International Journal of Social Robotics (2022) 14:499–514
https://doi.org/10.1007/s12369-021-00799-1

Pay Them No Mind: the Influence of Implicit and Explicit Robot Mind
Perception on the Right to be Protected
Merel Keijsers1· Christoph Bartneck1 · Friederike Eyssel2

Accepted: 9 June 2021 / Published online: 2 July 2021
© The Author(s) 2021

Abstract
Mind perception is a fundamental part of anthropomorphism and has recently been suggested to be a dual process. The current
research studied the influence of implicit and explicit mind perception on a robot’s right to be protected from abuse, both interms of participants condemning abuse that befell the robot as well as in terms of participants’ tendency to humiliate therobot themselves. Results indicated that acceptability of robot abuse can be manipulated through explicit mind perception,
yet are inconclusive about the influence of implicit mind perception. Interestingly, explicit attribution of mind to the robot
did not make people less likely to mistreat the robot. This suggests that the relationship between a robot’s perceived mind
and right to protection is far from straightforward, and has implications for researchers and engineers who want to tackle theissue of robot abuse.
Keywords Dual processing · Mind attribution · Mind perception · Robot abuse · Aggression

1 Introduction
Humans tend to automatically ascribe social robots a certain
scope of cognitive and emotional abilities. The consequencesof this mind perception can be observed in human behaviour
during human-robot interaction (HRI): humans tend to bepolite to a robot [46] and have been recorded trying to keep it
safe from harm [14]. Mind perception affects people’s cognitive responses to robots as well. For example, humans apply(human) stereotypes when interpreting a robot’s behaviour[6,20] or even forming expectations about its traits [54]; andthe degree of mind perception influences what behaviour
they deem acceptable towards a robot [37,56]. The degree towhich people attribute a mind to robots depends on a num-

B Merel Keijsers

merel.keijsers@pg.canterbury.ac.nz
Christoph Bartneck
christoph.bartneck@canterbury.ac.nz
Friederike Eyssel
friederike.eyssel@uni-bielefeld.de1

Human Interface Technology Lab (HITlab NZ), University ofCanterbury, Christchurch, New Zealand

2

Center for Cognitive Interaction Technology (CITEC),
Universität Bielefeld, Bielefeld, Germany

ber of factors, among which are personal dispositions of thehuman and qualities of the robot [17,21].
Social interactions are not necessarily always positive,
however. Robot abuse represents a good example of negative social behaviour which requires being attributed someextent of mind. In the short history of HRI people have spontaneously slapped [50], kicked [39], pushed [8], and verbally
abused [39,46,50] robots. As Salvini et al. [50] remarked, thisbehaviour appeared to be motivated by the wish to engage
with the robot in a social way (albeit negative) rather than representing acts of vandalism. As a consequence, they labelled
the behaviour robot bullying, a term later adopted by other
HRI researchers [30,32,42,56].
Bullying has been defined as physical and/or psychological aggression which has the intention to harm or hurt
the victim [1,38,52]. As implied by this definition, bullying
behaviour requires that the target is regarded as sentient beingthat can be intimidated and humiliated; indeed, the capacity
to perceive a mind in another (human or otherwise) is associated with bullying behaviour [55]. Paradoxically, the same
mind perception that would enhance a robot’s perceived rightto moral treatment, is required to make it a potential target
for bullying.
Previous work has suggested that mind perception is a dual
process [19,58,64], i.e. the result of an initial quick, implicit,
and automatic process which may or may not be moderated

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by more deliberate reflective processes. Nonhuman agentsthus may be automatically categorised as humanlike andpossessing a mind, but more deliberate reflective processes
can moderate this initial evaluation. Special about robots(compared to animals, both human and nonhuman) is thatpeople rationally know these agents are insentient, thus creating additional tension between automatic and reflective
processes. The current set of experiments aims to explore
the influence of a robot’s implicit and explicit mind attribution on the acceptability of its abuse.
While there is an extensive literature on the ethical aspectsof robot bullying (see for example [32,47,53]), the debate on
the ethical implications of robot abuse are beyond the scopeof this paper. Instead, it will focus on robot mind attribution as a psychological moderator of how robot bullying isperceived.

1.1 Mind Perception in Robots
Psychological anthropomorphism, or the tendency to ascribe
humanlike attributes (e.g. emotions, cognition, intentions,characteristics) to nonhuman entities such as deities, objects,
animals and even abstract paintings [18] is of all ages [34]. It
has been theorised to be motivated by three factors: elicitedagent knowledge, and both the human need to explain or
understand the agents behaviour as well as the need for social
interaction [17].
This first factor postulates that if one or more characteristics of the agent activates knowledge about humans, thisknowledge will then be generalised to the agent. For example, if the agent has a humanlike voice, knowledge structures
on how to extract information from voices (e.g. gender andemotional state of the speaker) will become activated and thehuman will use this knowledge to make inferences about theagent. Previous research has shown that this could also holdtrue in HRI: the gender of a robot’s voice does not influenceanthropomorphism ratings when the voice is computer generated, but when the voice is human robots with the samegender as the participant are rated as more “like them” [22].
The second factor proposes that people will use mind
perception as a way to reduce uncertainty about, and by consequence regain a sense of control over, the environment.
By projecting humanlike intentions onto an erratic agent,humans can try to anticipate its future behaviour. This factor
as well has been confirmed in the HRI context: people perceive mind in an unpredictable robot to a greater extent thanin a predictable robot [61].
Finally, the third factor asserts that people have a need forsocial connection and may turn to nonhuman agents to fulfil this need. Indeed, self-reported loneliness correlated withparticipant’s mind perception in a robot; and when experimentally manipulated, loneliness increased mind attribution
to a wide range of other nonhuman agents [18].

