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Research Article
Cecilia Roselli, Francesca Ciardo, and Agnieszka Wykowska*
Social inclusion of robots depends on the way a
robot is presented to observers
https://doi.org/10.1515/pjbr-2022-0003
received February 1, 2022; accepted May 25, 2022
Abstract: Research has shown that people evaluate others
according to specificcategories.Asthis phenomenon seems
to transfer from human–human to human–robot interac-
tions, in the present study we focused on (1)thedegreeof
prior knowledge about technology, in terms of theoretical
background and technical education, and (2)intentionality
attribution toward robots, as factors potentially modulating
individuals’tendency to perceive robots as social partners.
Thus, we designed a study where we asked two samples of
participants varying in their prior knowledge about tech-
nology to perform a ball-tossing game, before and after
watching a video where the humanoid iCub robot was
depicted either as an artificial system or as an intentional
agent. Results showed that people were more prone to
socially include the robot after observing iCub presented
as an artificial system, regardless of their degree of prior
knowledge about technology. Therefore, we suggest that
the way the robot was presented, and not the prior knowl-
edge about technology, is likely to modulate individuals’
tendency to perceive the robot as a social partner.
Keywords: knowledge, technology, intentionality attribu-
tion, cyberball, human–robot interaction
1 Introduction
Social categorization is a key mechanism of social cogni-
tion in humans. We tend to categorize others based on
various cues, such as gender, age, and ethnicity [1].
Social categorization allows us to cope with the com-
plexity of social information we process in everyday life
[2,3], reducing the amount of novel information that
needs to be processed by grouping information into a
single category. Indeed, from early infancy, our brain
uses various strategies to deal with the abundance of
information it needs to process. One strategy to avoid
overload is “chunking”. Chunking of information occurs
based on semantic relatedness and perceptual similarity,
which are often processed and stored in our memory
together to allow us to recall better and faster more infor-
mation [4].
Notably, such a “chunked”processing strategy seems
to be involved also in social cognition, where, for example,
group members are represented as interchangeable parts
of a global, heuristic whole [5]. This way, the complexity of
representing each group member is overridden by the less
cognitively demanding processing of chunks of informa-
tion (see ref. [6]for a generaldiscussion of the efficiency of
chunked processing). Interestingly, chunking has been
proposed as a potential explanation of why people are in
general more able to recall individual information about
minority members [7]. Indeed, compared to majority mem-
bers, minority members are fewer, which implies a smaller
information load because it can be “chunked”together in
memory as a single unit of encoded information [3].In
other words, chunking represents a cognitive “shortcut”.
First, it allows for categorizing the target of perception at
the group level, because the group members can be easily
discerned based on cues such as sex, age, and ethnic
identity. Then, only after processing available information
about the target at the group level, the person-level infor-
mation and the personal identity can be construed [8,9].In
shared social contexts with others, chunking simplifies
perception and cognition by detecting inherent shared
characteristics and imposing a structure on the social
Cecilia Roselli: Social Cognition in Human-Robot Interaction,
Fondazione Istituto Italiano di Tecnologia, Center for Human
Technologies, 16152 Genova, Italy; DIBRIS, Dipartimento di
Informatica, Bioingegneria, Robotica ed Ingegneria dei Sistemi,
16145 Genova, Italy
Francesca Ciardo: Social Cognition in Human-Robot Interaction,
Fondazione Istituto Italiano di Tecnologia, Center for Human
Technologies, 16152 Genova, Italy
* Corresponding author: Agnieszka Wykowska, Social Cognition in
Human-Robot Interaction, Fondazione Istituto Italiano di
Tecnologia, Center for Human Technologies, 16152 Genova, Italy,
e-mail: Agnieszka.Wykowska@iit.it
Paladyn, Journal of Behavioral Robotics 2022; 13: 56–66
Open Access. © 2022 Cecilia Roselli et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0
International License.
world [10]. Consequently, people categorize themselves
and others into differentiated groups (in-and out-groups).
Once determined, group categorization shapes downstream
evaluation and behavior, often without awareness [11].
In summary, social categorization shapes the way
people interact with others, making them develop a
stronger preference for people who are recognized as
part of their in-group [12]. Being part of a group has
numerous benefits: indeed, groups provide social sup-
port, access to important resources, protection from dan-
gers, and the possibility to create bonds with potential
mates [13,14]. Therefore, it is not surprising that group
membership represents a crucial aspect of human life,
which has been extensively investigated in psychological
research (e.g., see ref. [15]for a review).
