[1]Benjamin Lebrun

1]School of Psychology, Speech and Hearing, University of Canterbury,
Christchurch, New Zealand

2]Department of Computer Science and Software Engineering, University of
Canterbury, Christchurch, New Zealand

# Too good to be true: People reject free gifts from robots because they infer
bad intentions

<EMAIL_ADDRESS>Andrew Vonasch
<EMAIL_ADDRESS>Christoph Bartneck
<EMAIL_ADDRESS>[  [

###### Abstract

A recent psychology study found that people sometimes reject overly generous
offers from people because they imagine hidden “phantom costs” must be part of
the transaction. Phantom costs occur when a person seems overly generous for
no apparent reason. This study aims to explore whether people can imagine
phantom costs when interacting with a robot. To this end, screen or physically
embodied agents (human or robot) offered to people either a cookie or a cookie
+ $2. Participants were then asked to make a choice whether they would accept
or decline the offer. Results showed that people did perceive phantom costs in
the offer + $2 conditions when interacting with a human, but also with a
robot, across both embodiment levels, leading to the characteristic behavioral
effect that offering more money made people less likely to accept the offer.
While people were more likely to accept offers from a robot than from a human,
people more often accepted offers from humans when they were physically
compared to screen embodied but were equally likely to accept the offer from a
robot whether it was screen or physically embodied. This suggests that people
can treat robots (and humans) as social agents with hidden intentions and
knowledge, and that this influences their behavior toward them. This provides
not only new insights on how people make decisions when interacting with a
robot but also how robot embodiment impacts HRI research.

###### keywords:

Decision-Making, Intentionality, Online and In-Person Settings, Phantom Costs,
Robot Embodiment

##  1 Introduction

Humans are social agents who who engage in social cognition to help them
interacting with other humans and predicting other’s behaviors, such as
attributing them a mind and intentions (see [2, 9, 13]) such as selfishness.
People expect others to be selfish, at least to some extent [25]. However,
unlike selfishness, previous research has shown that people who are
excessively generous for no known reason lead others to imagine hidden reasons
to justify such generosity [50, 51]. While conventional economic decision-
making models would suggest that people are more keen to accept an offer when
more money is proposed (see “price effect” in [11]), recent research showed
that you might consider this offer suspicious and be less likely to accept the
offer [50]. To consider behaviors as suspicious, people need to first consider
the overly generous person as a social actor but also to imagine what his or
her intentions are. It is not known however, how humans would react if the
agent making an overly generous offer was not a human but a robot.

While it has been shown that people can attribute intentions to social robots
[23, 48], robots are still different from humans, physically and behaviorally.
Robots are expected to interact with humans and help them with different tasks
in different contexts, such as healthcare services. It is important to know
how robots need to sufficiently justify their behaviors to increase the
likelihood of people accepting their help. Exploring people’s behaviors in
economic situations involving a robot making an outstanding offer would
provide this information by giving insights concerning the manner people
perceive and behave with robots but also how those robots should interact with
humans to get adequate interaction.

###  1.1 Heuristic of Sufficient Explanation Model

People use explanations in order to understand the world (e.g., nature,
people, mental states, behaviors, etc). When communicating, people are
vigilant about information provided by others and tend to determine whether
the information they received is reliable in order to avoid deception and/or
misinformation [46]. Little is known however about how people act when no
sufficient information about specific deeds has been provided. When someone is
acting in a way that is not expected by other people, such as being overly
generous, people try to understand the reason why this person is acting that
way [37].

While classic economics models suggest that people are mainly rational and aim
to maximize gains [11], other scholars found that some people show a loss
aversion bias [16]. For them, avoiding losses is more important than
prioritazing gains. In this context, what would happen is someone is overly
generous with someone by giving more than what would already be expected?
Vonasch et al. [50] looked into this matter and found that when someone is
making an offer that seems “too good to be true”, people are more likely to
refuse it. Refusing generous offers seems to go against common sense and
rationality as we may expect people to accept generous offers if no explicit
reason is given to doubt the offer.

Vonasch and collaborators described the tendency of money backfire in their
Heuristic of Sufficient Explanation model – or HOSE – [50, 51]. This model
aims to explain people’s decisions based on the explanations they received
about the situation. More specifically, it proposes that that when an
explanation about an event seems insufficient, people will look for hidden
reasons to explain why this happens to fill the gap, such as trying to find
the “real” reasons and intentions of the person making the offer. In the case
of over generous offers, people will not necessarily choose to maximize their
monetary incentives but can rather make their decisions based on underlying
beliefs and expectations. This model is applicable in different contexts such
as economic decision-making, belief in conspiracy theories, or judgements of
other agents’ bad intentions [51].

