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Lost in Proxemics: Spatial Behavior for Cross-Cultural HRI
Michiel Joosse
Human Media Interaction
University of Twente
Enschede, the Netherlands
m.p.joosse@utwente.nl
Manja Lohse
Human Media Interaction
University of Twente
Enschede, the Netherlands
m.lohse@utwente.nl
Vanessa Evers
Human Media Interaction
University of Twente
Enschede, the Netherlands
v.evers@utwente.nl
ABSTRACT
Socio-psychological research hints to the fact that people from
different cultures have different preferences with respect to
proxemics. Thus, what might be considered normal for one
person, could be a violation of a norm for another person. If
cultural background influences spatial behaviors, a logical follow-
up question would be if a robot should be equipped with different
sets of normative motion behaviors for guiding people. In this
paper, we provide an overview of research into cultural
differences in proxemics and human-robot social norms. We will
address culture not at a national level (i.e. Dutch vs. German
national culture), but instead at a clustered, supranational level
based upon work by [13]. We conclude with foreseen challenges
and solutions for analyzing the appropriateness of HRI behaviors
in the context of different cultures.
Categories and Subject Descriptors
J.4 [Computer Applications]: Social and Behavioral Sciences
General Terms
Human Factors
Keywords
Human-Robot Interaction, Cultural Differences, Public Space,
Proxemics.
1. INTRODUCTION
The phrase “as robots start entering our life” might be an
understatement, especially in this field of research. It is not so
much a question of if, but more when, and how social robots will
enter our daily lives. Over a decade ago, Fong et al. [10] provided
an overview of the then-current state of robotics, and
distinguished six major application areas. In this paper we focus
on culture-aware robotics within the service application field, and
specifically short-term public interaction robots.
As part of the EU FP7-project Spencer
1
, we intend to elicit and
evaluate socially normative motion behaviors for a robot which
navigates through a crowded environment. The crowded
environment is an international airport, where the robot will guide
delayed, culturally diverse, passengers from their intercontinental
flight to their connecting continental (European) flight. We do not
1
http://www.spencer.eu
attempt to trivialize the underlying technical challenges to
navigate such an environment in an effective and safe way, but we
will focus on the aspect of cultural normative behavior.
Research has pointed to evidence suggesting that people explain
machine behavior in terms of human behavior. People
anthropomorphize, or have “the tendency to imbue the real or
imagined behavior of nonhuman agents with humanlike
characteristics, motivations, intentions, or emotions” [9].
Examples include a preference for a specific (static) robot head,
given a certain task [12], or the perception of cameras as eyes.
In this paper, we will first provide a short overview of human
social norms in general, and cross-cultural social norms research
specifically (Section 2). We will then discuss human-robot social
norms (Section 3), and discuss challenges for cross-cultural
human-robot interaction (HRI) research (Section 4).
2. ON SOCIAL NORMS
Social norms are unwritten norms, sustained by feelings of
embarrassment and guilt when violated [8], the disapproval of
other people, and social sanctions [32]. These norms are
situational dependent; norms governing appropriate conduct
during a soccer game differ from those which govern a funeral
[1]. The definition of social norms we use in this paper is “Rules
and standards that are understood by members of a group and that
guide and/or constrain social behavior without the force of laws”
[6].
Examples of research into human adherence to social norms
include series of experiments by Cialdini et al. and Keizer et al.
[24]. The norm researched was the social norm of littering in
public space. The main findings include that a) people tend to
litter more in an already-littered environment, b) littering
increased when the norm was made salient, and c) that the
violation of one norm (a littered environment) makes violation of
others norms more likely – the latter also called a cross-inhibition
effect. Similar results have been found for other social norms,
such as the norm of “being silent in the library” [1].
While above research provides insightful results, these are not
necessarily the social norms that are automatically relevant or
applicable for the Spencer project. A norm that ís relevant, is the
norm concerning the adherence to one’s personal space. Personal
space is one of the four proxemics zones defined by Hall [14], and
refers to the semi-circular shaped protective bubble people keep
around themselves that cannot be invaded without causing some
sort of discomfort. In his book, the Hidden Dimension [14], Hall
indicated the size of one’s personal space to be around 45 cm.,
this being applicable to Northern Americans, and indicating this
size to be different for, for instance, Chinese people.