Recently, social inclusion became a relevant topic
also in the human–robot interaction (HRI)field, as evi-
dence showed that humans adopt similar social cognitive
mechanisms to robots as those adopted toward other
humans [16]. For example, Eyssel and Kuchenbrandt
found that human users prefer to interact with robots
that are categorized as in-group members [17]. Specifi-
cally, German participants were presented with a picture
of a humanoid robot, which they believed to belong either
to their national in-group (Germany)or to the out-group
(Turkey). When asked to rate the robot regarding its degree
of anthropomorphism, warmth, and psychological close-
ness, participants tended to evaluate more positively the
robot that was presented as an in-group member, com-
pared to the out-group one.
A task commonly used in social psychology research
to evaluate ostracism and social inclusion in a more
implicit way is the Cyberball paradigm [18,19], a task in
which participants believe that they are playing online a
ball-tossing game with two or more partners, which are
animated icons or avatars controlled by the computer
program. During the task, the program can vary the
degree to which the ball is tossed toward the players.
For instance, ostracized players are not passed the ball
after two initial tosses and thus obtain fewer ball tosses
than the other players. Included players are repeatedly
passed the ball and obtain an equal number of ball tosses
as the other players. The Cyberball paradigm has been
extensively used as an implicit measure of social inclusion
in many different experimental contexts (e.g.,[20–23]).For
example, in a previous study [24]the ethnicity of con-
federates was manipulated so that Caucasian American
participants performed the Cyberball task with either
same-ethnicity (i.e., Caucasian American)or other-ethni-
city confederates (i.e., African American). Results showed
that being included or ostracized by in-group members
intensified the experience of either exclusion or inclusion.
In other words, ostracism was evaluated as more painful
and social inclusion as more positive when carried out by
in-group members (i.e.,same-ethnicity confederates)[24].
Individual differences have also been shown to affect
social inclusion. For example, individual traits such as
self-esteem, narcissism, and self-compassion seem to
modulate people’s tendency to socially include others,
and thus, they should be taken into consideration when
developing interventions aimed at reducing aggression in
response to social exclusion [25].Interestingly,theimpact
of individual differences on social inclusion does apply not
only to the inclusion of other humans but also to artificial
agents such as robots [26,27]. For example, age seems to
play a critical role, as demonstrated by a recent study
testing a group of clinicians who conducted a robot-
assisted intervention [23]. Specifically, when investigating
individual differences in both explicit and implicit atti-
tudes toward robots, it emerged that older clinicians dis-
played more negative attitudes. Moreover, also the level of
education has been shown to modulate the social inclu-
sion of robots, in such a way that the more educated
people were, the less they were prone to perceive robots
as social entities [28]. Individual differences in the social
inclusion of robots are also driven by culture, leading
people to express different levels of trust, likeability, and
engagement toward robots [29]. In a recent study [30],
participants of two different nationalities, i.e.,Chinese
and UK participants, performed a modified version of the
Cyberball task (e.g.,[18,19,23]), to assess their tendency to
socially include the humanoid robot iCub [31]in the game.
Interestingly, results showed that only cultural differences
at the individual level, but not at the country level, were
predictive of the social inclusion of robots [30].Inother
words, the more individual participants displayed a col-
lectivistic stance, the more they tended to socially include
the robot in the Cyberball game. However, social inclusion
was not affected by participants’nationality, namely whether
they belong to a collectivistic (i.e.,Chinese)rather than an
individualistic culture (i.e.,UK
)[30].
Social inclusion and exclusion are related to prior
knowledge or biases that people have toward others.
