in six-robot condition

Fig. 5 Experiment scenes in six-robot condition
4.4 Participants
Fifty-four people (27 female and 27 male whose average age was 31.39, S.D
9.82) participated in the experiment. None had previously interacted with our robots.

4.5 Procedure
Before the experiment, the participants were given a brief description of its
procedure. Since this experiment had a between-participant design, the participants
were assigned to one of the three robot situations: two-, four- or six-robot conditions.
The participants filled out questionnaires after the task. This research was approved by
our institution’s ethics committee for studies with human participants. Written,
informed consent was obtained from all of them.

4.6 Measurements
In this experiment, we calculated both the objective and subjective
measurements. For the former, we measured the conformity ratio when their answers
were incorrect. We also measured two items for the subjective measurements asadditional evaluations: the degree of their confidence in their own answers and thedegree of influence exerted on their choices by the robot answers. For these subjective
measurements, we used a questionnaire with a 1-to-7 point scale.

5 Results
5.1 Verification of Predictions
Figure 6 shows the conformity ratios when the robot answers were incorrect. We
conducted a two-factor ANOVA with two between-subject factors: the number ofrobots (two, four, or six) and the participant’s gender (male or female). There weresignificant differences in the number factor (F(2, 48)=6.906, p=.002, partial η2=.223)
and the gender factor (F(2, 48)=7.754, p=.008, partial η2=.139). There were no
significant differences in their interaction (F(2, 48)=1.451, p=.244, partial η2=.057).
Multiple comparisons of the number factor with the Bonferroni method showedsignificant differences between the six- and four-robot conditions (p=.003) and the sixand two-robot conditions (p=.020). We identified no significant difference between the
two and four conditions (p=1.000).
Moreover, we investigated the correlation between the number of robots and theconformity ratios when the robot answers were incorrect. There was a positive
correlation between them (r=0.334, p=.014), which showed a weak positive correlation.
Thus, prediction 1 was partially supported; i.e., the conformity ratio from six
robots was significantly larger than two or four robots. But the ratios between two andfour robots were not significantly different, even though the results showed a weak
positive correlation between the number of robots and the conformity ratios when the
robot answers were incorrect. Prediction 2 was supported; females conformed to therobots more than males.

* : p < .01**

Conformity ratio

100
80
60
40

*

20
0
two

four
Males

six
FemalesFig. 6 Conformity ratio when robot answers were incorrect
5.2 Additional Evaluation: Questionnaire Results
Figure 7 shows the degree of the confidence of the participants in their own
answers. We conducted a two-factor ANOVA with two between-subject factors: thenumber of robots (two, four, or six) and gender (male or female). There was asignificant difference in the gender factor (F(1, 48)=6.673, p=.013, partial η2=.122).
There were no significant differences in the number factor (F(2, 48)=.027, p=.973,
partial η2=.001) or their interaction (F(2, 48)=1.016, p=.370, partial η2=.041).
Figure 8 shows the degree of influence from the robot answers on the participant
choices. We conducted a two-factor ANOVA as a dependent variable, with the numberof robots (two, four, or six) and participant gender (male or female) as a between-subject
factor. There was a significant difference in the gender factor (F(1, 48)=4.574, p=.038,
partial η2=.087). There were no significant differences in the number factor (F(2,48)=.595 p=.556, partial η2=.024) or their interaction (F(2, 48)=.015, p=.985, partialη2=.001).

Therefore, in this experiment, the participant gender significantly influenced
their subjective feelings about their confidence in their own answers and theirimpressions of the robots. The male showed higher confidence and less influence than
the female in this study.

* : p < .01

7
6
5
4
3
2
1

*

two

four
Males

six
FemalesFig. 7 Degree of confidence

7
6
5
4
3
2
1

* : p < .01

*

two

four
Males

six
FemalesFig. 8 Degree of influence from robot

6 Discussion
6.1 Implication
Our results suggest that people’s choices are indeed affected by the number ofrobots. Similar to a past study [10], using four robots (or two) did not show any
significant conformity effects, but the participants in our study conformed more to the
choices of six robots than of two or four robots. Males showed the lowest conformity tofour robots, and we found a positive weak correlation between the number of robots andthe conformity ratio. But our statistical results between two and four robots did notshow any significant difference in multiple comparisons.
Our results also showed that females conformed to the robots more than males,
which resembles past studies [7, 8]. Questionnaire results also suggested that female felt
more pressure from multiple robots than male regardless of the number of robots.
These results provide one clear implication. For a behavior design with multiplesocial robots, scrutiny is required because the behaviors of multiple robots might
increase human susceptibility to deceptive advertising practices. Understanding othereffects of the behaviors of multiple robots on human decision-making is also needed toreduce incorrect human judgments.

