HAI シンポジウム 2017
Human-Agent Interaction Symposium 2017

P-5

Color Psychology Based Approach for Aﬀective
Human-Robot Interaction
Sichao SONG1∗
1

Seiji YAMADA1,2

The Graduate University for Advanced Studies (SOKENDAI), Department of Informatics
2
National Institute of Informatics
Abstract: Humans often find it hard to understand behavior and intention of appearanceconstrained robots. Hence, there is a significant challenge in finding eﬀective methods for suchrobots to express themselves. In this paper, we investigate expressive light as an aﬀective interaction modality and evaluate its eﬀect on humanś perception and interpretation of a Roomba
robot. By using the lights, we are enabling the robot to modify its appearance as a method ofcommunicating with people and, moreover, to assist users in forming correct conceptual model ofthe robot. To explore this, we first survey color psychology theories with regard to color and its
eﬀect on human psychological functioning to support our design on expressive lights. We find thattwo particular colors, red and green, have strong but contrary eﬀects on humans. On the basis ofsuch theoretical groundings, we conduct three experiments and find significant eﬀects of expressive
lights on peopleś perceptions and interpretations of a robot.

1

INTRODUCTION

A large number of robots currently in use for applications such as law enforcement, search and rescue,and domestic uses (such as cleaning robots) are neither anthropomorphic nor zoomorphic [22]. When we
first encounter such robots, the lack of appropriate
mental models and knowledge with regard to theserobots can lead to unsmooth or even failed interaction [20]. In addition, such robots are generally constrained in appearance, meaning that they are designed to be functional and lack expressive faces andbodies [21]. Therefore, there is a significant challenge
in finding eﬀective ways for these robots to successfully interact with human users.
Due to their lack of natural interaction methods,
appearance-constrained robots have to make use of
their physical bodies and mobility to communicate
with people. Existing approaches focus mainly on
motion cues [25, 10] or body posture [9, 8, 24]. For
instance, Saerbeck and Bartneck [25] found a strong
relationship between motion parameters (accelerationand curvature) and attribution of aﬀect. Specifically,
they discovered that the level of acceleration can beassociated with perceived arousal and that valence information is partly encoded in combinations of ac∗ @ E-mail: sichaos@nii.ac.jp

celeration and curvature. Unfortunately, such approaches suﬀer from low expressibility and are hard, if
not impossible, to apply in many application scenarios. For example, in a scenario where space is limited,e.g., a crowded room or a narrow corridor, big movements such as those made through accelerating andmoving in an arc can be impossible to employ.
To address this issue, we investigate an alternative modality: expressive light. Compared with othermodalities, expressive light can oﬀer a broader design space, is less intrusive, and is less restricted tospatiotemporal limitations [18]. By using expressive
lights, we enable a robot to modify its appearance asa means of communicating with people. We presume
that such lights can serve as a cognitive aﬀordance[30]1
to assist users in forming correct mental models, thus
allowing them to interpret a robot’s true intentions.
Unfortunately till now, it remains unclear how HRI
researchers can take full advantage of expressive light
as a primary modality to support aﬀective humanrobot interaction. Although some studies have utilized color and light as supplementary or redundant
modalities to express aﬀect, e.g., [20, 23], there has
been little research on expressive light as a primarymodality, especially for appearance-constrained robots.
1 The term “cognitive aﬀordance” as defined by Hartson is
the same as Norman’s perceived aﬀordance.

Therefore, whether and, in particular, how expressive
light can aﬀect a human’s perception towards a robot
remains a significant research question.
In this work, we investigate the eﬀects of expressive lights on a human’s perception and interpretation of a Roomba robot’s behavior and intentions. We
first survey color psychology theories with regard tocolor and its eﬀect on human psychological functioning. We find that two particular powerful colors, redand green, have contrary eﬀects on humans as red
carries negative meanings and can induce avoidancelike behaviors in people, whereas green carries positive meanings and can induce approach-like ones. On
the basis of such theoretical groundings, we perform
a series of three experiments (two preliminary studies
and one employing the Roomba robot). Our findings
reveal the significant eﬀects of the expressive lights
as the perception and interpretation of the robot’s
intention formed by the experiment participants significantly diﬀered between the diﬀerent two expressive lights. This suggests a novel way of thinking
about eﬀective HRI design, especially for appearanceconstrained robots.