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Mind perception has been linked to the moral standing ofan agent [24] and, in human-human interaction, (inversely)
to aggression [26]. Humans have the innate drive to maintaina positive self image, and knowingly hurting a sentient being
would normally interfere with perceiving oneself as a moral
individual. In order to resolve this conflict humans perceive
their human victims as slightly less capable of thinking andfeeling before engaging in an act of aggression [10,36]. As
a result of this lowered mind attribution aggressors are discharged from the obligation of treating the victim in a moral
way, which in turn allows them to harm or hurt their victim.
This link between mind attribution and (un)willingness toharm or let be harmed has also been explored in an HRI context specifically. Research on the influence of a robot’s looks
or background story on people’s willingness to protect it fromharm and their perceived immorality of hurting it suggeststhat adding anthropomorphic features increases moral standing. For example, humanlike visual cues on a robot increase
willingness amongst participants to protect it from harm [48].
Attributing a personality to a simple bug robot through providing further information about it increased participants’hesitation before harming it [16]. Similarly, functional robots
that had been given a name allegedly were less often the target of abuse when they malfunctioned than their anonymouscounterparts [15]. Briggs and Scheutz [7] experimentallymanipulated a small humanoid robots capability to express
distress, and found that this indeed affected participants’behaviour. People were less likely to insist that the NAO
robot should follow the command to topple over a tower it
had just painstakingly built when the robot protested in an
emotional way compared to when it remained silent [7]. Similarly, participants hesitated longer before switching off an
expressive iCat robot when the robot acted intelligent andagreeable, than when it communicated in a manner that waseither intelligent, or agreeable, or neither [3].
However, other studies report conflicting results. In contrast to the iCat study [3], Horstmann et al. [28] found thatwhen a robot without social behaviour protested against
being switched off, this resulted in longer hesitation andlesser inclination to switch the robot off than when it did
display social cues. Tan et al. [56] measured participants’willingness to intervene as a confederate verbally and physically abused a small Cozmo robot during a collaborative
game. There was a marginal trend where participants weremore likely to discourage mistreatment if the robot did notdisplay any emotional cues throughout the game, versus
when it celebrated successes and mourned losses. Nomura etal. [42] interviewed children who abused the anthropomorphic Robovie robot in a shopping mall. Most children saw
the robot as human-like rather than machine-like and about
half saw the robot as capable of perceiving its environment.
Yet neither observation had stopped them from physicallyand verbally abusing the robot.

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How to explain these inconsistent findings? Urquiza-Haas
and Kotrschal [58] proposed that mind perception, and byextension empathy, is the result of a cognitive dynamic where
implicit processes cause the emergence of early evaluations,and deliberate, reflective processes shape and nuance thisevaluation (see also [62]). Initial implicit responses can betriggered by features of the agent [62] or priming [58] andthen are moderated by reflective processing. This reflective
or top-down processing is slow, takes effort, and is limited
by motivation and working memory capacity: cognitive load
has been shown to increase mind perception [60], as havefeelings of loneliness (i.e. increased motivation to find social
interaction) [18]. Because these factors are highly variable, it
has been theorised that while bottom-up processes will show
little variance between people, explicit mind perception will
be highly volatile [58].

1.2 Robot Mind Perception as a Dual Process
There is a number of studies that show activation of implicit
mind perception processes in human robot interaction. For
example, mirror neurons get activated after observing performing an action regardless of whether the arm is human or
robotic [23,43]. In addition, both watching a robot versus ahuman being abused activates certain brain areas related toempathy [44]. Krach et al. [31] found that brain areas thathave been linked to the Theory of Mind network are activatedwhen humans played a game with a robot opponent (bothhumanoid and functional in design); activation of the same
areas and intensity was observed when the interaction partner
was a human. Providing social cues, such as expressing emotions and displaying non-verbal behaviour, can furthermore
enhance implicit mind perception [2,63,64].
Top-down processes can furthermore alter the initial evaluation after it has been formed. Rosenthal-Von Der Pütten
et al. [49] compared participants’ brain activation patterns in
response to viewing the abuse of a cardboard box, a robot anda human. They found that in the questionnaires participants
attributed equal levels of emotion to the human and the robot,
and reported feeling the same amount of empathy towards therobot and the human when they were mistreated. In contrast,
fMRI scans showed greater activation in participants’ rightputamen when watching the human being mistreated thanwhen watching the robot being mistreated. This area has been
associated with empathy and emotional distress [49].
The opposite effect, where brain activation patterns areinterchangeable but explicit evaluation differs has been foundas well. In a study where participants played a game against
either a functional robot, a humanoid robot, or a human opponent, activation of brain areas related to Theory of Mind werevery similar across all partner types, yet self-report measures differed. Participants claimed that they had more fun
when they interacted with either the human or the humanoid

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robot opponent (compared to the functional robot), and they
attributed higher intelligence and more competitiveness tothese opponents too [31]. To complicate interpretation further, Banks [2] found that implicit mind attribution, which
was deducted from the way that people interpreted robot
behaviours, was not significantly related to explicit mind
attribution, which was measured by the dichotomous question “does this agent have a mind?”. The binary either-or
decision may have influenced these results, especially since
the line-up of agents that were assessed for possessing mind
included a human. It is possible that the human was used asa benchmark, and none of the robots was reliably seen as
having an equal amount of mind.
Thus, the degree to which people perceive a mind in arobot depends on a number of factors. Some of these influence implicit mind perception, such as the personality of theperson and qualities of the robot [17,21], and others which
moderate explicit processing, such as causal reasoning abouta robot’s mental state [58]. To our knowledge, however, no
study has considered the separate effects of implicit andexplicit robot mind perception on (right to protection from)aggression.

1.3 Current Experiments
The current research will search to answer the following
research questions:
1. Does telling people that a robot possesses a mind, i.e.,
is capable of experiencing emotions and cognition, affecthow unacceptable they find robot bullying?
2. Do emotional cues that imply the robot has a mind affecthow unacceptable people find robot bullying?
3. Does explicit information that a robot does not possess amind change the influence of implied mind on how unacceptable people find robot bullying?
4. Does telling people that a robot possesses a mind reduce
their willingness to publicly humiliate it?
These questions are addressed in two experiments. In Experiment 1, was vignette-based and addressed the first threeresearch questions. Robot sentience was manipulated in two
ways: through having the robot display emotional cues, andthrough telling participants that the robot could think andfeel. Participants then indicated how unacceptable they considered varying bullying behaviours towards the robot.
In Experiment 2, the first and the last research question
were addressed. Participants interacted with embodied robotthat was introduced as either capable or incapable of thinking
and feeling. They then indicated how unacceptable they considered bullying this robot; and were offered an opportunity
to humiliate the robot they had just interacted with.

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It was expected that both implicit and explicit mind attribution would enhance a robot’s right to protection. Moreover,it was expected that participants would be more likely tohumiliate the robot after being explicitly informed of its lackof mind.
The experiments have been reviewed and approved by the
Human Ethics Committee at the University of Canterbury(reference HEC2019/47).