6.2 Effects of Number of Robots
Does increasing the number of robots also linearly strengthen the conformity
effect? In human science literature, an increase over three persons does not largely
promote conformity. In human-robot interaction situations, however, our study showedthat six robots did exert an conformity effect, even though a past study reported thatfour did not [10]. Therefore, investigating the effects of different numbers of robots(e.g., eight or ten) is another future work of this study. Using more robots might
increase the intensity of the conformity effect because our results identified a weak

positive correlation between the number of robots and the conformity ratio; but ofcourse, the conformity effects caused by the robots will eventually reach saturation. One
intriguing future work is to identify the saturation number in the robot case, which was
not covered in this study.
Related to the effects of the number of robots, another future work will
investigate whether the number of computer agents causes a conformity effect. A past
study concluded that four computer agents exerted a conformity effect under high taskdifficulties [9]. Increasing the number of computers might increase the conformity
effect’s power.

6.3 Appearance and Voice Effects
In this study, we used identical robots with slightly different voices. However, arobot’s appearance and its voice are other factors related to the conformity effect. For
example, a past study showed that a large robot creates more anxiety in people than asmall robot [17]. Another study concluded that more humanlike robots might exert a
different conformity effect since groups of humanlike robots are perceived morepositively than groups of machine-like robots [18]. Moreover, a robot’s gender,
including appearance and voice differences, also changes the impression of theinteracting partners [19]. Siegel et al. reported that people tended to rate a robot of the
opposite sex as more credible, trustworthy, and engaging. These studies suggest thatusing a different appearance and voice matching a robot’s appearance might differently
affect human decision-making.

6.4 Personal and Group Relationship Effects
Another factor is the relationship between robots and participants, including
previous familiarity, personal and group relationships. Even if people perceive robots

differently and don’t treat them as human beings [20, 21], several studies successfully
constructed friendly relationships between humans and social robots through long-term
interaction [22, 23]. If a robot established a rapport or a social relationship with people,
their responses might be different even if the robot’s answers are incorrect.
Investigating the effects of such personal/group relationships on the conformity effect isanother future approach.

6.5 Limitations
Since this study investigated the conformity effect from multiple robots, ourpredictions cannot be generalized. Even though we showed pressure effects frommultiple robots, our work was conducted within the framework of an academic study
with an artificial task with relatively few participants.
The participants had limited interaction with our desktop-sized robots. Thus, theeffect shown in our experiment might be moderated if they interacted with a robot witha different appearance or size. Of course, the number of interacting robots also
influences the experiment results as described in the section 6.2. Moreover, we did notinvestigate the previous familiarity of the participants with robots, it also has influences
to their responses as described in the section 6.3.

7 Conclusion
We reported the influence of the conformity effect by multiple robots byfocusing on the number of robots and the participants’ gender. To investigate such
effects from multiple robots, we conducted an experiment in which multiple robots(two/four/six) provided correct or incorrect answers under an ambiguous situation. Our
experimental results indicated that the participants conformed more to the choices of six
robots than two or four. Moreover, females conformed to the robots’ choices more thanmales regardless of the number of robots.

Acknowledgements
This research work was supported by JSPS KAKENHI Grant Numbers
JP18H03311.