2.1 Personal space is dependent on culture
Several experiments showed that people with different cultural
backgrounds have a different sized personal space zone. One
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HRI’14 Workshop on Culture Aware Robotics, 3 March, 2014, Bielefeld,
Germany.
example dimension to explain cultural differences is the
dimension, or maybe division, of cultures into “contact” and
“noncontact” cultures. Based upon observations, Hall [14] noted
that people from noncontact cultures (Northern European,
Northern American countries) maintain a larger personal space
compared with their counterparts from contact cultures (Southern
European, Southern American, Arab countries).
In one of the experiments, 105 students from three different
ethnical groups (Japanese, American and Venezuelan) had a
(seated) five-minute conversation with a same-sex, same-
nationality confederate [34]. Either in their native language, or in
English. They found, when speaking English, participants from
the non-contact culture (Japan) sat further apart from each other
compared to the contact culture (Venezuela). Within the ethnical
groups male participants sat further apart than female participants.
Furthermore, when speaking their native language, contact culture
participants sat closer together.
Other experiments looking at cross-cultural proxemics distances
include the work by Little [27], who used the placement of dolls
to infer at which distance people from either the U.S., Sweden,
Scotland, Italy and Greece would place people in 19 different
social situations, and found similar differences between countries.
Likewise, Høgh-Olesen [19] looked at proxemic differences
between cultures, but also at similarities. Based upon the work of
Pike [31], he differentiated between two terms; proxethics and
proxemics. Proxethics refers to the behaviors and dynamics which
are shared by humans – thus being universal. In contrast,
proxemics looks at the differences [19]. Høgh-Olesen found six
cross-cultural proxethics conventions within six cultures
(Greenland, Finland, Denmark, Italy, India and Cameroon). For
instance, people leave more room between two strangers
compared with one stranger, and the personal space is smaller in
social spaces (a café) as compared with non-social spaces
(library).
With the knowledge that social norms exist for humans, and these
norms can be different for people with different cultural
background, a question arises what culture is, and what research
has been conducted with regards to cross-cultural human-robot
interaction. However, before discussing this in Section 3, we will
take a look at the current research in HRI with respect to social
norms.
2.2 Human-Robot Social Norms
HRI work related to social norms has mostly been concerned with
physical norms, such as approaching someone. Work by Walters
[38] focused primarily on the identification of the size of humans’
personal space bubble. Takayama & Pantofaru [35] looked at the
effect of robot gaze on the approach distance humans keep. They
found that when the robot would gaze towards one’s legs, men
and woman would approach equally close (M=0.28 / 0.30m).
However, when the robot gazed towards the participants face,
woman maintained a significant larger personal space (M=0.30
m.) compared with men (M=0.24m).
Related to personal space, Dautenhahn et al. [7] looked at the
angle of robot approach. In a between-subjects experiment, the
majority of participants indicated the robot should bring a remote
control from a right-frontal side approach, instead of a full-frontal
approach. Koay et al. [25] found comparable results in a
longitudinal study, however, over time, participants allowed the
PeopleBot to approach equally close from the full-front as from
the front-side.
Pandey & Alami (2009) developed and tested a framework for a
social robot which (autonomously) conformed to four different
social conventions, these being: (1) Maintain right-half portion in
a narrow passage, (2, 3) pass and overtake a person from his / her
left side. (4) Avoid very close sudden appearance from behind a
wall. In a between-subjects experiment (N=8), a 84.7% reduction
in unwanted behavior was found [29].
From this we conclude that social norms exist for humans, and
that, if equipped with social norms, acceptance and user
experience of social robots can be improved.
3. THERE’S CULTURE AND THERE’S
CULTURE
Culture is an ambiguous concept. We use the following definition
of culture: “a fuzzy set of attitudes, beliefs, and behavioral norms,
and basic assumptions and values that are shared by a group of
people, and that influence each member’s behavior and his/her
interpretations of the ‘meaning’ of other people’s behavior” [33].
Triandis divided culture into a subjective and material culture.