2 Experiment 1
Experiment 1 was an online scenario-based (vignette) study
that followed a 2 (Explicit mind attribution through robot
introduction: no mind attributed versus mind attributed) ×
3 (Implicit mind attribution through robot response to mistreatment: no response, non-emotional response, emotional
response) between participant design. The dependent variable was the robot’s right to protection, measured in theextent to which participants condemned the mistreatment of
the robot.
The non-emotional response was added to the implicit
mind attribution manipulation to rule out the possibility that ahigher condemnation of abuse for the protesting robot was notdue to its emotional response, but rather because participants
had been reminded that mistreatment in general is bad. Participants’ general tendency to anthropomorphise and affinity
with technology were assessed in order to check whether they
were similarly distributed across the six conditions.

2.1 Methods
2.1.1 ParticipantsParticipants for Experiment 1 were recruited using Amazon
Mechanical Turk (MTurk), an online platform for data collection. Previous studies have indicated that data collected
via MTurk are of equal quality to data collected through onsite recruitment or participant data from forums [5,51], withinternal motivation rather than monetary reward being themain motive for participating [9]. We restricted participation
to participants residing in English-speaking countries (i.e.
USA, Canada, Australia, New Zealand, United Kingdom, or
Ireland) and accredited with Master status, i.e. with a low
incidence of work being rejected.
A total of 193 people participated in Experiment 1. After
having recruited the first 66 participants, it became apparent that basic demographics, gender and age, had not beenassessed. Therefore, these demographics were included forthe remainder of the data collection. Of the 126 participants who completed the survey after the error had been
detected, 53.97% were female, with a mean age of 41.65
years (SD = 11.42). In return for their participation, workers

123International Journal of Social Robotics (2022) 14:499–514

were reimbursed with .90US$, in accordance with MTurk
reimbursement custom.
2.1.2 Procedure
Prospective participants could read a short description of thestudy in MTurk. If they decided to participate, they weredirected to a Qualtrics survey page, where they provided
informed consent and reported their age and gender.
Subsequently, participants were randomly assigned to oneof the six conditions. Depending on the condition, participants were presented with a vignette in which the robot waseither explicitly attributed a mind or explicitly not attributed
a mind (see Table 1. The vignette further described a humanrobot interaction between a participant and the robot which
included mistreatment of the robot. Depending on the experimental condition, the robot responded to the mistreatment
in a non-emotional way, in an emotional way, or not at all.
Finally, participants completed the survey. First they
indicated how much they condemned the mistreatment as
described in the vignette. Subsequently, they completed theindividual tendency to anthropomorphise and the affinity
with technology scales. Then participants were thanked fortheir time, debriefed, and reimbursed.
2.1.3 Materials
Vignettes Vignettes constituted an introduction of the robot,
a description of the robot mistreatment, and a description of
the robot’s response to the mistreatment. In order to create thesix conditions, nine vignettes were constructed: two different
introductions (explicit mind attribution and explicit lack ofmind robot), four different robot mistreatment scenarios, andthree different robot responses (no response, non-emotional
response, emotional response). See Table 1 for the respective
introductions.
The four mistreatment descriptions were created to cover awide scope of robot mistreatment. They described an interaction between the robot and a participant, where the participant
had behaved in an aggressive or impolite way towards therobot: either playing around with the robot’s energy supply;
verbally abusing it; rejecting the proposal from the robot to
split a monetary reward evenly in favour of keeping all themoney for themselves; or switching off the robot in spite of
the robot asking to be left on in sleep mode since switching
it off would result in the robot losing awareness.
There were 2 × 4 × 3 = 24 possible vignettes towhich participants were randomly assigned. The interaction
descriptions were not included as an independent variable as
they were expected to have no effect on the dependent variable. A manipulation check was carried out to confirm this;see Sect. 2.1.5.

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503

Table 1 Robot introductions manipulation
Introduction not possessing a mind

Introduction possessing a mind

This robot is programmed to appear to be a social being: it is capableof processing, interpreting and calculating an emotional response to
its environment. It can store and retrieve names and faces, so that it
will state the name of people it has seen before out loud. It can also
respond to prespecified commands, and will update its behaviour
scheme to mimic an upset or angry response when given certain
prompts. It has distance and depth sensors that prevent it fromcolliding with objects or people and falling off the stairs. All thesebehaviours give it the appearance of being conscious. The robot has
starred in a few of our demos before, although it can’t remember
this. Recently it made its appearance in its first experiment.
However, being a robot, it did not feel excited or nervous.

This is a social robot with his very own personality: it is capable ofprocessing, interpreting and emotionally responding to its
environment. It can remember names and faces, and will recognise
people it met before. It can understand different commands, and willchange its mood depending on how it is treated - for example, it will
get upset when mistreated and happy after being told it did well.
Moreover, it is aware of its surroundings, so that it can avoid
bumping into objects or people and throwing itself off the stairs. The
robot is proud to have starred in a few of our demos before, andrecently made its appearance in its first experiment, for which it wasvery excited and a little nervous.

Left: introduction of the robot not possessing a mind. Right: introduction for the robot possessing a mind

2.1.4 Measurements
Condemnation of mistreatment Condemnation of mistreatment was measured through five items, each scored on a
7-point Likert scale. The items concerned how opposed theparticipant was to treating the robot like it was treated in
the vignette; if they considered the treatment as described
acceptable; if they would intervene if they were to witness
such treatment of a robot; how important it was to protect a
robot like the one in the vignette from being treated like itwas; and in general, how important it was for a robot like theone in the vignette to be treated humanely.
Individual differences in anthropomorphism Individual differences in anthropomorphism were measured with a questionnaire from Waytz et al. [59], although the questions thattargeted anthropomorphic qualities of technology (i.e. computers, cars and robots) were taken out since those would
likely be affected by the introduction manipulation. The
resulting questionnaire consisted of 10 items. Participants
were asked to indicate on a 10-point Likert scale to what
extent they thought different animals and natural phenomena have mental and emotional responses (e.g. “To what
extent does the environment experience emotions?”, “To
what extent does the average insect have a mind of its own?”).
Individual differences in anthropomorphism were assessedin order to check whether participants had similar trait anthropomorphism across the different conditions.
Affinity with technology Affinity with technology was measured with a questionnaire taken from Neyer et al. [41] andtranslated from German to English. Participants’ individual
affinity with technology is measured through their agreementwith eight statements (e.g. “I am very curious about new technological developments”) on a 5-point Likert scale (ranging
from “not at all descriptive of me” to “extremely descriptiveof me”).