Indeed, it has been demonstrated that, when people are
repeatedly presented with novel stimuli, they tend to
develop a preference for them, as repeated exposure
allows people to gain knowledge about them. This psy-
chological phenomenon has been called the mere expo-
sure effect [32], and it has been extensively demonstrated
to occur also in situations of interaction with other humans
(see ref. [33]for a review).Indeed,themorefrequently
individuals are exposed to someone, the more they would
Social inclusion of robots depends on the way a robot is presented to observers 57
bepronetolikethemandshowmore willingness to interact
with them. Further studies supported it, highlighting that
repeated exposure leads to increase perceived likeness,
reduces prejudices toward others, and enhances the prob-
ability to treat them as social partners, as they are con-
sidered as a part of one’sownin-group [34–36].Asan
explanation, it has been proposed that repeated exposure
increases liking because it reduces, over time, people’s appre-
hension toward novelty, such as other humans [33,37].In
other words, humans have evolved to be wary of novel
stimuli, which could constitute a potential danger. Therefore,
with repeated exposure, individuals gain more knowledge. As
they gain more knowledge, they understand that these enti-
ties are not inherently threatening. Consequently, over time
individuals start to like them more [32,38].Notably,thesame
mechanisms seem to take place when interacting with robots,
as people report to like robots more and to be well disposed
toward them after repeated interactions [23,39].
Alternatively, it may also be that people’saffective
reaction toward novel entities becomes weaker with their
increased familiarity with them, due to the affective habi-
tuation [40]. However, it would apply only to “extreme”
entities, i.e., entities showing a nearly perfect human
representation in terms of physical appearance. According
to the uncanny valley hypothesis [41], these entities, if still
distinguishable from real humans, could amplify people’s
emotional response toward them. However, for initially
neutral stimuli, increased exposure could make them
affectively more positive because of the mere exposure
effect [32].
Taking into account the role that exposure plays for
social acceptance and inclusion, it is crucial to address
the role that prior knowledge and technical background
have in perception of robots as social partners (and hence
social inclusion).
2 Aims
The present study aimed at investigating whether the
tendency to perceive robots as social partners is modu-
lated by participants’prior knowledge about technology, in
terms of theoretical background and technical education.
Another factor that we examined, as having the
potential to modulate the readiness to include robots as
social partners, was the way the robots are presented to
observers, namely whether they are presented as inten-
tional agents or mere mechanical devices. Malle and col-
leagues (2001)argued that attribution of intentionality
helps people to explain their own and others’behavior
in terms of underlying mental causes [42,43]. Humans are
good at detecting intentions: a substantial agreement in
judgments emerges when people are asked to differ-
entiate between intentional and unintentional behaviors
[44]. For example, we can make accurate judgments of
intentional behavior from the simple form of appearance
of an agent [45]. It can also happen by observing motor
signals [46], structured in goal-directed, intentional actions.
According to Searle (1999), the competence in predicting
and explaining (human)behavior involves the ability to
recognize others as intentional beings, and to interpret
other minds as having “intentional states”such as beliefs
and desires [47]. This is what Dennett refers to as the
Intentional Stance,i.e., the ascription of intentions and
intentional states to other agents in a social context
[48,49]. In the context of HRI, there are several studies
showing that people treat robots as if they were living
entities endowed with mental states, such as intentions,
beliefs, and desires (e.g.,[50–52]) following Searle’s
definition. Interestingly, also form and appearance can
relate to the perception of intentionality. For instance,
when interacting with an anthropomorphic robot, the like-
lihood of building a model of its mind increases with its
perceived human-likeness [50]. Moreover, people empathize
more strongly with human-like robots, so that when human-
likeness increases, people’s adoption of Intentional Stance
toward a robot could be very similar to the one toward a
human [53].
A possible explanation of why people might adopt
the Intentional Stance toward robots is that people are
not well informed about how the system has been designed
to behave. Thus, they would treat robots as intentional sys-
tems,asitallowsforusingthefamiliarandwell-trained
“schema”–usually used to explain other humans’beha-
vior –to explain also the robot’sbehavior[54,55].Inline
with this, it might be that the more people are exposed to
robots, the more they would gain knowledge about how
these systems are designed and controlled [23]. Therefore,
it might prevent people from adopting the Intentional Stance
and lead them to consider robots only as pre-programmed
mechanical systems, making people less willing to perceive
them as social partners.
However, to the best of our knowledge, no studies
previously investigated how social inclusion depends
on the combined effect of both prior knowledge about
technology and attribution of intentionality elicited by
how the robot is presented to observers.
In this study, addressing this question, we orthogon-
ally manipulated both factors. To test the effect of prior
knowledge about technology, we recruited two groups of
participants varying in their level of prior knowledge.
58 Cecilia Roselli et al.
Namely, we tested a group of participants with prior
knowledge about technology, in terms of theoretical back-
ground and technical education (i.e.,“Technology Expert”
group), and a group of participants having little prior
knowledge regarding technology, given their formal edu-
cation (i.e.,“General Population”group).