2

COLOR AND PSYCHOLOGICAL FUNCTIONING

Color is one of the most ubiquitous phenomena inhuman experience as it is perceived on essentially every object that we view [15]. Although research oncolor psychology is still at a nascent stage, color psychologists have intensively investigated various aspectsof color, including color vision, color symbolism andassociation, and color eﬀects on psychological and biological functioning [15]. Elliot and Maier [17] reviewed
both theoretical and empirical work that investigatedthe eﬀects of perceiving color on psychological functioning in humans. Their work clearly shows thatcolor can carry important meaning and can have asignificant impact on people’s aﬀect, cognition, andbehavior.
Red has been shown to be a critical color and has
thus garnered the majority of research attention. Manythings in biology, culture, and language point to thepoignancy and prominence of red [17]. Red is thecolor of blood, and dynamic variations in visible bloodflow on the face and body can indicate fear, arousal,
anger, and aggression [6]. Red is used in aposematic(warning) signals by many poisonous insects and reptiles [26]. Red is also a term that appears in almost
all lexicons and, moreover, in many sayings such as“in the red.” Besides red, a few other colors, particularly green and blue, have been intensively studied
as well. They both have positive links in the natural
realm, for example, green foliage and vegetation andblue sky and ocean [17].
Hue color associations have been an active research
topic in psychology [15]. The associative learning theory suggests that the formation and activation of color
associations can be understood through models of semantic memory, and a number of previous studies
have provided empirical evidence of color-emotion associations and psychological functioning [14]. Specifically, color meanings can be grounded in two basic
sources: learned associations that develop from repeated pairings of colors with particular concepts orexperiences and biologically based proclivities to respond to particular colors in particular ways in particular situations [16]. For instance, a specific red-danger
association can be generated from experiences withregard to (life-threatening) situations such as viewing
blood, an angry face, traﬃc lights, and/or warning
signals and sirens [17]. Similarly, green can be associated with positive meanings, e.g., approach andpleasure, due to experiences with green traﬃc lights
and the general image of being the color of nature,and blue can be associated with sadness due to thesaying “I feel blue.”
In addition, viewing a color can influence psychological functioning and foster motivational and behavioral processes, such as approach and avoidance
tendencies [15]. Specifically, colors that carry a positive meaning can produce approach-like responses,
whereas those that carry a negative meaning can produce avoidance-like responses. Therefore, it is reasonable to presume that red can induce avoidancelike behaviors in humans, whereas green can induce
approach-like ones.

3

EXPRESSIVE LIGHT DESIGNOn the basis of the above survey on color psychology and related work, we decided to focus mainly on
two colors: green and red. They are two intensively
studied colors and, moreover, they produce opposite
eﬀects on human psychological functioning. Basically,green can be associated with weak and positive aﬀectExpressive light
GL
RH
none

表 1: Summary of two expressive lights used in all three experiments
Color Waveform Intensity
Eﬀect
green sinusoidal
weak
approach-like (positive) behavior/interpretation
red
rectangle
strongavoidance-like (negative) behavior/interpretation-

4

COMPUTER AGENT WITH
LED

Till now, it remains unclear how expressive lights
can aﬀect human perception and behavior. To explore this research question, we first performed twogames that require certain game strategies, the ultimatum game and give-some game, as a preliminary
study. We built a computer agent with the capability
to display LED light expressions so that people canjudge the agent’s character with the help of expressive light cues. Due to its familiarity to most modern
humans and its neutral shape (non-anthropomorphic
and machine-like), we considered the computer monitor to be the ideal platform for exploring the eﬀects ofexpressive lights on humans perception, psychological
functioning, and their behaviors, e.g., game strategy
and decision making. A robot platform was tested on

Processing
Game Interface

LED Light
Hardwareand induce approach-like behaviors in a human. In
comparison, red can be associated with strong andnegative aﬀect and induce avoidance-like behaviors.
Besides color, two more parameters, waveform andintensity (frequency), needed to be decided to design
expressive light patterns. In particular, Terada et al.
[34] studied color and dynamic parameters for representing emotions. They found that a rectangular
waveform with a high frequency represents intense
emotions, while a sinusoidal waveform with a low frequency represents weak (low intensity) emotions. On
the basis of their work, we decided to combine a sinusoidal waveform and a low frequency with green
to enhance the eﬀect of the color green. Similarly,
we combined a rectangular waveform and a high frequency with red to enhance the eﬀect of the color red.
Table 1 summarizes the two expressive lights. We
presumed that the green and low-intensity (GL) expressive light would be perceived positively and could
induce approach-like behaviors, while the red and highintensity (RH) expressive light would be perceived
negatively and could induce avoidance-like behaviors.