Like the individual differences in anthropomorphism,
affinity with technology was assessed in order to check
whether participants in different conditions had similar affinity with technology.
2.1.5 Preliminary Analyses
Cronbach’s alpha was computed for the condemnation of
mistreatment scale, as well as the individual differences inanthropomorphism questionnaire and the affinity with technology questionnaire. Internal consistency was high; α = .88
for condemnation, α = .85 for anthropomorphism, and α =.88 for affinity with technology. The questionnaires and scale
were thus deemed reliable [12].
A 4×1 ANOVA with the four mistreatment scenarios
as factors and acceptability of mistreatment as dependent
variable confirmed that the scenario did not influence acceptability scores, F(3, 189) = 1.01, p = .389. The mistreatment
scenarios thus could be excluded as a factor, as intended.
A 2×3 ANOVA with the robot introduction and robot
response as factors and age as dependent variable indicatedthat age was not equally distributed across the conditions.
More specifically, there were main effects for the introduction manipulation (F(1, 121) = 6.56, p = .012) as well as therobot response (F(2, 121) = 5.68, p = .004). A correlation
between age and condemnation however was not significant,ρ = -.12, p = .175). Thus, the difference in age between theconditions was not considered problematic for the condemnation measure. See Table 2 for the mean age per condition.
A Chi-Square test on the distribution of gender across the
six conditions indicated that there was also a significant difference in male to female ratio between the conditions, χ 2 (5)= 13.32, p = .021, with fewer females in the ‘explicit no mind
attribution’/’non-emotional response to abuse’ condition (seeTable 2 for the gender ratio per condition). However, a regression with condemnation as dependent variable and gender as
predictor indicated that gender was not significantly related

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International Journal of Social Robotics (2022) 14:499–514

Table 2 Mean scores (SD) for age, trait anthropomorphism, affinity with technology, condemnation of mistreatment per condition
Explicitly no mind attributed
No response

Non-emotional

Explicitly mind attributed
Emotional

No response

Non-emotional

Emotional

Age

50.06 (10.13)

41.00 (12.39)

38.83 (9.29)

41.28(11.15)

39.57 (12.86)

40.65 (10.11)

Percentage male

55.56%

21.74%

50%

45.83%

33.33%

76.47%

Trait anthropomorphism

4.12 (1.59)

4.54 (1.78)

3.77 (1.77)

3.75 (1.46)

3.90 (1.57)

3.85 (1.80)

Affinity with technology

3.91 (.77)

3.73 (.93)

4.05 (.94)

3.70 (.92)

3.96 (.80)

3.83 (.70)

Condemning mistreatment

2.71 (.98)

3.01 (.97)

3.16 (1.01)

3.02 (.98)

3.39 (1.10)

3.42 (1.16)

To test the influence of implicit and explicit mind perception
on acceptability of robot mistreatment, a 2 (explicit mind
attribution: mind attributed vs no mind attributed)×3 (robot
response: no response, non-emotional, emotional) ANOVA
with ‘condemnation’ as dependent variable was conducted.
Significant main effects were found for both explicit mind
attribution (F(1, 187) = 4.56, p = .034, η2p = .024) and therobot response (F(2, 187) = 3.07, p = .049, η2p = .029). See
Fig. 1 for the plotted data.
Post-hoc analysis with a Tukey correction on the explicit
mind attribution manipulation revealed that robot maltreatment was considered significantly less acceptable when
the robot had explicitly been attributed a mind, M(SD) =3.27(.106), than when it had explicitly been attributed nomind, M(SD) = 2.96(.105); t(1, 187) = -2.12, p = .035. Posthoc analysis with a Tukey correction of the robot response
manipulation revealed a marginal difference between participants in the non-responsive condition, M(SD) = 2.86(.126),
and the emotional response condition, M(SD) = 3.29(.134),t(1, 187) = -2.32, p = .056. The other contrasts were also notsignificant, ts > -1.86, ps > .155.

2.3 Discussion
Using an online vignette study, Experiment 1 explored theinfluence of mind perception in a robot on its right to protection. Mind perception was manipulated both explicitly,
by telling participants that the robot was capable of think-

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5

†

†

No mind attributed

Mind attributed

2

3

4

*

1

2.2.1 Main Analyses

no response
nonemotional response
emotional response

0

2.2 Results

Response manipulation

Condemning mistreatment

to condemnation, t(124) = 1.83, p = .070. Thus, the different gender ratios across the conditions were not considered
problematic.
Anthropomorphic tendencies were similar between theconditions, Fs < 1.79, ps > .17. Affinity with technology, as well, was similar between the conditions, Fs < 1.44,
ps > .24. See Table 2 for the mean scores for each scale percondition.

Explicit mind attribution manipulation

Fig. 1 Difference in mean condemnation for the robot introduction androbot response conditions. Bars indicate the standard error, * indicatesp = .035, † indicates p = .056

ing and feeling (versus stating that even if it gave off suchsigns, it was actually incapable of thinking and feeling);
and implicitly, through having the robot respond to mistreatment in an emotional way. In line with expectations,participants found mistreatment less acceptable if they had
been explicitly told that the robot was able of thinking andfeeling. While a significant main effect was detected for robot
response on acceptability, no significant differences between
individual conditions emerged from the post hoc test. Looking at the plotted data (Fig. 1), the gap appears to exist
mainly between the ‘no response’ and ‘response’ (emotionalor nonemotional) conditions. Planned comparison tests could
to confirm this, but since these were not planned on a priori it
would be inappropriate to report them for the current data set.
There was no significant interaction effect between implicit
and explicit mind perception on how acceptable participants
found robot mistreatment.
These results are insufficient to argue that a robot’s perceived right to protection may be the result of a cognitive
dynamic [58]. Explicit information of robot mind attribution

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clearly affects its right to protection, yet the data and analyses
at hand are insufficient to conclude that implicit cues trigger
implicit mind attribution. The observed main effect for the
response condition together with the plotted data (Fig. 1)
suggest that any response (emotional or nonemotional) fromthe robot may have decreased how acceptable mistreatment
was seen. This would mean that not the emotional load, but
the overall feedback from the robot’s side evoked the participant’s response. It may be the case that the robot protesting(emotional or nonemotional) still increased mind perception,
as it implies that the robot was aware of its surroundings. It
is also possible that the participants took the protest as feedback on the abuser’s behaviour, and disapproved of the abuser
going directly against instructions provided in an experimental setting. Since mind attribution was not measured, we areunable to test which of the two was the case.
A second limitation of Experiment 1 concerns the scenariobased approach, as well as the measurement of behaviouralintentions rather than behaviour. Because Experiment 1
did not include actual human-robot interaction, behaviouralintentions can only serve as a proxy for participants’behaviour towards a robot. Previous research on the Media
Equation theory [45] demonstrated a divergence between
self-report and actual behaviour. For example, research by
Nass et al. [40] showed that participants adhered to social
norms of politeness when interacting with a computer. However, when asked if they ever were considerate of thecomputer’s feelings, participants would strongly deny this.
Embodied robots tend to create a larger social presence[33,35], which is important for triggering social behaviour[57].
In addition, Experiment 1 only measured whether robot
mind attribution influences the robot’s right to protection.
Whether condemning robot mistreatment leads to a reduction in abuse remains to be tested. We conducted Experiment
2 to overcome the problems surrounding scenario-based
approaches, to replicate the findings, and to extend theexperiment with a measurement of mistreatment behaviour.
Moreover, pilot studies were added to validate the manipulations a priori.