Material culture consists of elements, for instance food, houses
and tools. Subjective culture, on the other hand refers to the
characteristic way in which a specific group perceives its
environment [36]. When referring to culture, we are referring to
subjective culture.
Usually, when scholars are looking at a culture – and the
differences between cultures, the level of analysis is the nation, or
sometimes subcultures within a nation. Karahanna et al. [22]
defined different levels of cultures, these being supranational,
national, and levels within a nation, such as the professional,
organizational and the group level.
Over the years, there have been several scholars like Hofstede
[18] and Pelto [30] who described differences between national
cultures according to different dimensions. In a study by Gelfand
et al. [11] participants (N=6823) from over 33 countries were
asked to rate the appropriateness of twelve behaviors in fifteen
everyday situations, and, whether or not there were clear rules for
appropriate behaviors in these situations. It was found that there
was a high within-nation agreement about the level of constraint
in everyday situations, and a high level of variability between-
nations. The nation as unit of analysis appears to have proven to
be an useful unit of analysis.
A common belief is that society is becoming more and more
individualistic, in part due to IT advances. As Jones [21] puts it:
“[…] many researchers find culture to be a dynamic, constantly
changing field. Cultures are merging, technology is changing the
way we communicate, and globalization is changing the way we
trade and interface”. Thus, the question arises if cultures as a
whole are also becoming more individualistic. Hamamura [15]
compared national studies studying individualism-collectivism in
the U.S. and Japan over time. In contrast to the common belief
they concluded both cultures did not become significantly more
individualistic. Similar, Gelfand et al. [11] concluded that social
constraint appeared to be more or less stable over time in the
United States.
Due to various reasons, some of the 196 countries on this planet
will have inhabitants with similar cultural backgrounds. We
intend to analyze cultures at the supranational level, here being
regional clusters of countries.
3.1 Supranational Level: Clusters of Cultures
According to Gupta et al. [13], three major forces have been used
historically to cluster countries, these being (1) geographic
proximity, (2) mass migration & ethic social capital, and (3)
religious and linguistic communality. Societal clustering is a part
of the GLOBE project. One of the goals of the authors was to
understand similarities and differences among the countries
studied within the GLOBE project [20]. As part of this project, 61
nations were clustered into 10 clusters of cultures (see Figure 1,
and Appendix I ) [13]. Examples include the Nordic European
cluster containing Finland, Sweden and Denmark, and the
Germanic European cluster with Austria, Switzerland, the
Netherlands and Germany. Appendix I provides the countries
contained within each of the ten regional clusters. The remainder
of this section will discuss the methodology by which the
measures underlying this clustering were developed in more
detail.
Among the measures were nine dimensions of culture. These
dimensions (performance orientation, assertiveness, future
orientation, humane orientation, institutional collectivism, in-
group collectivism, gender egalitarianism, power distance, and
uncertainty avoidance) are the primary measures of interest for us.
For each of these scales, questions assessed participants’ idea
regarding both the practices (as is) as well as the values (should
be) in organizations and society.
As high wind blows on high hills, there are limitations with the
GLOBE project as with any other research paper. Hofstede [17]
provides an overview of similarities and differences between the
GLOBE study and his own work [16]. One of his major concerns
is that the questionnaire items might not have captured what the
researchers had in mind, and, that the complete GLOBE
questionnaire has not been published. Hofstede is well-known for
his work on national value differences while employed by IBM.
Five dimensions of national culture were identified based upon
results from a survey completed by 117.000 IBM employees.
Both GLOBE and Hofstede’s IBM studies make sense of culture
within an industrial setting. On the other hand, the GLOBE
involved managers, whereas the IBM study involved seven
categories of employees, of which two were managerial categories
[17] of employees. While it can be expected that the GLOBE
project will either be loved or hated by scholars, in a way like the
IBM study [21], for us the most important fact is that both studies
provide empirical evidence that there are differences between
cultures.
The next section will provide an overview of cross-cultural
research in HRI.
3.2 Human-Robot Cultural differences
Several studies have been conducted in order to explain cultural
differences in different situations involving robots. These
situations range from a plain, general attitude to robots, to
experiments involving human-robot teamwork.