References:[1] M. Sherif, “A study of some social factors in perception,” Archives of Psychology(Columbia University), 1935.
[2] S. E. Asch, “Opinions and Social Pressure,” Scientific American, vol. 193, pp. 1726, 1955.
[3] S. E. Asch, “Effects of group pressure upon the modification and distortion of
judgments,” Groups, leadership, and men. pp. 222-236, 1951.
[4] 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.
[5] V. Griskevicius, N. J. Goldstein, C. R. Mortensen, R. B. Cialdini, and D. T.
Kenrick, “Going along versus going alone: when fundamental motives facilitate
strategic (non) conformity,” Journal of personality and social psychology, vol.
91, no. 2, pp. 281, 2006.
[6] A. H. Eagly, Sex differences in social behavior: A social-role interpretation:
Psychology Press, 2013.
[7] C. Leaper and M. M. Ayres, “A meta-analytic review of gender variations in adults'
language use: Talkativeness, affiliative speech, and assertive speech,”
Personality and Social Psychology Review, vol. 11, no. 4, pp. 328-363, 2007.
[8] M. Rosander and O. Eriksson, “Conformity on the Internet–The role of taskdifficulty and gender differences,” Computers in Human Behavior, vol. 28, no.
5, pp. 1587-1595, 2012.
[9] C. Midden, J. Ham, and J. Baten, "Conforming to an Artificial Majority: Persuasive
Effects of a Group of Artificial Agents," Persuasive Technology: 10th
International Conference, PERSUASIVE 2015, Chicago, IL, USA, June 3-5,
2015, Proceedings, T. MacTavish and S. Basapur, eds., pp. 229-240, Cham:
Springer International Publishing, 2015.
[10] J. Brandstetter, P. Racz, C. Beckner, E. B. Sandoval, J. Hay, and C. Bartneck, “Apeer 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.
[11] N. Salomons, M. v. d. Linden, S. S. Sebo, and B. Scassellati, “Humans Conformto Robots: Disambiguating Trust, Truth, and Conformity,” in Proceedings of the2018 ACM/IEEE International Conference on Human-Robot Interaction,
Chicago, IL, USA, pp. 187-195, 2018.
[12] A.-L. Vollmer, R. Read, D. Trippas, and T. Belpaeme, “Children conform, adults
resist: A robot group induced peer pressure on normative social conformity,”
Science Robotics, vol. 3, no. 21, 2018.

[13] 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.
[14] G. S. Berns, J. Chappelow, C. F. Zink, G. Pagnoni, M. E. Martin-Skurski, and J.
Richards, “Neurobiological correlates of social conformity and independence
during mental rotation,” Biological psychiatry, vol. 58, no. 3, pp. 245-253, 2005.
[15] D. Forsyth, Group dynamics: Cengage Learning, 2009.
[16] S. E. Asch, “Effects of group pressure upon the modification and distortion of
judgments.”[17] Y. Hiroi and A. Ito, “Influence of the Size Factor of a Mobile Robot Moving
Toward a Human on Subjective Acceptable Distance” Mobile Robots - Current
Trends, Intech, 2011.
[18] M. R. Fraune, S. Sherrin, S. Sabanović, and E. R. Smith, “Rabble of robots effects:
Number and type of robots modulates attitudes, emotions, and stereotypes,” inProceedings of the Tenth Annual ACM/IEEE International Conference on
Human-Robot Interaction, pp. 109-116, 2015.
[19] M. Siegel, C. Breazeal, and M. I. Norton, “Persuasive robotics: The influence ofrobot gender on human behavior,” in Intelligent Robots and Systems, 2009.
IROS 2009. IEEE/RSJ International Conference on, pp. 2563-2568, 2009.
[20] 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 a humanoid robot,”
Developmental psychology, vol. 48, no. 2, pp. 303, 2012.
[21] K. Hayashi, M. Shiomi, T. Kanda, and N. Hagita, “Are Robots Appropriate forTroublesome and Communicative Tasks in a City Environment?,” IEEE
Transactions on Autonomous Mental Development, vol. 4, no. 2, pp. 150-160,
2012.
[22] T. Kanda, M. Shiomi, Z. Miyashita, H. Ishiguro, and N. Hagita, “Acommunication robot in a shopping mall,” Robotics, IEEE Transactions on, vol.
26, no. 5, pp. 897-913, 2010.
[23] M. Shiomi, T. Kanda, I. Howley, K. Hayashi, and N. Hagita, “Can a Social Robot
Stimulate Science Curiosity in Classrooms?,” International Journal of Social
Robotics, vol. 7, no. 5, pp. 641-652, 2015.