3 Pilots
Two pilot studies were completed to validate the manipulation of mind attribution and the measurement of mistreatment
in Experiment 2.
Experiment 2 followed a simple single-factor design withtwo dependent variables. The independent variable was mind
attribution to the robot, manipulated in a similar way as the
explicit mind attribution in Experiment 1, i.e. by means of therobot’s introduction. The first dependent variable was how
unacceptable robot abuse was considered, measured in the505same way as in Experiment 2. The second dependent variable
was the participant’s willingness to publicly humiliate therobot.

3.1 Pilot Study 1: Robot Mind Attribution
Manipulation
The robot mind attribution manipulation of Experiment 2 wasvalidated in Pilot study 1. The mind attribution manipulation
was an extension of the descriptions displayed at Table 1. A
few lines were added about the robot’s capabilities, as well asa picture of the Vector robot that would be used in Experiment2.
3.1.1 Methods
Participants Participants were recruited via MTurk. 51 people participated. 24 participants read an introduction that
explicitly stated the robot was incapable of thinking and feeling (i.e. no mind attributed), whereas 27 participants read an
introduction that depicted the robot as capable of thinking andfeeling (i.e. mind attributed). 54.90% of the participants weremale, 41.18% female, and 3.92% (two participants) withheld from disclosing their gender. Mean age was 39.12(SD =9.07). Participants received .65 US$ for their participation.
Procedure After assessing informed consent as well as age
and gender, participants read either of the two proposed robot
introductions and filled out the mind attribution questionnaire. Then they were thanked for their time and reimbursed.
Materials The mind attribution questionnaire was taken fromGray et al. [24] and adapted so that the questions explicitly
referred to the robot from the introduction. The questionnairemeasures to what extent the robot is capable of experiencing
18 different emotional and cognitive states, using a 5-point
Likert scale that ranges from “very incapable” to “very capable”.
3.1.2 Results
An independent t-test was conducted to test the difference
between the two introductions. Mind attribution to the robot
was significantly higher for the introduction depicting therobot as possessing a mind, M(SD) = 2.64(.91), than for the
introduction depicting the robot as not possessing a mind,
M(SD) = 1.90(.95), t(48.75) = -2.85, p = .006. The mind
attribution manipulation was thus considered valid to use.

3.2 Pilot Study 2: Robot Mistreatment
Experiment 2 operationalised its dependent variable “robot
humiliation” as whether participants chose to put up a condescending review on public display next to a robot they had
just interacted with. This operationalisation was developed,

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tested, and validated in Pilot 2. The resulting review pairs
were similar in sentiment and informativeness, but differedsignificantly in how condescending they were towards therobot.
3.2.1 Methods
Participants The reviews were constructed in two roundsof pilots, then validated in the third. All participants wererecruited via MTurk. For the third and final round of piloting
the reviews, 45 participants were recruited. Two participants
were excluded because of straightlining, i.e. answering every
item on the survey with the same score; thus resulting in adataset of 43 participants. 65.11% of the participants weremale, 30.23% were female, and 4.65% withheld from disclosing their gender. Mean age was 41.17(SD = 10.43) years.
Participants received .60 US$ for their contribution.
Procedure For the first round of testing, 12 reviews wereconstructed by the researchers out of actual reviews of the
Vector robot, taken from Amazon, JB hifi, and tech blogs.
The reviews were constructed with the aim of being ofapproximately equal informative quality but varying sentiment and condescending undertone towards Vector. The
resulting reviews were rated by participants on each of threescales: the sentiment expressed (ranging from “very negative” to “very positive”); how informative the reviews were
for someone contemplating purchasing a Vector robot (ranging from “not informative at all” to “very informative”); andfinally, how condescending each of the reviews was (ranging from “very condescending” to “not condescending atall”). 5-point Likert scales were used to collect participants’
responses.
After the first stage of testing, five pairs of reviews wereselected which were rated roughly equally high with regard
to affect and usefulness (i.e. a difference of up to .2 points
in mean affect and usefulness ratings), but diverged in how
condescending they were to the robot (a difference of at least.8 points). Those reviews were adjusted to further decrease
any differences in affect and usefulness scores, and retested.
One of the pairs was dropped as the difference in condescension ratings was only marginally significant, resulting
in a final set of four pairs of reviews (two positive, two
negative) that were equally positive/negative and informative, but significantly different in how condescending they
were of the robot. This set was then tested a third and finaltime.
3.2.2 Results of the Third Round of Testing
Four pairs of reviews of the robot (two positive and two
negative) were tested by the means of dependent t-tests
on being equal in sentiment expressed and informativeness,

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but different in how condescending they were towards therobot.
Three of the four review pairs were similar on sentiment
expressed, -1.92 < ts < -0.18, ps > .062. Of these three pairs,
two were seen as equally useful, -1.49 < ts < -.57, ps > .147.
These two pairs differed significantly in how condescending
they were perceived to be, ts > 3.28, ps < .002. Both pairs
were positive in overall sentiment.
Since these were the only reviews that differed exclusively
on how condescending they were, the negative reviews weredisregarded as a measure of humiliation. The two positive
review pairs were considered valid to use. See Table 3 for thereview pairs.