Bartneck et al. [3] distributed a survey among internet users from
different countries in which participants were asked to complete
the Negative Attitudes towards Robots Scale (NARS)
questionnaire. Results indicated cultural background significantly
effected attitude towards robots.
In an unpublished experiment by Sau-Lai Lee, reported by Kiesler
[24], Chinese participants viewed a video of robot interaction with
an experimenter, they were asked whether or not the robot would
know certain landmarks. The “cultural background” of the robot
was manipulated by having the robot talk either English or
Cantonese, and informing participants the robot was created in
either China or New York. Based upon the origin of the robot,
people had a different mental model of the robot. Lee found two
relevant results providing evidence for this. First, people expected
the robot to have more knowledge about famous landmarks in
both countries, than about not so famous landmarks. The second,
perhaps the most important: participants expected the “Chinese”
Figure 1. Ten clusters of cultures, figure based upon [13].
Legend: Anglo, Latin Europe, Nordic Europe, Germanic Europe, Eastern Europe, Latin America,
Sub-Saharan Africa, Middle East, Southern Asia, Confucian Asia
.
robot to know more about Chinese landmarks than the
“American” robot, and vice versa. In a similar way, Trovato et al.
[37] found that Egyptian and Japanese participants preferred a
robot displaying a similar cultural background. A robot was
programmed to greet participants in the English language with
either an Arabic or Japanese accent, and performing a greeting
gesture also performed by humans in that culture. It was found
that Japanese participants preferred the Japanese robot, and
Egyptians the Arabic robot.
Wang et al. [39] conducted a 2x2 experiment involving robots,
manipulating culture and robot communication style. 320
participants, 80 Chinese dyads and 80 U.S. dyads, interacted with
a robot providing advice either implicitly or explicitly. The
underlying hypothesis was that since the Chinese typically prefer
and implicit communication style, and U.S. people a more
explicit, a robot displaying a matching communication style
would be seen as a more in-group member and thus more trusted
and perceived as more credible. Supporting their hypothesis,
Chinese participants preferred the implicit robot whereas U.S.
participants preferred the explicit robot. Furthermore, when the
robot communicated in the preferred way, participants were more
likely to change their decisions in order to align with the robot.
Li et al. [26] also found evidence in a HRI trial that participants
from a low-context culture (Germany) had different scores with
respect to the evaluation of the interaction than those from high-
context cultures (Chinese and Korean).
From the above we expect people from different cultures will
have different views on which behaviors are normative for a
robot. Previous work with regards to cultural aspects in HRI has
been limited mostly to human-robot collaborative teamwork. The
work in HRI on proxemics has not yet taken culture into account,
which could become a shortcoming when robots are going to
interact in public spaces with people having different cultural
backgrounds.
4. TOWARDS A METHODOLOGY
In this section, we will describe two major challenges we see for
HRI research researching cross-cultural robot behavior. These
challenges are:
1) Choosing a research methodology
2) Sampling of cultures of interest
We will describe both challenges, insofar as not discussed before,
and offer our ideas to solve this in Section 4.2.
4.1 Overview of methodologies
Different methodologies have been employed in order to gather
data from participants from different cultures. In this section, we
will first provide an overview of different methods which have
been used to find answers with regards to cross-cultural
differences, both in human-human, and human-robot interaction.
We will then conclude with an experimental setup.
A number of studies manipulated culture by using native students
and exchange students in a lab experiment. ([4], [26], [34]).
Already in the 80s, Baldassare & Feller [2] hinted that the
frequent comparison of U.S. versus exchange students of a culture
decreases ecological validity, because a) the students are not
observed in their natural culture, b) they have been influenced by
North American proxemics patterns for an undisclosed time, and
c) they are not a representative sample. Wang et al. [39] collected
data at two separate sites; thus using native students in both
settings. However, this sample was also not representative
because it only included students.
Woods et al. [40] used a method called “video-based human-robot
interaction” (VHRI) in which participants viewed videos of a
human interacting with a robot. Results between this video-based
methodology and a lab experiment with real participants were
found to be comparable.
Self-reported measures, such as questionnaires, were also
frequently employed. The advantage here being able to use
participants from geographically distributed locations. ([3], [11],
[5]). All reported studies report having the questionnaires
translated and back-translated into the participants’ native
language.