This model highlights that people are suspicious when explanations are
considered as insufficient. This suspicion serves to protect themselves from
danger and understand and predict future behaviors better, people will look
for reasons that may be correct or incorrect. If imagined reasons are correct,
people will have behaviors adapted to the situation. If reasons imagined were
to be misleading, people will imagine phantom costs that may negatively
influence their behaviors.

###  1.2 Phantom Costs

The HOSE model explains that when people receive insufficient explanations,
they imagine hidden reasons to fill the gap. These hidden reasons are often
phantom costs. People imagine phantom costs when someone makes and overly
generous offer without explicit reasons to justify why Vonasch et al. [50].
This is grounded on the idea that people are social agents, not only self-
interested economic agents. For phantom costs to occur, three criteria need to
be met [50]. First, people expect others to follow the norm of self-interest.
Second, this expectation has to be violated. Third, no sufficient reason is
provided to justify this violation. In the face of such unselfish behavior –if
no sufficient reasons justify this–, people start being suspicious and looking
for reasons explaining unjustified behaviors [37]. Because people generally
expect other humans to pursue self-interest, they imagine phantom costs when a
human offers too much in a transaction.

Vonasch et al. [50] showed that people imagine phantom costs when a person was
offering people money to eat a cookie. Why would this person not want them for
him/herself? Moreover, why would this person pay others $2 to eat them. This
behaviour is in conflict with the self-interest expectation. Furthermore,
he/she did not offer any explanation for his generosity. All three criteria
described earlier for phantom costs to occur are met and hence, people are
likely to reject the offer. This shows that phantom costs lead to money
backfire where offering money mitigates people’s acceptance rate in the offer.
This money backfire effect is explained via the reduction of the net
psychological value, including both the money and phantom costs associated to
the transaction [50]. This is because money modifies the potential costs
people might perceive during a transaction. These phantom costs have been
shown not only with food (cookie experiment) in several contexts such as
buying cheap flights, being paid to take a specific path etc [50]. As a
baseline for our study, we aim to replicate the results of the experiment
involving cookies [50] and hence lead us to the first research question:

  1. RQ1: Do phantom costs exist in a human-human interaction? - This is a replication of Vonasch et al. [50].

We consider that these phantom costs do occur because people infer intentions,
mostly bad intentions, to those making the offer (e.g., the person poisoned
the cookies). But what about robots? People may or may not expect them to act
like other social agents and pursue their own self-interest. The present
research tested whether people ascribe such intentions to social robots.

###  1.3 Intention Attribution

Intentional stance corresponds to predicting and explaining other’s behaviors
by attributing them mental states such as intentions and beliefs [9].
Ascribing intentions (in the sense of having a mind) to robots seems twofold.
One might argue that attributing their owns intentions to robots is not
possible since they are mere machines and should not be treated as intentional
agents. On the other way, some theories, such as the Media Equation and the
Computer as Social Actors theories [29, 38], argue that people behave with
technology, such as media or computers, as they would do with another human.
If so, if people do ascribe intentional behaviors to robots, it is likely that
these behaviors can be perceived as suspicious and leading to phantom costs.

Some scholars focus their research on the ascription of intentionality to
robots and consider that people can adopt the intentional stance towards
robots, at least to some extent [23]. Roselli et al. [41] showed education
background differences where people who have a high demand of mentalizing
skills (e.g., psychotherapists) do attribute intentionality to robots more
often than those who do not need these skills (e.g., roboticists). Another
study was conducted by Thellman et al. [48] where participants had to assess
screen embodied agents that were a human and a robot doing different behavior
on different criteria including the attribution of intentionality. Overall,
similar results were found between both human and robot displayed behaviors.
de Graaf and Malle [8] extended this idea by analyzing the explanations of
both humans’ and robots’ behaviors. To do so, they presented verbal
descriptions of robots’ behaviors (so without visual representation of the
robot) and participants had to explain in their own words why the robot was
performing this action. They found that people use similar explanations for
both agents, meaning that people do attribute intentionality to robots.