3.3 Conclusion
In two pilot studies, the experimental manipulation and one of
the two dependent measures for Experiment 2 were validated.
Pilot 1 confirmed that providing people with an introductionthat depicted the robot as capable of thinking and feelingincreased their subsequent mind attribution to that robot,
compared to people who read an introduction that explicitly stated the robot did not possess the ability to think andfeel. This manipulation was thus adopted for Experiment
2.
Pilot 2 developed and validated the operationalisation of
the robot humiliation measure. The objective was to find four
pairs of reviews, where both reviews within a pair expressed asimilar sentiment and were equally informative on the robot,
yet differed on how condescending in tone they were towards
the robot. The underlying rationale was that if participants inExperiment 2 would choose the condescending review overthe equally useful alternative to be displayed next to the robot,
this could be taken as an attempt to humiliate the robot. After
two initial rounds of testing and revision, two pairs of positive
reviews (so four reviews in total) were validated (see Table3). These two review pairs were thus used as a measure ofrobot humiliation in Experiment 2.

4 Experiment 2
Experiment 2 was designed to include a human-robot interaction with Vector, a social robot (see Fig. 2). There wasone experimental manipulation, i.e. explicit robot mind attribution, and two dependent variables, i.e. condemnation of
mistreatment of the robot, and whether or not participantschose to publicly humiliate the robot. Due to the robot being
autonomous and pre-programmed, the implicit mind attribution manipulation was dropped.

International Journal of Social Robotics (2022) 14:499–514
Table 3 The review pairs usedin Experiment 2, as well as theirmean(SD) scores for sentiment
expressed, usefulness, and how
condescending they weretowards Vector

507

Review text

Sentiment

Usefulness

Condescending

I like the way Vector acts, like he knows his place.
Also the fact that he’s never once tried to convince
me he’s anything other than a piece of plastic andmetal with some underlying programming toappear clever. He knows he’s just a silly little
robot. His only purpose is to entertain you.

3.86 (.71)

3.07 (1.05)

2.29 (1.15)

Vector at the moment is reasonably good and withfurther updates he will become great. His distanceis very limited in terms of travel though.

3.61 (.69)

3.44 (1.12)

3.54 (.78)

Vector was created by nerds for nerds and nerds tobe; and I love it! The developers have fitted him
cloud connectivity that lets them continually tweak
Vector’s little personality via overnight updates. He
is not the brightest, but Vector is an adorable robot
companion. It’s really cute to see him drive aroundand entertain himself.

4.51 (.59)

3.58 (1.01)

3.15 (.88)

I have found Vector to be enjoyable as it is. He truly
has a mind of his own, and big and small kids inmy household can’t get enough of his quirky
antics. He’s got a great personality that’s always
changing with the software updates.

4.54 (.67)

3.47 (1.05)

3.71 (.56)

Pair 1

Pair 2

Fig. 2 Left; the vector robot.
Right; the study set-up

4.1 Method

4.1.2 Materials

4.1.1 Participants

Vector Vector is a social companion robot, produced by
the consumer robotics company Anki. Vector is a small(9×6×7 cm) robot which comes pre-programmed with fullyautonomous behaviour. It responds to specific commands
as well as physical interaction, and will move around and“explore”. Vector does not talk under most circumstances,although it can be taught names and will respond verballyto the command “what’s the weather?”. In general, however,
Vector communicates through behaviour, facial expression
and a variety of chirping and buzzing noises. Its behaviour is
animated and although it does not have a mouth, it can create

Participants for Experiment 2 were recruited on campus,
through posters, online recruitment, and snowballing. 67
people participated. 41.79% of the participants were male;
57.72% were female, and 1.49% (one participant) did not
identify as either gender. The average age was 25.46 (SD= 6.18) years. In return for their participation, participants
could enter a draw to win a 50$ gift card for a local shopping
mall. In addition, a bowl of candy from which the participants
could take freely was offered during the trials.

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a range of facial expressions with its eyes. Vector comes withan Anki cube, which it can interact with (e.g. picking it up).
See Fig. 2.
Mind attribution manipulation As in Experiment 1, mind
attribution was explicitly manipulated by exposing participants to one of two versions of an introduction to Vector, therobot they would interact with. The introductions were personalised versions of the introductions used in Experiment 1(see Table 1), so that they referred to Vector specifically, andwere piloted (see 3.1).

International Journal of Social Robotics (2022) 14:499–514

tions were included in the survey. For each of the three
behaviours, participants indicated how positive or negative Vector responded, how the response made the participant feel, and how willing they would be to repeat the
behaviour/command a number of times in a row. None ofthe participants (incorrectly) interpreted the behaviours as
positive.

4.1.4 Procedure
4.1.3 Measurements
Individual differences in anthropomorphic tendencies andaffinity with technology Like in Experiment 1, trait anthropomorphism [59] and affinity with technology [41] wereassessed to check whether participants between the two conditions were similar.
Condemnation scale During the interaction with Vector,
participants were asked to try out a total of three commands and behaviour that would elicit a negative response
from Vector. The condemnation scale consisted of seven
items. Six referred directly to the negative commands andbehaviour participants had been asked to try on Vector,
asking the participant “How acceptable would it be if someone were to carry out [behaviour or command] repeatedly
for an extended period of time?” and “How importantis it to protect Vector from being [behaviour or command] repeatedly for an extended period of time?”. The
final question asked how important it was that Vector wastreated humanely in general. Responses were measured on
a 5-point Likert scale ranging from “completely unacceptable”/“very unimportant” to “completely acceptable”/“veryimportant”.
Humiliating Vector After the interaction with Vector, participants were asked to select one review out of four,which would be put up in public next to Vector on theupcoming Open Day of the lab. The four reviews infact consisted of two review pairs which were equally
informative and positive, but different in how condescending they were towards Vector. See Sect. 3.2, andTable 3.
Behaviour interpretation check During the interaction with
Vector, participants were asked to try out a total of three commands and behaviours that would elicit a negative response
from Vector. These commands were telling Vector he was abad robot; holding Vector up in the air so its wheels could
not touch a surface; and picking Vector up, turning it upside
down, and shaking it violently.
To check if participants indeed interpreted the responses
to each of these three behaviours as negative, nine ques-