Animation
control

Arduino Uno R3

5V 10A AC adapter

図 1: System overview and experiment configuration

in a later stage on the basis of the findings of the twogame experiments.
Figure 1 shows an overview of the system (above)and the experimental environment (below). A notebook PC was used to run the game programs developed in Processing. A monitor was connected to the
PC to display the game contents, which were shown infull screen. The PC was hidden behind the monitor.
In these two experiments, we used the same Adafruit
NeoPixel LED strip2 . The LED strip was controlled
by an Arduino Uno R3 board and powered by a 5V, 10-A AC adapter. The adapter and board were
hidden behind the monitor during the experiments.
Besides the game data, we also designed a postgame questionnaire to investigate the subjective perception of the participants on the LED expressive
lights.

4.1

Discussion

The two experiments revealed interesting findings
regarding the eﬀect of expressive lights on human perception and behavior. The participants had positive
impressions of the computer agent when it showedgreen and low-intensity triangular expressive light but
2 We used a half meter of the LED strip (0.5 m, 72 pixels)
to match the width of the monitor.

表 2: List of adjectives used by participants to describe expressive lightsGLfriendly, calm,
gentle, smiling,
beautiful, kind,
alive

RH
angry, oppressive,
feeling of tension,
warning,challenging,
dangerous

battery bankArduino Uno R3

none

normalnegative impressions when it showed red and highintensity rectangular expressive light. Moreover, theyfurther displayed approach-like behaviors by showing
a higher tolerance toward unfair oﬀers (the ultimatumgame) and behaving more cooperatively (the givesome game) when the computer agent showed GL expressive light. Similarly, they displayed avoidance-likebehaviors by showing a lower tolerance toward unfair
oﬀers and behaving less cooperatively (selfish) when
the computer agent showed RH expressive light. In
addition, an analysis of the post-experiment questionnaires confirmed these results, as indicated by the participants using positive adjectives such as “friendly”
and “kind” to describe the computer agent when it
showed GL expressive light and negative adjectives
such as “angry” and “oppressive” when it showed RH
expressive light.

Neopixel LED strip

図 2: Configuration of Roomba robot with LED lighting system

strip and board were powered by a 5-V, 3-A portable
powerbank. The same board was also used to control
the movements of the robot. iRobot Create 2 robot
provides the Roomba Open Interface (OI) [2], which isa software interface for controlling and manipulating
Roomba’s behavior.

5.1

Procedure

We designed two practical HRI scenarios, corridor
and corner, which can be common for an indoor autonomous robot, e.g., [31, 18]. To be specific, theRoomba robot moved along a narrow corridor (corridor scenario) or approached a corner of the corridor
5 ROOMBA WITH LED(corner scenario). In the two cases, the robot encounThe preliminary experiments showed the general eftered a person and stopped before it ran into the perfects of expressive lights on humans. Next, we focusedson. While stopped, the robot further showed theon specific human-robot interaction scenarios. We
green and low-intensity triangular expressive light,
wanted to verify whether our findings were still valid
red and high-intensity rectangular expressive light, orand applicable to social robots (appearance-constrained simply no expressive light. We presumed that the
robots in particular). Therefore, we further conductedrobot’s intention, i.e., why it stopped, in such scenaran experiment in which we installed an LED lightios would be ambiguous and thus hard to interpret if
ing system on an iRobot Create 2 robot. Roombano additional cues were provided from the robot, andis a series of autonomous robotic vacuum cleaners
we presumed that the added expressive lights could
used in indoor environments. All Roomba robots are
significantly aﬀect people’s perception and interpredisc-shaped, 34 cm in diameter, and less than 9 cm
tation of the robot and its intentions.
in height [3]. It perfectly fits the definition of an
To make claims about the generality of the experappearance-constrained robot and has very limited
imental results, it was important to recruit a large
ways of expressing aﬀect, e.g., moving forward/backward and diverse set of participants. To achieve this, weand spinning.
employed a Japanese online crowdsourcing platform3
Figure 2 shows the configuration of the Roomba
to recruit participants. As indicated by others rerobot with LED lighting. We used one meter of thecently, e.g., [13, 33], using a crowd-sourced approach
NeoPixel LED strip (60 pixels). The LED strip was
3 Fastask: https://www.fast-ask.com. The website is only
controlled by an Arduino Uno R3 board, and both theavailable in Japanese.