To evoke different degrees of attribution of intention-
ality, as a between-subject manipulation we presented
participants with a video depicting the iCub robot performing
either a goal-directed (intentional)action (“Mentalistic”
video; see Data Availability section to access the URL of
the video, filename: “Ment_Video.mp4”)or a video of a
robot being mounted on its platform and then calibrated
(“Mechanistic”video, see Data Availability section to access
theURLofthevideo,filename: “Mech_Video.mp4”).
To test individuals’tendency to include the robot as
an in-group social partner we developed a modified ver-
sion of the Cyberball task (e.g.,[18,19,23]), a well-estab-
lished task measuring (implicitly)social inclusion (see
also [56], for more information). In the original study
[19], participants were told that the Cyberball task was
simply used to assess their mental visualization skills.
The authors found that although participants played
with animated icons depicted on the screen, and not
with real people, they cared about the extent to which
they were included in the game by the other players.
For example, if participants were included (i.e.,ifthey
received the ball for one-third of the tosses), after the
game they reported more positive feelings –in terms of
control, self-esteem, and meaningful existence –than if
they received the ball only for one-sixth of the tosses [19].
In our version of the Cyberball task, participants were
instructed to toss the ball as fast as possible to one of the
other players (either iCub or the other human player),
being free to choose which player they wanted to toss
the ball to. Notably, both players (i.e., the iCub robot
and the other human player)were avatars that partici-
pants believed to be real agents playing online with them.
In detail, the avatar of the iCub robot was programmed
to equally alternate the ball between the two players,
whereas the avatar of the other human player was pro-
grammed to toss the ball to iCub only twice at the begin-
ning of the game, and not at all thereafter. It was intended
to make participants believe that the robot was excluded
by the other agent, and thus to investigate whether parti-
cipants tended to interact with iCub by re-including it in
the game.
To test the effect of presentation of the robot as either
intentional or mechanistic, we asked participants to perform
the Cyberball task in two separate sessions, namely before
and after watching the “Mechanistic”or “Mentalistic”video
(i.e.,CyberballPrevs Post). Notably, the structure of the
Cyberball task was identical in the two sessions.
We hypothesized that if prior knowledge about tech-
nology is the sole factor that affects the social inclusion of
robots, then people with prior knowledge about tech-
nology (i.e.,“Technology Expert”sample)should socially
include the robot more, compared to non-expert partici-
pants (i.e.,“General Population”sample), regardless of
the way the robot was presented in the video. In line with
the mere exposure effect [32], it would be because a
higher degree of prior knowledge about technology may
increase the knowledge about technical systems such as
robots, and, as a consequence, the perceived likeness of
robotic agents.
In contrast, if the attribution of intentionality is the
sole factor affecting the social inclusion of robots, then
the probability to re-include the robot should be higher
for people who observed iCub being presented as an
intentional system compared to people who observed
iCub being presented as an artificial, pre-programmed
artifact, regardless of participants’prior knowledge about
technology. Namely, participants should toss the ball to
the iCub more frequently following the video of iCub per-
forming goal-directed actions, as relative to the video in
which iCub is shown as being mounted on its platform
and then calibrated. This effect should be similar for both
technology expert and non-expert participants.
Finally, if both factors (i.e., prior knowledge and the
way the robot is presented to participants)play a role in
the social inclusion of robots, then we would expect to
find an interaction between the two. Namely, the way the
robot was presented in the videos, i.e., as an artificial or
as an intentional system, should modulate the probability
to re-include the robot in the game in the Cyberball Post
session compared to Pre, but dependent on prior knowl-
edge about technology (i.e.,“General Population”vs
“Technology Expert”sample).