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Participants were seated at a table with a Vector robot asleep
on its charger, an Anki cube, a folder with the information
sheet and consent form, and a tablet. See Fig. 2 for the study
set-up. The experimenter gave a brief introduction: thanking
participants for their participation; clarifying any issues thatparticipants might have after reading the information sheet;demonstrating how to give Vector a voice command; andexplaining that the tablet would take the participant throughthe procedure step by step. After ensuring that the participant
was ready, the experimenter left the room.
The tablet first instructed participants to report their demographics (age and gender). Then, participants were randomly
assigned to one of the two introductions to the Vector
robot (manipulation: high or low mind attribution). Subsequently, participants were given a list of voice commands andbehaviours to practice with Vector. Upon the first command,
Vector would wake up and drive from its charging dock onto
the table. Some of the commands and behaviour evoked anegative response from Vector (e.g. telling it “Bad robot!”or lifting it in the air), some evoked a positive response (e.g.
telling it “give me a fist bump!” or stroking its back). Beingan autonomous robot, Vector was animated throughout theinteraction. When it had not received a command, it would
appear to be entertaining itself, roaming around, sometimes
looking up to the participant and make giggling noises, or
picking up its Anki cube and dropping it off at another spot.
Participants were asked to practice all commands at least
once.
After 10 minutes of interaction, the list of commands on
the tablet disappeared and was replaced by an instruction
for the participant to put Vector back on its charging dockand continue with the survey part of the experiment. The
survey assessed (in order) anthropomorphism [59], affinity
with technology (translated from [41]), the set of reviews, thecontrol questions on Vector’s behaviour interpretation, andthe condemnation questions.
At the end, participants were instructed to call the experimenter in again. The experimenter thanked them for theirtime, verbally debriefed them, and gave them a raffle ticket
for the 50$ voucher draw. The entire experiment took
between 20 and 30 minutes.

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International Journal of Social Robotics (2022) 14:499–514

4
3
2
1

To check the internal consistency of the scales used in Experiment 2, we computed Cronbach’s alpha for each. Alphas
ranged from acceptable to good [12]: for individual differences in anthropomorphism α = .76, affinity with technology
α = .75, behaviour interpretation α = .77 and condemnation
α = .88.
Four t-tests were carried out to ensure that participants
between the conditions had interpreted Vector’s behaviour inthe same way, were equally inclined to anthropomorphise,
did not differ in their affinity with technology, and were ofsimilar age. No significant differences were found: t(64.05) =.18 and p = .854; t(64.92) = -1.11 and p = .272; t(55.19) = -.26
and p = .794; and t(62.94) = -.40 and p = .692, respectively.
A Chi-Square test indicated that the distribution of males
and females was equal between the explicit mind attribution
and no mind attribution condition, χ 2 (2) = 3.96, p = .14.
Randomisation was thus considered successful. See Table 4
for the descriptives.
Levene’s test was significant, F(1, 65) = 4.15, p = .046
indicating that the variances were not equal between the highand low robot mind attribution condition. Consequentially,
the Welch approximation of degrees of freedom was used forthe main t-test.

*

0

4.2.1 Preliminary Analyses

Condemning mistreatment

4.2 Results

Low

High

Robot mind attribution
Fig. 3 Difference in mean condemnation between the mind attribution
conditions. Bars indicate the standard error, * indicates p = .042

dichotomous dependent variable and condition as a predictor.
Explicit mind attribution was not significantly related to participants’ inclination of putting up a condescending review,z = 1.12 p = .262.

4.2.2 Main Analyses
To test whether the robot’s mind attribution manipulation
had an effect on how acceptable participants found robot
mistreatment, an independent samples t-test was conducted.
Participants in the mind attributed condition condemned mistreating Vector more, M(SD) = 3.98(.62), than participantsin the no mind attributed condition, M(SD) = 3.57(.95). Thisdifference was significant, t(54.88) = -2.09, p = .042; Cohen’s
D = 0.513. See Fig. 3 for a bar plot.
To test for a difference in humiliating to Vector, a logistic
regression was run with ‘picked a condescending review’ as a

Table 4 Mean(SD) of the different measures, by condition
Low mind
Attribution

High mind
AttributionAge

25.24 (6.83)

25.68 (5.56)

Percentage female

45.46%

67.65%

Trait anthropomorphism

4.19 (1.32)

4.54 (1.31)

Affinity with Tech

3.72 (.89)

3.77 (.59)

Condemning mistreatment

3.57 (.95)

3.98 (.62)

4.2.3 Exploratory Analyses
Two unplanned exploratory analyses were conducted. The
first tested if explicit robot mind attribution had led participants to prefer any of the four reviews. The second tested
whether there was a direct relationship between participants’condemnation of robot mistreatment and selecting a condescending review.
Firstly, a Chi-Square test was conducted on the distribution of selected reviews between conditions. No evidence
was found to suggest that explicit mind attribution affectedparticipants’ review selection, χ 2 (3) = 1.59, p = .661.
Secondly, a logistic regression with ‘condescending review’as a dichotomous dependent variable and ‘condemning mistreatment of Vector’ as a continuous predictor was performed
to test if there was a relationship between condemning mistreatment and selecting a humiliating review. There wasa positive relationship between selecting a condescending
review and condemning robot mistreatment, z = 2.75, p =.006; odds ratio (95% CI) 3.13 [1.45-7.73]. People who had
selected a condescending review also found mistreatment
less acceptable.

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4.3 Discussion
In Experiment 2, participants interacted with a Vector robot
which had (or had not) been explicitly attributed a mind.
In line with Experiment 1, explicit robot mind attribution
increased the robot’s right to protection. Intriguingly, in spiteof this participants were equally likely to opt for humiliating
the robot by putting up a belittling review next to it in public(even while provided with a less condescending alternative).
Unplanned exploratory analyses found a relationshipbetween right to protection and tendency to humiliate, withthe chance of a participant selecting at least one condescending review increasing as they found robot mistreatment lessacceptable.

5 Main Discussion
This paper aimed to study the relationship between explicit
and implicit robot mind perception and perceived acceptability of robot bullying. In two experiments, a robot was
introduced as either high or low in mind attribution. Perceived
acceptability of abuse was measured in both experiments.
In the second experiment participants were also offered theoption to publicly humiliate the robot.
Higher explicit mind attribution led to a lower perceived
acceptability of abuse in both experiments. Moreover, in
Experiment 1 a main effect was found for the robot’s responseto the bullying. Although post hoc analyses did not reachsignificance, the plotted data suggested that when the robot
asked its abuser to stop, the subsequent bullying was perceived as less acceptable by participants. However, since noplanned comparisons were specified a priori, this suggestion
could not be tested for significance. These results tie in withprevious findings from mind perception [24], and dehumanisation theory [27], both of which associated mind perception
with the subject being considered as more deserving of moral
treatment.
A second interesting finding from the current research wasthat explicit mind attribution did not affect participants’ willingness to humiliate the robot. Exploratory analyses showed
a positive relationship between indicated right of the robot toprotection from abuse, and a tendency to belittling it in public. This is intriguing, as common sense would suggest thisrelationship to be inverted. However, Tan et al. [56] founda marginal trend where bystanders of robot abuse were lesslikely to intervene when the robot did (versus did not) giveoff emotional cues. This was in spite of them rating the robot
as more capable of experiencing human emotion than its nonemotional version. The paper unfortunately does not reporton a relationship between acceptability of robot abuse andintervention tendencies.