According to the aforementioned theories and claims, we expect phantom costs
to occur not only in a human-human interaction but also in a human-robot
interaction. Phantom costs have the capacity to influence human decision-
making processes and the way they will perceive their interlocutor.
Understanding the relevance of phantom costs in HRI is crucial. This study
aims to provide new insights into how humans make decisions when they interact
either with a human and a social robot. Phantom costs are not directly visible
to people and can even just be imagined if perceived insufficient information
is provided. Therefore, they are challenging to predict. Being aware of their
existence in HRI will help to better understand hidden and contextual factors
influencing human decision making in HRI. While several studies explore
decision making in the context of human-robot interaction, none, to our
knowledge, explored such hidden factors that influence human decision-making.
A need for transparency is necessary. Transparency is defined in Wortham et
al. [53] as “the extent to which the robot’s ability, intent, and situational
constraints are understood by users.” In this context two research questions
emerge:

  1. RQ2: Do phantom costs exist in a human-robot interaction?

  2. RQ3: Is there a difference in terms of phantom costs when the agent is a human versus a robot?

Whereas Vonasch et al. [50] highlighted phantom costs in online and in-person
studies, results seemed to be heading in the same direction but with different
amplitudes. The authors showed, for the in person and online experiments
respectively, an acceptance rate of 39.4% and 55.2% when the offer was not
associated with a monetary outcome compared to 20.5% and 34.6% when it was the
case. However, even if these both study designs were a bit different,
differences between these settings and more specifically in HRI (after having
discusses about [48] above) need to be discussed further as both study
settings and robot embodiment are known to influence results in HRI.

###  1.4 Online and In-Person Studies

Recruiting participants using crowdsourcing platforms, such as Prolific or
Amazon Mechanical Turk, is faster, cheaper, and allows a higher demographic
diversity of participants [7]. More importantly, online studies ensure that
all participants will have exactly the same interactions allowing better
control [28]. However, using these online services might provide different
conclusions as participants are not in a real situation. Facing this debate,
scholars attempted to determine if results are similar between online and in-
person settings. While some considered that results are pretty similar [12],
some others discussed potential risks of using online settings such as the
deterioration in results over time [47] or the use of sophisticated bots [14].
As specified earlier, Vonasch et al. [50] found similar conclusions in both
settings. It therefore seems important that we validate our results in terms
of robot embodiment, not physically embodied in the online setting, and
physically embodied in the in-person setting. This will show whether robot
embodiment influences the perception of phantom costs.

Unfortunately, in the field of human-robot interaction, not all laboratories
and scholars have a real robot, questioning the usability and similarity of
results between according to agent’s embodiment needs to be addressed.

###  1.5 Robot Embodiment

Robot embodiment is an important debate in HRI research. Scenario-based media
are frequently used in the literature in place of real interactions between a
human and a robot. They are defined as “discounted media” as only partial
information is provided [36, 56]. For the sake of clarity, we will refer to
the discounted media as “representations.” These robot representations could
be of different types such as pictures, videos, virtual agents, or simply
text-written scenarios. Xu et al. [56] used the model of prototyping fidelity
[24] to distinguish these representations according to their fidelity and
interactivity. The former corresponds to the “level of detail a feature or
sequence is represented” (e.g., visual, tactile). The authors also defined
interactivity as “the level of feedback that a product or a system affords an
evaluator’s engagement with the product.” The authors gave the example of high
interactivity when the user can directly interact with the robot without a
limitation, and a low level of interactivity happens when the user is a mere
observer of the interaction.

While a live interaction is high for both fidelity and interactivity levels,
text and pictures are low. They describe the Wizard-of-Oz method (i.e.,
controlling the robot without people to know) as high in terms of
interactivity but intermediate in terms of fidelity. Even so, it is already
known that pictures can elicit human reactions and emotions, the use of the
Self-Manikin Assessment associated with the International Affective Picture
System is a good example [5].

Several scholars explored the effect of robot embodiment. Screen embodied
robots have several advantages over physically embodied agents. They allow
researchers to run studies online, and no risk of having the robot crashing
during an experiment can happen [4]. Using such screen embodied robots is
nowadays easy since Phillips et al. [33] who created a database of real robots
photos. Xu et al. [55] specified that text-written scenario proposes high
flexibility in their creation but also a reasonable power of the results on
user acceptance.