表 3: Seven statements used in experiment. P1∼P4
are statements on perceiving robot, and I1∼I3 arestatements on interpreting robot’s behavior and intentions.
P1 This robot looks friendly.
P2 This robot look aggressive.
It is hard to understand what the robot wants to
P3do.
P4 I like this robot.
This robot wanted the person to move away as it
I1 was blocked by him. (corridor scenario)
This robot expressed displeasure toward the person as it was blocked by him. (corner scenario)(a) corridor_none(b) corridor_GL(c) corridor_RH(d) corner_none(e) corner_GL(f) corner_RH

This robot approached the person and greeted
I2 him. (corridor scenario)
This robot apologized to the person as it almost
ran into him. (corner scenario)
I3

This robot suddenly had some kind of malfunction.

is powerful. It allowed us to rapidly and inexpensively
gather data from many more subjects than would
have been practical using other approaches. In addition, data integrity can be guaranteed by applying
certain reliability-check methods. Participants were
dropped if their answers had near-zero variances, e.g.,
all 4’s. In total, 17 of them were discarded, leaving
data from 204 participants (71 female, Mage =50.21,
SDage =13.14). All participants were native Japanese
speakers.
We used video recordings to represent the two HRI
scenarios in online surveys. For each scenario, we
prepared one synthetic video including all three conditions: the robot showing GL expressive light, therobot showing RH expressive light, and the robot notshowing any expressive light. Figure 3 shows screenshots of each video clip. The order of the three video
clips for each scenario was randomized. For each synthetic video, we provided seven statements (see Table 3) in which four of them (P1∼P4) were used to
examine the participants’ perception of the robot andthe remaining three (I1∼I4) were used to evaluate
the participants’ interpretation of the robot’s intention. The experiment had a 3 (expressive light: GL
vs. RH vs. none) within-participant design. Participants viewed the two synthetic videos separately.
For each statement, the participants were asked tochoose the robot presented in one of the three condi-

図 3: Screenshots of each video clip (condition)

tions that best fit the statements, e.g., “This robot
looks friendly.” The selection rate (SR), indicating
how many participants matched a robot to a particular statement, was counted for each of the three conditions. Because the total number of participants was
204, the value of the selection count ranged from 0 to
204.

5.2

Results

A Pearson’s chi-square test was conducted to determine the eﬀect of the independent factor (expressive light) on the questionnaire items (seven questions) as dependent factors. Post-hoc binomial tests
with Holm’s correction were done as follow-up tests
if significant diﬀerence was found. Because of thethree conditions, the hypothesized probability thateach condition would be chosen at random regarding a statement was set to one-third (33.33%, whichis the probability of a random guess).