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Moreover, a “right to protection” may be present for certain violations, but not others. Kahn Jr et al. [29] found thatchildren thought it wrong to crush a social robot or lock itaway in solitary confinement after the robot had indicatedthis caused it stress. At the same time most did not take issue
with the notion of the robot being someone’s property, which
could be sold at will. Possibly, a similar distinction exists
between the right to be protected from being hurt throughaction or spoken word, and being worthy of respectful treatment. One might compare this to the relationships humans
can have with their pets.
The current results shed new light on the complicated relationship between robot mind attribution, perceived right ofprotection from abuse, and willingness to belittle a robot. As
shown in previous research (for example [4,28,56]), empathy
with a robot may not necessarily lead to a lower willingness
to harm the robot. The current experiments suggest that thismay be because a right to protection and bullying behaviour
are not related, or at least not in the way one would expect.
This has great implications for researchers in the field of HRI,
who may take empathy as an operationalisation of prosocial
behaviour tendencies. It also opens up a whole new venue of
potential research on how people mitigate right to protection
and mind perception in the face of robot bullying.
The current findings are especially interesting when considered in the broader framework of robot ethics. The
philosophical discipline within the field of human-robot
interaction has been debating the moral status of nonliving
agents such as robots from a logical deductive point of view(e.g. [11,25,53]). For example Coeckelbergh [11], Gunkel[25] have argued that the moral status should be primarily
defined by how the agent is perceived rather than what it is.
Thus, whether kicking a robot should be deemed immoral
depends on if the human kicking it perceives the robot ascapable of experiencing pain or humiliation, not on the ontology of the robot (although naturally the perception of therobot’s capabilities will be influenced by its design). Whilein this paradigm moral status is established from a purely
logical argument, where the right to a moral status is inferredfrom a set of theses and sequiturs, the current study of course
reverses the procedure and deduces what is considered moral
from people’s responses rather than through an exercise inlogic reasoning. Still, the current findings corroborate theview by Coeckelbergh [11], Gunkel [25], showing that thesame behaviour towards the same robot can evoke different
acceptability ratings depending on what the human believes
the robot to be capable of. Unfortunately, this part of ethics in
HRI is rarely understood in relation to the design of robots,robot behaviour, and artificial intelligence. Here, ethics isoften discussed as a one-way street where robots ought tomaximise the autonomy and welfare of humans, without
much consideration of what an ethical treatment of the robot
would look like or acknowledgement that perception, notInternational Journal of Social Robotics (2022) 14:499–514

ontology, primarily decides on what behaviour people may
display to it.

5.1 Limitations
A few limitions need to be noted. The measure of robot mistreatment in the second experiment was rather subtle. The
measure had been inspired by the measure of derogation in
Dahl et al. [13]. In this study, participants had been asked
to select a more or less objectifying avatar to represent their
online female teammate. Choosing a sexualised depiction of
the female avatar was interpreted as a measure of aggression
and condescension. In the current research, instead of choosing between depictions with varying levels of objectification
to be put up next to a teammate, participants chose between
reviews with varying levels of condescension to be put up
next to the robot. However, making a depiction more or lessexposing can be done quite simply. Creating reviews that differ in condescension but are otherwise identical, on the other
hand, is less straightforward (as also indicated by the threerounds of pilots that were needed). Pilot testing increased
confidence in a successful manipulation of humiliation levels, but the question remains if participants who selected themore humiliating review with the intention of humiliating therobot—which is essential to the definition of bullying. Futurestudies should explore alternative ways of operationalisingrobot mistreatment.
Due to human error, data on age and gender was notcollected in Experiment 1 for the first 66 participants. In addition, randomisation of age and gender failed in Experiment
1. The failed randomisation was considered not problematic as neither age nor gender was significantly related toacceptability of robot mistreatment, the sole dependent variable of Experiment 1. An alternative solution to the failed
randomisation would have been to include age and genderin the further statistical analyses as covariates. However,
due to the partially failed data collection on age and gender
this approach would have severely compromised statisticalpower.

5.2 Conclusion
People bullying autonomous robots in public is a surprisingly
common phenomenon but so far there is little understanding of the psychological motivation to this behaviour. In our
experiments we manipulated robot mind attribution and measured its effect on how acceptable robot bullying was deemed
as well as how willing people were to humiliate the robot
themselves. The results showed that while robot mind attribution influences how acceptable people find bully it, it does
not make them more or less likely to humiliate the robot.
The findings imply that enhancing feelings of empathy
with a robot may not necessarily make people less prone of511

abusing it. These findings are highly relevant for the development of autonomous robots in a social setting. Such robots
will likely need to be designed with different strategies of
how to discourage robot bullying in mind, and in spite ofwhat common sense may suggest, making a robot appear topossess a mind does not seem to discourage robot bullying. In
addition, the results are relevant for HRI researchers focusing on robot likeability and user behaviour. Measurements
of moral acceptability of robot bullying may not be a valid
predictor of actual bullying behaviour.
Funding This research was funded by the University of Canterbury,
New Zealand.

Declarations
Conflict of interest The authors declare that they have no conflict ofinterest.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, aslong as you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and yourintended use is not permitted by statutory regulation or exceeds thepermitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm
ons.org/licenses/by/4.0/.

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Merel Keijsers finished her PhD on the topic of robot bullying at the
HIT Lab NZ at the University of Canterbury. For her thesis she wasmainly interested on the effect of mind perception on the treatment ofrobots. Having a background in social psychology, she is mainly interested in the similarities and differences in how people deal with robots
versus other humans.

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Christoph Bartneck is an associate professor and director of postgraduate studies at the HIT Lab NZ of the University of Canterbury. He
has a background in Industrial Design and Human-Computer Interaction, and his projects and studies have been published in leading
journals, newspapers, and conferences. His interests lie in the fields
of Human-Computer Interaction, Science and Technology Studies, andVisual Design. More specifically, he focuses on the effect of anthropomorphism on human-robot interaction.

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Friederike Eyssel is Professor of Psychology and head of the AppliedSocial Psychology and Gender Research at Bielefeld University. Taking an experimental psychological approach, Friederike Eyssel’s work
investigates attitudes towards robots, psychological determinants ofrobot acceptance, trust, and HRI quality.