It is of course possible to go beyond images. In their review, Jung et al.
[15] found that about 10% of the HRI research evaluating the usability of
social robots are using photos, and another 10% are using videos. We can
expect these percentages to be extended and more or less similar to all the
HRI research. It seems of the utmost importance to know if results are the
same using different level of embodiment (screen and physically embodied).
Woods et al. [52] found moderate to high agreement on several between videos
and live interactions on preferences for specific robot behaviors (i.e., the
manner the robot was approaching the human). While photos are static
representations of the robot, videos can show the dynamic of the interaction
with a more realistic viewpoint of the robot [55]. However, our study did not
focus on this dynamic but more on the perception of the robot. Moreover,
looking at the crowdsourcing platform Prolific, we can easily see that studies
which require participants to use sounds (so most likely to be a video) have a
very slow recruitment rate. At the day this paper is written, two out of the
three proposed studies require sound and are recruiting their participants
since weeks and even a few months. As an example, a last check on Prolific on
January 29, one of the study required 800 participants. For now, 614 people
participated in the study, and the researcher recruited only 5 participants
the last two days.They seem to not get their required sample size easily. It
seems reasonable to think that requiring sound mitigates the likelihood of
recruiting participants and therefore decreases the main advantage of the
online studies which is to recruit faster. For our purpose, it seems
reasonable to think that a photo of the agents is enough.

While some scholars showed more positive interactions in favor of physical
robots, such as greater trust [3], others found better results for virtual
social robots or even mixed effects (see Oliveira et al. [31] for a review).
Li [21] also analyzed 39 effects reported in HRI papers and showed that 79%
were in favor of physically present robots and only 10% were in favor of non
physically present robots. [49] proposed that it is not really the physical
presence that matters but rather its social presence. The latter is defined as
experiencing artificial objects as social actors as they manifest humaneness,
such as a conversation [19]. This occurs when people respond to agents as if
they were humans [20]. We consider that both plays a role in the attitudes
towards robots but that the one does not replace the other. If the social
presence seems to be sufficient, scenario-based media should prove to be
enough.

Xu et al. [55] highlighted that different scenario representations triggers
different human behaviors. More specifically, live scenario were superior in
several aspects they have measures but text and videos provided pretty good
results when it was about general attitudes towards robots.

While scholars do not seem to agree on the efficiency of using scenario
representations in HRI, it is still unclear if different robot representations
can replace in-person human-robot interactions. It seems therefore important
to conduct our study using two types of embodiment, on a screen and physically
present, to contribute to the collective understanding of the issue and
understand better how scenario representations influence phantom cost
perceptions. If similar results were found between scenario representations
and real life human-robot interaction, online studies might be conducted by
researchers, in this context of suspicious behaviors and phantom costs, saving
money and time. Criteria people use to make their decisions should also be
collected to understand better how they make their decisions. This information
lead us to the three last research questions of this paper:

  1. RQ4: Are there interaction effects between the Agent, Offer and Embodiment?

  2. RQ5: What are the criteria participants use to make their decision?

  3. RQ6: Is there a difference in terms of phantom costs when the agent is physically or screen embodied?

###  1.6 Objectives

This study is a partial replication of the work of Vonasch et al. [50],
enabling us to verify our method by comparing the results. We extend it by the
perception of phantom costs in the context of human-robot interaction to
explore differences in terms of Agent and their Embodiment. Additional
exploratory research question will aim to know if there are phantom costs
perception differences between genders and what are the criteria people use to
make their decisions.

Exploring the perception of phantom costs in HRI will allow us to know whether
a broader context is taken into account by people when interacting with a
robot to make decisions related to the interaction. If so, phantom costs can
occur when explanations seem insufficient and hence scholars should keep this
in mind when conducting HRI studies.

##  2 Method

We conducted a between-participants design using three factors. An Agent,
either a human or a robot, was making an Offer that was either a cookie or a
cookie associated with $2. The Agent was either physically or non-physically
embodied. While for the physically embodied agents, the study was conducted
in-person, the study for the non-physically embodied agents was conducted
online. To conduct the latter, people saw a picture of the real agents on
their screen. For the sake of clarity, we will refer to these two conditions
as physical and screen embodiment. While it is only one study with three
factors, we chose to describe the method of both embodiment conditions into
two different sections.