3 Materials and methods
3.1 Participants
One hundred sixty participants were recruited to partici-
pate in the study, via the online platform Prolific(https://
prolific.co/). Participants were selected based on the fol-
lowing criteria: age range (18–45 years);fluent level of
English, to ensure that participants could understand the
instructions; handedness (right-handed);andpriorknowl-
edge about technology, in terms of theoretical background
Social inclusion of robots depends on the way a robot is presented to observers 59
and technical education. Specifically, half of the partici-
pants (“Technology Expert”sample)were selected based
on “Engineering”and “Computer Science”as educational
backgrounds, whereas for the other half (“General Population”
sample)we excluded these two backgrounds to prevent col-
lecting data from participants already having prior knowledge
about technology, given their formal education. To double-
check whether the educational background declared by parti-
cipants corresponded to the one selected via Prolific, before
the experiment we explicitly invited participants to indicate
their educational background, and whether it was related to
robotics. Notably, four participants who fell into the “General
Population”sample declared to have a background in robotics
when explicitly asked. Therefore, after checking that they had
a background in robotics, they were further included in the
“Technology Expert”sample.
The study was approved by the Local Ethical Committee
(Comitato Etico Regione Liguria)and conducted in accor-
dance with the ethical standards of the World Medical
Association (Declaration of Helsinki, 2013).Allparticipants
gave informed consent by ticking the respective box in the
online form, and they were naïve to the purpose of the
experiment. They all received an honorarium of £4.40 for
their participation.
3.2 Procedure
The experiment was a 2 (Session: Cyberball Pre vs Post,
within-subjects)×2(Type of Video: Mechanistic vs Mentalistic,
between-subjects)×2(Group: General Population vs
Technology Experts, between-subjects)design.
At the beginning of the experiment, participants were
asked to perform a modified version of the Cyberball (e.g.,
[18,19,23]), where participants believed to play online
with another human player and the humanoid robot
iCub (Figure 1).
Each trial started with the presentation of both the
human player and the iCub robot, on the right and the left
side of the screen, respectively; the participants’name
(“You”)was displayed at the bottom. The act of tossing
the ball was represented by a 1-s animation of a ball. As
previously mentioned, iCub was programmed to alternate
between the participant and the human avatar, with an
equal probability to pass the ball to either of them; con-
versely, the human player was programmed to toss the
ball to iCub only twice at the beginning of the game,
and not thereafter. When participants received the ball,
before tossing it, they were instructed to wait until their
name (i.e.,“You”)turned from black to red. Then, they
had 500 ms to decide which player to toss the ball to.
They were asked to be as fast as possible, being free to
choose either of the players. To choose the player on the
right side (“Human”), participants had to press the “M”
key, whereas they had to press the “Z”key to choose the
player on the left side (“iCub”). To make sure that parti-
cipants were not biased by the different locations of the
keys, we asked participants to use a standard QWERTY
keyboard to perform the task. If participants took more
than 500 ms to choose a player, a red “TIMEOUT”state-
ment was displayed on the screen, and the trial was
rejected. The task comprised 100 trials in which partici-
pants received the ball in both Pre and Post Cyberball
sessions. Namely, in both sessions participants had to
choose to toss the ball to either of the two players 100
times. Stimuli presentation and response collection were
programmed with PsychoPy v2020.1.3 [57].
After performing the Pre-session Cyberball, partici-
pants were asked to watch a 40-s video. As a between-
subjects manipulation, half of the users watched a video
in which iCub was presented as an artificial system, and
Figure 1: Schematic representation of the Cyberball ball-tossing game.
60 Cecilia Roselli et al.
the other half of users watched a video in which iCub
behaved as an intentional, human-like agent performing
goal-directed actions.
After watching the videos, participants were asked to
answer a few questions (i.e.,“How many humans/robots
did you see in the video?,”and “What title would you
give to the video?”). The purpose of these questions was
to ensure that participants paid attention to the content
of the video. After the experiment, we carefully checked
participants’responses to see whether there were responses
not congruent with the content of the video. All partici-
pants’responses were congruent with the content of the
depicted video, indicating that participants paid attention
to the video.
After answering the questions, participants were asked
to perform the Cyberball again, which was identical to the
one performed before the video.
4 Results
4.1 Data preprocessing
Data of two participants, i.e., one from the “General
Population”sample and one from the “Technology Expert”
sample, were not saved due to a technical error, and there-
fore they were not included in the analyses. The remaining
data were analyzed with R Studio v.4.0.2 [58],usingthelme4
package [59], and JASP Software v.0.14.1 (2020).Dataofpar-
ticipants with less than 80% of valid trials (i.e.,trialswhere
they pressed either the “Z”or “M”key within 500 ms after
participants’“You”name turned red)were excluded from
further analyses (11 participants excluded; 5.29% of the total
number of trials, mean =120 ms, SD =100 ms).Thus,the
final sample size on which we ran the analysis was N=147
(“General Population”group, N=75: Mechanistic video, N=
39, Mentalistic video, N=36; “Technology Expert”group, N=
72: Mechanistic video, N=34, Mentalistic video, N=38).