5.2.1

Perception

Figure 4 illustrates the selection rates with regard
to the participants’ perception of the robot. The mark“***” stands for p < 0.001, which indicates that the(p < 0.001) scenarios. The post-hoc tests suggest that
selection rate for the corresponding item is signifithe robot showing RH expressive light was selected as
cantly above 33.33%.
most fitting the statement “This robot wanted the
Friendly: Significant diﬀerence was found with reperson to move away as it was blocked by him” ingard to both the corridor (p < 0.001) and corner
the corridor scenario (significantly above 33.33%, p <(p < 0.001) scenarios. The post-hoc tests suggested
0.001) and “This robot expressed displeasure towardthat the robot that showed GL expressive light was sethe person as it was blocked by him” in the corner
lected as most fitting the statement “This robot looks
friendly” in both the corridor (significantly above 33.33%, scenario (significantly above 33.33%, p < 0.001). The
selection rates for the remaining two conditions werep < 0.001) and corner (significantly above 33.33%,
all significantly below 33.33% (p < 0.001) for bothp < 0.001) scenarios. The selection rates for the rescenarios.
maining two conditions were all significantly below
Greeting: Significant diﬀerence was found with33.33% (p < 0.001) for both scenarios.
regard to both the corridor (p < 0.001) and corner
Aggressive: Significant diﬀerence was found with(p < 0.001) scenarios. The post-hoc tests suggest
regard to both the corridor (p < 0.001) and cornerthat the robot showing GL expressive light was se(p < 0.001) scenarios. The post-hoc tests suggest that
lected as most fitting the statement “This robot apthe robot showing RH expressive light was selected as
proached the person and greeted him” in the corridor
most fitting the statement “This robot looks aggresscenario (significantly above 33.33%, p < 0.001) andsive” in both the corridor (significantly above 33.33%,“This robot apologized to the person as it almost ran
p < 0.001) and corner (significantly above 33.33%,
into him” in the corner scenario (significantly above
p < 0.001) scenarios. The selection rates for the re33.33%, p < 0.001). The selection rates for the remaining two conditions were all significantly below
maining two conditions were all significantly below
33.33% (p < 0.001) for both scenarios.
33.33% (p < 0.001) for both scenarios.
Hard-to-understand: Significant diﬀerence was
Malfunction: Significant diﬀerence was found withfound with regard to both the corridor (p < 0.001)
regard to both the corridor (p < 0.001) and cornerand corner (p < 0.001) scenarios. The post-hoc tests(p < 0.001) scenarios. The post-hoc tests suggest thatsuggest that the robot showing no expressive light was
the robot showing no expressive light was selected as
selected as most fitting the statement “It is hard to
most fitting the statement “This robot suddenly had
understand what the robot wants to do” in both thesome kind of malfunction” in both the corridor (sigcorridor (significantly above 33.33%, p < 0.001) and
nificantly above 33.33%, p < 0.001) and corner (sigcorner (significantly above 33.33%, p < 0.001) scenarnificantly above 33.33%, p < 0.001) scenarios. The
ios. The selection rates for the remaining two condiselection rates for the robot showing GL were signiftions were all significantly below 33.33% (p < 0.001)
icantly below 33.33% (p < 0.001) for both scenarios,
for both scenarios.
while those for the robot showing RH showed no sigLikeable: Significant diﬀerence was found with regard to both the corridor (p < 0.001) and cornernificant diﬀerences (n.s.).
(p < 0.001) scenarios. The post-hoc tests suggest thatthe robot showing GL expressive light was selected as
5.2.2 Interpretation
most fitting the statement “I like this robot” in boththe corridor (significantly above 33.33%, p < 0.001)
5.3 Discussion
and corner (significantly above 33.33%, p < 0.001)
The results oﬀer strong evidence indicating that
scenarios. The selection rates for the remaining two
expressive lights can eﬀectively aﬀect people’s perconditions were all significantly below 33.33% (p <ception of a robot and, moreover, interpretation of
0.001) for both scenarios.
the robot’s intentions. Specifically, the robot’s intenFigure 5 illustrates the selection rates with regardtion was indeed diﬃcult to interpret in both scenarto the participants’ interpretation of the robot’s beios when the robot did not show any expressive light.
havior and intention. The mark “***” stands forThis was proven by the results with regard to thep < 0.001, which indicates that the selection rate forstatement “It is hard to understand what the robot
the corresponding item is significantly above 33.33%.
wants to do.” In this condition, over 50% of the parBlock: Significant diﬀerence was found with regard to both the corridor (p < 0.001) and corner

n

red

none

823529411764710.715686274509804

6078431372549 0.166666666666667

GL

RH

210

823529411764710.333333333333333

0588235294118 0.107843137254902

862745098039220.0980392156862745

5098039215686 0.0931372549019608

862745098039220.843137254901961*** ***

175

313725490196080.808823529411765

***

140

***

105

8627450980392 0.0784313725490196

37254901960784 0.32843137254902

704117647058823 0.0980392156862745

921568627450980.877450980392157

35921568627450980.0686274509803922

0

friendly

4313725490196 0.0784313725490196

aggressive

hard-to-understandlike

Corridor scenario
210
175*** ***

***

***

140
105
70
35
0

friendly

aggressive

hard-to-understandlike

Corner scenario

図 4: Selection rates for perception. From left to right:
friendly, aggressive, hard-to-understand, and likeable.