###  2.1 Part A: Screen Embodiment

We conducted a between-participants design where a screen embodied agent,
either a human or a robot, was making an offer that was either a cookie or a
cookie associated with $2. The pre-registration for this study is available at
https://aspredicted.org/XRZ_ZH7. Initially, a first version of the screen
embodiment study has been conducted only using the screener “fluent in
English” but it seems to not have be sufficiently controlled because of the
large demographic variety (people from 27 different countries participated in
the study). Some participant seem to not have correctly understood the
scenario or even answered in Spanish. Also, the point of this study was not to
create specific groups to explore potential cultural differences in the
perception of phantom costs. While we suspect it may exist, we have chosen to
rerun the study using more specific screeners via Prolific. For the sake of
clarity and transparency, please find analyses included the first version in
Appendix A. Note that the conclusions drawn are alike.

####  2.1.1 Participants

481 participants were recruited via the Prolific
platform111https://www.prolific.com to take part in this online study.
Inclusion criteria were to be located in the USA, have the American
nationality, and fluent in English.

####  2.1.2 Measurements

Two independent variables were used for this study: Agent and Offer. The Agent
was either a Human (coded as 0) or a Robot (coded as 1). The Offer was either
a Cookie (coded as 0) or a Cookie + $2 (coded as 1). Participants were asked
to say if they would accept or refuse the offer using a forced binary choice
(coded as 0 if they refused or 1 if they accepted). On a new page, they had to
rate statements on 7-point Likert scales. They were then asked to explain
their choice in a text-entry box and provide some demographic information
(i.e., gender, age, Hispanic origins, and race).

####  2.1.3 Material and Place of Study

##### Human Stimuli.

The two pictures (see Figures 1(a) and 1(b)) show a casually dressed man in
his twenties standing. In his right hand, he holds a clear container with
several cookies. His hand is slightly outstretched to show commitment and make
the offer clear. The man had a $2 coin in his left hand for the “Cookie + $2”
context, with the arm also positioned forward. He smiled lightly as he would
have done if he was in a direct interaction. We expect this to be a better
choice than a neutral face as it might have negatively influenced our results
as smiling while approaching someone might respect the social norms for an
interaction. The man’s height is not specified, but can be approximately
estimated regarding his age. The human is the same size and position in both
photos.

##### Robot Stimuli.

We took two pictures which resemble as closely as possible the interaction
that will take place in physically embodied agent conditions (see 2.2). The
photo shows the Nao robot (V6) on a table (see Figures 1(c) and 1(d)). In its
right hand, it holds the same clear container with several cookies as used for
the human photos. Its hand is slightly outstretched to show commitment and
make the offer clear. For the “Cookie + $2” condition, the robot had on the
back of its hand a $2 coin with the arm also positioned forward. The robot is
the same size and position in both photos.

(a) Human - Cookie

(b) Human - Cookie + $2

(c) Robot - Cookie

(d) Robot - Cookie + $2

Figure 1: Pictures Used for Each Condition of the Screen Embodiment Part.

##### Recruitment and Conduction Platforms.

Participants were recruited online via the Prolific platform. The experiment
was conducted using the web-based questionnaire service
Qualtrics222https://www.qualtrics.com.

####  2.1.4 Procedure

When Prolific customers chose to participate in the study, they were
redirected to the web-based questionnaire service Qualtrics. After their
Prolific ID was recorded, a reCAPTCHA was displayed to exclude potential bots
as their open-ended responses might fool human and AI detectors [18]. They
were then presented with an information letter outlining the risks involved,
and a statement of their rights as participants (i.e., to withdraw from the
study without having to justify themselves). They then had to consent to
participate in the study if they wished. Otherwise, they were thanked and
asked to leave the questionnaire. After giving their consent, the study was
divided into four parts. The overall study lasted approximately 3 minutes.

##### Vignette

First, each participant had to read one of the four vignette where they had to
imagine they were in the described situation and say what they would do. The
vignettes were representing the four possible scenarios (combination of the
two factors Agent and Offer) and was randomly and evenly distributed among all
participants. The vignette was presented as follows:

> During a break, you go for a walk. A [person / robot] carrying a clear
> container with several cookies starts talking to you:
>
> “Hi, excuse me, I’ve just been having food with my friends and we had some
> cookies left over. Would you like one? [I’ll pay you 2 dollars if you eat
> it.]”
>
> What do you do?

To answer the question, participants clicked on the option they thought they
would have done if they were actually in the situation. A photo representing
the scenario (i.e., the person or the Nao robot with a cookie container) was
shown below the text-written scenario (see Figure 2).

Figure 2: Example of One of the Vignettes Participants Could Meet in the
Screen Embodiment Part.