Furthermore, to check for outliers, all trials which deviated
±2.5 SD from participants’mean Reaction Times (RTs)were
excluded from the subsequent analyses (3.17% of trials,
mean =420.86 ms, SD =26.57 ms).
4.2 Probability of robot choice
To test whether individuals’tendency to include the robot
as an in-group social partner was modulated by the com-
bined effect of (i)prior knowledge about technology and
(ii)the way the robot was presented, the probability of
passing the ball to iCub was considered as the dependent
variable in a logistic regression model. Session (Cyberball
Pre vs Post), Type of Video (Mechanistic vs Mentalistic
video), and Group (General Population vs Technology
Experts), plus their interactions, were considered as fixed
effects, and Participants as a random effect (see Table 1 for
more information about mean values and SD related to the
rate of robot choice). Notably, in this study, the model met
the assumptions of logistic regression, namely linearity,
absence of multicollinearity among predictors, and the
lack of strongly influential outliers (see Supplementary
Material file, point SM.1, for more information).
Results showed a main effect of Session (β=0.18,
SE =0.05, z=3.96, p<0.001, 95% CI =[0.10; 0.28]),
with a higher probability to choose the robot in the
Cyberball Post-Session compared to Pre-Session. Moreover,
asignificant Session ×Type of Video interaction emerged
(β=−0.18, SE =0.07, z=−2.69, p=0.007, 95% CI =
[−0.32; −0.05]).
To investigate this two-way interaction, we first ran
two logistic models, with Type of Video (Mechanistic vs
Mentalistic video)as a fixed effect and Participants as a
random effect, separately, according to the within-sub-
ject Session (Cyberball Pre vs Post). Results showed that,
in the Post Session, participants tended to re-include
iCub more in the task only after watching the Mechanistic
video (β=−0.13, SE =0.04, z=−3.4, p<0.001, 95% CI =
[−0.21; −0.06];meanvalues=58% vs 55.7% for Mechanistic
and Mentalistic Video, respectively). Importantly, this was
not observed in the Cyberball Pre Session (β=0.10, SE =
0.07, z=0.26, p=0.79, 95% CI =[−0.06; 0.09];mean
values =52.2% vs 51.8% for Mechanistic and Mentalistic
Video, respectively)(Figure 2).
Moreover, we hypothesized that people who observed
iCub being presented as an intentional, human-like system
(i.e.,“Mentalistic”video), compared to people who observed
iCub being presented as an artificial, pre-programmed arti-
fact (i.e.,“Mechanistic”video)should tend to re-include iCub
Table 1: Mean values and SDs (in parentheses)related to the rate of
robot choice, reported separately by Session (Cyberball Pre vs
Post), Type of Video (Mechanistic vs Mentalistic Video), and Group
(General Population vs Technology Experts)
Rate of robot choice Pre Post
General population Mechanistic 51.5% (12.2)56.6% (17.8)
Mentalistic 50.6% (16.2)53.7% (17)
Technology experts Mechanistic 53% (8.5)59.3% (16.5)
Mentalistic 52.9% (10.3)57.7% (14.9)
Social inclusion of robots depends on the way a robot is presented to observers 61
more in the game. Thus, we also ran two logistic models
separately according to Type of Video (Mechanistic vs
Mentalistic video),withSession(Pre vs Post)as a fixed
effect and Participants as a random effect. Results showed
that, in the Post session compared to Pre, participants
tended to re-include iCub more in the game after watching
the Mechanistic video (β=0.20, SE =0.04, z=5.77,
p<0.001, 95% CI =[0.13; 0.27],meanvalues=52.2% vs 58%
for Pre and Post sessions, respectively).However,thiswas
not observed for participants who watched the Mentalistic
video (β=−0.03, SE =0.07, z=1.9, p=0.06, 95%
CI =[−0.02; 0.13]; mean values =51.8 % vs 55.7% for Pre
and Post sessions, respectively)(Figure 3).