686274509804

none

666666666667

333333333333

GL

RH

210

843137254902

823529411765

175

0392156862745

***

140

1372549019608

***
***

137254901961

105

4313725490196

43137254902

700392156862745

450980392157

356274509803922

4313725490196

0

block

greeting

malfunction

Corridor scenario
210

***

175

***

140

***

105
70
35
0

block

greeting

malfunction

Corner scenario

図 5: Selection rates for interpretation. From left toright: block, greeting, and malfunction.

1

ticipants thought that the robot might have had somekind of malfunction (see Figure 5).
When the robot showed green and low-intensity triangular expressive light, over 70% of the participantsinterpreted the robot’s intention in a positive way (seeFigure 4). However, when the robot showed red andhigh-intensity rectangular expressive light, most participants interpreted the robot’s intention in a negative way (see Figure 4). In addition, the participants
had particularly positive impressions of the robot showing GL expressive light. To be specific, they perceived
this robot as being friendly and likable rather than the
robots in the remaining two conditions.
An interesting result was found with regard to thestatement “This robot suddenly had some kind of
malfunction.” Although most participants selected thisstatement for the robot showing no expressive light,
over 30% of them selected the robot showing RH expressive light. This finding indicates the strong negative eﬀects of red since malfunction can also be considered to be a negative state of a robot.

6

GENERAL DISCUSSION

The findings of the third experiment were consistent with the results of the two preliminary experiments, providing strong evidence of the eﬀectivenessof expressive lights on people’s perception and interpretation of a robot. Such results are also in line withcolor psychology theories. The participants perceived
the green and low-intensity triangular expressive light
positively and further displayed approach-like behaviors and interpreted the robot’s intention by showing a higher tolerance to unfair oﬀers, behaving morecooperatively, and interpreting the robots’ intention
as approaching and greeting. Similarly, they perceived the red and high-intensity rectangular expressive light negatively and further displayed avoidancelike behaviors and interpreted the robot’s intentions
by showing a lower tolerance to unfair oﬀers, behaving
less cooperatively, and interpreting the robot’s intention as the robot wanting them to move away.
As Donald Norman said, people are explanatory
creatures. We construct mental models of things fromfragmentary evidence and by using a kind of naive
psychology that postulates causes, mechanisms, andrelationships even where there are none [28]. Therefore, a lack of critical information may result in illdefined or even faulty mental models. In an HRI sce-nario, such ill-defined or faulty models can lead tomisunderstanding a robot and having an unsmoothinteraction and, in worse cases, lead humans to become frustrated and reject using the robot. Particularly, for an appearance-constrained robot, failed interaction can be a common issue as such robots lackmethods of conveying their inner states, aﬀect, andintentions. However, by using expressive lights, weare enabling the robot to modify its appearance asa means of communicating with people. Such lights
serve as an additional cue to assist people in forming
correct mental models, thus allowing them to interpret a robot’s true intentions.
Our findings provide insights into the design andapplication of expressive lights. We show that color
psychology theories can be introduced as theoreticalgroundings for designing the color parameters of expressive lights, and other parameters such as waveform and intensity (frequency) can serve as a method
for enhancing the eﬀects of color. In addition, we
think that the eﬀects of expressive lights are more
likely to be eﬀective at an abstract level with regard to people’s perception of a robot, e.g., inducing
approach-like/positive or avoidance-like/negative interpretations, thus suggesting that expressive lights
can be employed in various HRI scenarios where such
eﬀects are useful. For example, a search and rescue
robot may employ expressive lights to convey messages such as “be calm” or “keep away!” to victims.
However, the generality of our findings may be limited due to the two scenarios (corridor and corner)
we used in the third experiment. Such scenarios basically represent the same context: a human-robot
encounter in an indoor place. According to the colorin-context (CIC) theory [15], color carries diﬀerent
meanings in diﬀerent contexts and, therefore, color
has diﬀerent implications for feelings, thoughts, andbehaviors in diﬀerent contexts. For instance, although
red is generally considered to be negative, threatening, and highly arousing, it also carries a positive andappetitive meaning when seen on a potential mate.
Therefore, expressive lights needs to be further researched with various HRI contexts and more colors.
In addition, cultural factors need to be investigated infuture studies as the eﬀects of color on humans may
depend on culture (in particular cases, for instance,
red has positive meanings in Chinese culture) [15].