Two experiments made use of either scaled dolls or silhouettes in
order to capture people’s impression of appropriate interpersonal
distance in different situations ([27], [28]). Like a lab experiment,
the use of dolls does require some sort of physical location when
collecting data at different sites.
All these methods have advantages and disadvantages. The first
method, experiments with an actual embodied robot, would be
preferred for HRI since it would provide the most realistic setting.
An ideal situation would be an experiment, be it a Wizard-of-Oz
experiment with one type of robot, shipped all over the world to
various data collection sites. This is an utopian experiment design
in a world not constrained by resources like time, money and
man-hours. The other methods (VHRI studies and scaled figures)
could provide a solution, albeit generalizability of the results to a
real-world setting could be questioned. In the next section we
propose a hybrid approach to tackle these issues.
4.2 Proposed methodology
At this moment, we are conducting a survey with this setup using
stills of 3D people. This survey is currently being distributed to
three countries. While data collection has not yet been finished,
one of the possible issues we might face is that the results are not
generalizable enough because when you approach a group, the
formation of the group is going to change as soon as you
approach. Therefore, the use of 3D pictures might not be a
sufficient methodology to investigate cross-cultural robot spatial
behavior.
Based on this insight, we propose a combination of a lab- and
video study to increase ecological validity while investigating the
following questions:
1) “From which angle should a robot approach a small group of
people?”
2) “Do people from different cultures have significant different
preferences when a robot approaches a small group of people?”
3) “Do survey-based HRI studies provide reliable results when
used in lieu of experiments when evaluating robot spatial
behavior?”
In our situation, we have access to two robots of similar design, at
two different sites – a site in the Netherlands, and a site in Spain.
We propose to run a between-groups field experiment at both
locations, thus having two different cultures. In the experiment,
we will ask small groups of people (3-5) to stand in a room and
discuss a topic. Participants will be informed that after a minute a
robot will approach the group and bring the new discussion topic.
The robot will approach the group from various angles, and stop
at different distances.
At one of these locations, we will make a video recording of the
different experiment conditions with actors. In order to test if the
behaviors are perceived equally (un-)appropriate in videos
compared with the field experiment, we will distribute the video
to participants from the same countries as those in the field
experiment. If it turns out to be true, the questionnaire can be
distributed to participants with cultural backgrounds not
investigated in the field experiment.
5. CONCLUSION
Service robots start entering our daily lives. When real social
robots do, an important question will be if culturally different
motion behaviors are necessary for a robot guiding people with
distinct different backgrounds. Previous HRI research focusing on
cultural aspects does not provide indisputable results, though we
find it likely these results could surface when evaluating motion
behaviors with respect to different cultures.
Based upon an overview of previously used methods to evaluate
cross-cultural differences we have proposed a mixed-methods
method in order to evaluate cross-cultural HRI behavior
preferences in a resource-efficient way.
6. ACKNOWLEDGMENTS
This work has partly been supported by the European
Commission under contract number FP7-ICT-600877
(SPENCER).
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8. Appendix I
Table 3 provides the ten GLOBE clusters of societies and the
respective countries within each cluster.
Table 3. GLOBE clusters. Source [13]
Anglo Cultures
England, Australia, South Africa (White sample), Canada,
New Zealand, Ireland, United States
Confucian Asia
China, Hong Kong, Japan, Singapore, South Korea, Taiwan
Eastern Europe
Albania, Georgia, Greece, Hungary, Kazakhstan, Poland,
Russia, Slovenia
Germanic Europe
Austria, Germany, Netherlands, Switzerland (German
speaking)
Latin America
Argentina, Bolivia, Brazil, Colombia, Costa Rica, Ecuador, El
Salvador, Guatemala, Mexico, Venezuela.
Latin Europe
France, Israel, Italy, Portugal, Spain, Switzerland (French
speaking)
Nordic Europe
Finland, Sweden, Denmark
Southern Asia
India, Indonesia, Iran, Malaysia, Philippines, Thailand
Sub-Sahara Africa
Namibia, Nigeria, South Africa (Black sample), Zambia,
Zimbabwe
Middle East
Egypt, Kuwait, Morocco, Qatar, Turkey