Notably, the two-way Group ×Type of Video interac-
tion resulted not to be significant (β=−0.02, SE =0.06,
z=0.4, p=0.68, 95% CI =[−0.1; 0.16]; Table 1), showing
that the degree of participants’prior knowledge about
technology did not influence the probability of robot
choice according to the Type of Video (Mechanistic vs
Mentalistic video).
Moreover, also the two-way Group ×Session interac-
tion was not significant (β=−0.02, SE =0.07, z=0.37, p=
0.71, 95% CI =[−0.1; 0.16]; Table 1), showing that parti-
cipants’degree of prior knowledge about technology did
not modulate the probability of robot choice across ses-
sions (Cyberball Pre vs Post).
Figure 2: Probability of robot choice as a function of Type of Video (Mechanistic vs Mentalistic), plotted separately according to the Session
(Pre, on the left side; Post, on the right side).
Figure 3: Probability of robot choice as a function of Session (Pre vs Post), plotted separately according to the Type of Video (Mechanistic
Video, on the left panel; Mentalistic Video, on the right panel).
62 Cecilia Roselli et al.
No other main effect or interaction reached the sig-
nificance level, with all p-values >0.31.
5 Discussion
The present study aimed at investigating whether indivi-
duals’tendency to include the robot as an in-group social
partner would be modulated by (1)the degree of prior
knowledge about technology, given participants’theore-
tical background and technical education, and (2)the
way iCub was presented to observers (as an artificial,
pre-programmed system vs an intentional agent).Concerning
the first aim, we collected two samples of participants varying
in their degree of prior knowledge about technology,
namely a sample of “Technology Experts”and a “General
Population”sample with little prior knowledge about tech-
nology. To address our second aim, we asked participants to
either watch a “Mechanistic”video, in which iCub was
represented as a mechanical artifact, or a “Mentalistic”
video, in which iCub was performing a goal-directed action.
The tendency to socially include iCub as an in-group
member was operationalized as the probability to toss
the ball toward the robot during the Cyberball task (e.g.,
[18,19,23]). Specifically, we asked participants to perform
the task in two separate sessions, i.e., before and after
watching the videos, to assess whether the behavior of
the robot displayed in the video modulated the tendency
to re-include iCub in the task.
Our results showed that participants tended to re-
include the robot more in the Cyberball Post Session com-
pared to the Pre Session, but only after watching iCub
depicted as an artificial system (“Mechanistic”video).It
was not the case of people watching iCub presented as an
intentional agent (“Mentalistic”video), as they did not
show any difference in the probability of robot choice
across sessions (Cyberball Pre vs Post). Notably, these
effects were not modulated by the degree of individuals’
prior knowledge about technology, as the three-way
interaction (Session ×Type of Video ×Group)was not
significant.
Therefore, our resultssuggested thatthe way the robot
was presented to observers, but not the prior knowledge
about technology, modulated individuals’tendency to per-
ceive the robot as a social partner. This was also confirmed
by the fact that the probability of re-including iCub in the
game varied only in the Cyberball Post Session, namely
after participants watched iCub in the video.
The results showing that participants were more
prone to socially include the robot in the game only after
watching the “Mechanistic”video does not support our
initial hypothesis. Indeed, we expected that people who
observed iCub being presented as an intentional, human-
like system (i.e.,“Mentalistic”video)should re-include
the robot more in the game compared to people who
observed iCub being presented as an artificial, pre-pro-
grammed artifact (i.e.,“Mechanistic”video).
One possible explanation might be that people’s atti-
tudes toward robots are driven by their preconceived
expectations toward them, much like when interacting
with other humans [60]. For example, Marchesi and col-
leagues [61]recently investigated whether the type of
behavior displayed by the humanoid iCub robot affected
participants’tendency to attribute mentalistic explana-
tions to the robot’s behavior. Thus, they assessed the
ascription of intentionality toward robots both before
and after participants’observation of two types of beha-
vior displayed by the robot (i.e., decisive vs hesitant).
Interestingly, they found that higher expectations toward
robots’capabilities might lead to a higher intentionality
attribution, with increased use of mentalistic descriptions
to explain the robot behavior, even if it was presented as
mechanistic [61].
This reasoning is also in line with previous findings
in HRI [62,63], which suggest that when people experi-
ence unexpected behaviors displayed by robots, the posi-
tive or negative value of the violation of expectations
may significantly affect individuals’perception of robots
as social partners.