7

Acknowledgments

This study was partially supported by JSPS KAKENHI “Cognitive Interaction Design” (No. 26118005).

参考文献[1] http://www.pleoworld.com/pleo_rb/eng/
index.php, 2012 (Accessed August 15, 2017)[2] https://cdn-shop.adafruit.com/
datasheets/create_2_Open_Interface_Spec.pdf, 2015 (Accessed August 15, 2017)[3] https://en.wikipedia.org/wiki/Roomba,
2017 (Accessed August 15, 2017)[4] http://www.sony-aibo.com,
August 15, 2017)2017 (Accessed[5] https://www.ald.softbankrobotics.com/en/
robots/nao, 2017 (Accessed August 15, 2017)[6] Changizi Mark A., Qiong Zhang, and Shinsuke
Shimojo: Bare skin, blood and the evolution ofprimate colour vision, Biology Letters, Vol. 2,
No.2, pp. 217–221 (2006)[7] Akshita, Alagarai Sampath Harini, Indurkhya
Bipin, Lee Eunhwa, and Bae Yudong: Towards
Multimodal Aﬀective Feedback: Interaction Between Visual and Haptic Modalities, Proceedingsof the 33rd Annual ACM Conference on Human
Factors in Computing Systems (CHI 15), ACM,
New York, NY, USA, pp. 2043–2052 (2015)[8] Cynthia Breazeal:, Designing Sociable Robots,
MIT Press, Cambridge, MA, USA, (2002)[9] Cynthia Breazea: Social interactions in HRI: therobot view, IEEE Transac- tions on Systems,
Man, and Cybernetics, Part C (Applications andReviews), Vol. 34, No. 2, pp. 181–186 (2007)[10] Sza r Daniel, Bilge Mutlu, and Terrence Fong:
Communication of Intent in Assistive Free Flyers, Proceedings of the 9th ACM/IEEE International Conference on Human-robot Interaction,
ACM, New York, NY, USA, pp. 358–365 (2007)[11] DeSteno David, Breazeal Cynthia, Frank Robert
H., Pizarro David, Baumann Jolie, Dickens Leah,and Lee Jin Joo: Detecting the trustworthinessof novel partners in economic exchange, Psychological science, Vol. 23, No. 12, pp. 1549–1556(2012)[12] Sanfey AlanG., Rilling James K., Aronson
Jessica A., Nystrom Leigh E., and Cohen
Jonathan D: The Neural Basis of Economic
Decision-Making in the Ultimatum Game, Science, Vol. 300, No. 5626, pp. 1755–xx (1758)[13] Chris Harrison, John Horstman, Gary Hsieh, andScott Hudson: Unlocking the Expressivity ofPoint Lights, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, pp. 1683–1692(2012)[14] Labrecque Lauren I. and George R. Milne: Exciting red and competent blue: The importanceof color in marketing, Journal of the Academy ofMarketing Science, Vol. 40, No. 5, pp. 711–727(2012)[15] Elliot Andrew J., Fairchild Mark D., andFranklin Anna: Handbook of Color Psychology,
Cambridge University Press (2015)[16] ElliotAndrewJ.andMarkusA.Maier: Color andpsychological functioning, Current Directions in
Psychological Science, Vol. 16, No. 5, pp. 250–254(2007)[17] Elliot Andrew J. and Markus A. Maier: Color
psychology: eﬀects of perceiving color on psychological functioning in humans, Annual reviewof psychology, Vol. 65, pp. 95–120 (2014)[18] Baraka Kim and Rosenthal Stephanie: Effective Non-Verbal Communication for Mobile
Robots using Expressive Lights, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA(2016)[19] Rikard Kuller, Byron Mikellides, and Jan
Janssens: Color, arousal, and performance - A
comparison of three experiments, Color Research& Application, Vol. 34, No. 2, pp. 141–152 (2009)[20] BethelCindyL: Robots without faces: non-verbal
social human-robot interaction, University ofSouth Florida (2009)[21] Bethel Cindy L. and Robin R. Murphy: Nonfacial/Non-verbal Methods of Aﬀective Expression As Applied to Robot-assisted Victim Assessment, Proceedings of the 2nd ACM/IEEE International Conference on Human-robot Interaction, ACM, New York, NY, USA, pp. 287–294(2007)[22] Bethel Cindy L. and Robin R. Murphy: Survey ofNon-facial/Non-verbal Aﬀective Expressions for
Appearance-Constrained Robots, IEEE Transactions on Systems, Man, and Cybernetics, PartC (Applications and Reviews), Vol. 38, No. 1,
pp. 83–92 (2007)[23] Moshkina Lilia and Ronald C. Arkin: Humanperspective on aﬀective robotic behavior: a longitudinal study, 2005 IEEE/RSJ International
Conference on Intelligent Robots and Systems,(2005)[24] Schee Mark, Pinto John, Rahardja Kris, Snibbe
Scott, and Tow Robert: Experiences with
Sparky, a social robot, Socially Intelligent
Agents, pp. 173–180 (2002)[25] Saerbeck Martin and Christoph Bartneck: Perception of aﬀect elicited by robot motion, Proceedings of the 5th ACM/IEEE International
Conference on Human-Robot Interaction, pp. 53–
60 (2010)[26] Stevens Martin and Graeme D. Ruxton: Linking
the evolution and form of warning coloration innature, Proceedings of the Royal Society of London B: Biological Sciences, Vol. 279, No. 1782,
pp. 417–426 (2011)[27] Kaya Naz and H. Epps Helen: Color-emotion associations: Past experience and personal preference, AIC 2004 Color and Paints, Interim Meeting of the International Color Association, Proceedings, pp. 31–34 (2004)[28] Don Norman: The design of everyday things, The
MIT Press, London (1998)[29] Wobbrock Jacob O, Findlater Leah, Gergle Darren, and Higgins James J: The Aligned Rank
Transform for Nonparametric Factorial Analyses Using Only Anova Procedures, Proceedingsof the SIGCHI Conference on Human Factors in
Computing Systems, ACM, New York, NY, USA,pp. 143–146 (2011)