In the present study, a possible explanation might be
that if people conceive robots as artificial systems, seeing
the robot presented in this way (i.e.,“Mechanistic”video)
might confirm their existing expectations toward robots.
Therefore, it could be that if people watched a video in
which iCub behaved in the way they expected it to
behave, they would be more prone to interact with iCub
during the Cyberball game.
An alternative explanation may derive from the Computer
Are Social Actors (CASA)framework [64,65]. Originating
from the Media Equation Theory [54],itsuggeststhat
humans treat media agents, including social robots, like
real people, applying scripts for interacting with humans
to the interactions with technologies [66]. Importantly,
CASA does not apply to every machine or technology:
two essential criteria must be respected for a technology
serving CASA application [65].Thefirst criterion is social
cues, namely, individuals must be presented with an
object that has enough cues to lead the person to cate-
gorize it as worthy of social responses [65].Thesecond
criterion is sourcing. Nass and Steuer (1993)clarified that
CASA tests whether individuals can be induced to make
Social inclusion of robots depends on the way a robot is presented to observers 63
attributions toward computers as if they were autonomous
sources [67]; namely, whether they can be perceived as an
active source of communication, rather than merely trans-
mitting it or only serving as a channel for human–human
communication (e.g.,[68]). In the light of this, it might be
that participants of this study displayed more willingness
to interact with the robot only after watching the Mechan-
istic video because they perceived it as behaving autono-
mously, thus respecting the second criterion. Related to
the first criterion (i.e., presence of social cues),itisimpor-
tant to point out that the perception of what is social varies
from person to person and from situation to situation [26].
Therefore, it is difficult to clearly define an objective, uni-
versal set of parameters of what constitutes “enough”for
signals to be treated as social.
Notably, the CASA framework has argued in favor of
the potential role of individual differences such as educa-
tion or prior experience with technology. Nass and Steuer
first argued that some individual differences, including
demographics (e.g., level of education)and knowledge
about technology might be crucial when testing CASA
[67]. In line with this, also recent findings suggest that
CASA effects are moderated by factors such as previous
computer experience [69],andthatpeople’s expectations of
media agents such as social robots might vary based on their
experience [70]. Thus, prior experience with technology
seems to be relevant to CASA’s assumptions. However, our
results are not entirely in line with the predictions stemming
from this framework, as our results showed no effect of prior
knowledge about technology on participants’tendency to
socially include the robot in the Cyberball task. However,
these results need to be further confirmed by future studies,
also conducted in a well-controlled laboratory setting. In
addition, post-experiment questionnaires might be added
at the end of the experiment, to disentangle the specificroles
of each factor for the social inclusion of robots.
6 Conclusions
Taken together, these findings suggest that the way the
robot was presented to observers, but not the degree of
prior knowledge about technology, modulated individual
tendency to include the robot as an in-group social partner.
However, these first exploratory findings need to be
addressed in future studies, in more controlled laboratory
experiments (as opposed to online testing protocols).
Funding information: This work has received support from
the European Research Council under the European
Union’s Horizon 2020 research and innovation program,
ERC Starting Grant, G.A. number: ERC –2016-StG-715058,
awarded to Agnieszka Wykowska. The content of this article
is the sole responsibility of the authors. The European
Commission or its services cannot be held responsible for
any use that may be made of the information it contains.
Author contributions: C.R. designed the study, collected
and analyzed the data, discussed and interpreted the
results, and wrote the manuscript. F.C. designed the
study, discussed and interpreted the results, and wrote
the manuscript. A.W. designed the study, discussed and
interpreted the results, and wrote the manuscript. All the
authors revised the manuscript.
Conflict of interest: The authors declare that the research
was conducted in the absence of any commercial or
financial relationship that could be construed as a poten-
tial conflict of interest.
Informed consent: Informed consent was obtained from
all individuals included in this study.
Ethical approval: The research related to human use has
been complied with all the relevant national regulations,
institutional policies and in accordance the tenets of the
Helsinki Declaration, and has been approved by the
authors’institutional review board or equivalent committee.
Data availability statement: The dataset analyzed during
the current study is available, together with videos served
as stimuli, at the following link: https://osf.io/7xru6/?
view_only=cb4d0196df64465481a7fc4c90c1d6c4 (name of
the repository: “Social inclusion of robots depends on the
way a robot is presented to observers”).
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