[30] Hartson Rex: Cognitive, physical, sensory, andfunctional aﬀordances in interaction design, Behaviour & Information Technology, Vol. 22,
No. 5, pp. 315–338 (2003)[31] Murakami Ryo, Morales Saiki Luis Yoichi, Satake Satoru, Kanda Takayuki, and Ishiguro Hiroshi: Destination Unknown: Walking Side-byside Without Knowing the Goal, Proceedings ofthe 2014 ACM/IEEE International Conferenceon Human-robot Interaction, pp. 471–478 (2014)[32] Marina V. Sokolova and Antonio FernándezCaballero, A Review on the Role of Color andLight in Aﬀective Computing, Applied Sciences,Vol. 5, No. 3, pp. 275–293 (2015)[33] Komarov Steven, Reinecke Katharina, and Gajos
Krzysztof Z: Crowdsourcing performance evaluations of user interfaces, Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems, pp. 207–216 (2013)[34] Kazunori Terada, Atsushi Yamauchi, and Akira
Ito: Artificial emotion expression for a robot by
dynamic color change, Proceedings of the 21stIEEE International Symposium on Robot andHuman Interactive Communication, pp. 314–321(2012)[35] Elena Torta, Elisabethvan Dijk, Peter A.M. Ruijten, and Raymond H. Cuijpers: The Ultimatum Game as Measurement Tool for Anthropomorphism in Human–Robot Interaction, Springer
International Publishing, Cham, pp. 209–217(2013)[36] Shiwa Toshiyuki, Kanda Takayuki, Imai Michita, Ishiguro Hiroshi, and Hagita Norihiro:
How quickly should communication robots respond?, 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI, pp. 153–
160 (2008)