##### Justification

In order to better understand their choice, participants were then asked to
justify their decision by writing in a text-entry box. The response
participants wrote also served as an attention check for the experimenter.
Participants who failed to answer this question were excluded from the data
analysis.

##### Statements Ratings

Participants rated statements on 7-point Likert scales from 1 (“Strongly
disagree”) to 7 (“Strongly agree”). These were used as check Statements were
(i) “I would eat the cookie.” (to have more sensitive data compared to the
binary question previously asked, and compare these result to the other
statements), (ii) “The person had good intentions for offering me the cookie.”
(to check whether people attribute positive intentions to agents separated
from bad intentions that are in the following statement), (iii) “The person
had hidden reasons for offering me the cookie.” (to check whether people
imagine phantom costs), and (iv) “The person gave a good explanation for why
he offered the cookie.” (to check that the HOSE model is applicable).

##### Demographics

After having rated each statement, they were requested to provide some
demographic information (i.e., gender, age, Hispanic origins, and race).

##### Debriefing

Finally, a debriefing of the study was displayed. Participants were then
automatically redirected to Prolific to confirm the completion of the study
and receive their compensation on a basis of £6 per hour (i.e., £0.3 for 3
minutes).

###  2.2 Part B: Physical Embodiment

We conducted a between-participants design where a physically embodied agent,
either a human or a robot, was making an offer that was either a cookie or a
cookie associated with $2. This part was kept as consistent as possible with
the screen embodied agents part. The pre-registration for this study is
available at https://aspredicted.org/98X_VPG.

####  2.2.1 Sample Size Estimation

The first version of the screen embodiment study was conducted to estimate the
minimum required sample size for the study involving physically embodied
agents. To do so, data sets were simulated with different sizes and the number
of times the effect was found, at $\alpha=.05$, using the R software. The
simulated data had sample sizes ranging from 20 to 80 with a step of 5. One
thousand simulations were performed per sample size leading to 13,000
simulations in total. Based on these simulations, to have a power of 0.8,
about n = 48 participants per condition are required to detect the same
effects. We have conducted similar analyses on the current version of the
screen embodied study described above to seek the minimum required sample
size. Similar analyses were conducted and suggest that n = 22 participants per
condition may have been sufficient. We seek for a minimum of 48 participants
per condition involving physically embodied agents to be confident in the
power of our study.

####  2.2.2 Participants

251 persons at the University of Canterbury took part in the study with no
prior knowledge that they would be taking part in a study. Sufficient power is
expected for this study since the sample size is in line with the power
calculation of Experiment 1 (see 2.2.1).

####  2.2.3 Measurements

Participants were asked to accept or not the offer (coded as 0 if they refused
and 1 if they accepted). Then participants were asked to justify their choice
and provide demographic information (i.e., age, gender, and field of study).

####  2.2.4 Material and Place of Study

The study took place at the University of Canterbury. Most of the participants
were students. As the photos of the screen embodied agents were shown indoors
(see Figure 1), this part of the study took also place indoors in areas of the
university frequented by all types of people. The agents were not only the
same used in the screen embodied agents part (see Figure 1) but also in the
same position. However, here, they were physically embodied.

##### Human stimuli

The human was a casually dressed man in his twenties. He was of average
height. He was holding in his right hand a clear container with several
cookies in it. For the “Cookie + $2” condition, he was also holding $2 coin in
this left hand. When making his offers, he was also moving his arms forwards
for people to pay attention to the offers and to show commitment. He also
smiled slightly (as in 1(a) and 1(b))

##### Robot stimuli

The robot used for this study was Nao333http://www.aldebaran.com/fr/nao V6.
This robot was created by Aldebaran Robotics. The robot is 58 centimetres tall
with a weight of 5.4 kilograms. The Nao used for this study was blue (see
Figure 1). Two robots (V5 or V6) were available as backup robots in case the
main one had a problem during the experiment. Nao has 25 degrees of freedom,
enabling it to perform complex movements. Several tactile sensory sensors in
the hands, feet, and head, as well as microphones, speakers, cameras, and
voice recognition, enable the robot to interact with people and its
environment. This robot is frequently used in HRI papers because it is fully
programmable using the
Choregraphe444http://doc.aldebaran.com/1-14/software/choregraphe/choregraphe_overview.html
platform. For the study, Nao was on a table for it to be at the same height
level of people. It had a fixed tray in his right hand with the clear
contained of cookies on it (see 1(c)). If the “Cookie + $2” was conducted, it
also had a $2 coin on its left hand fixed using BlueTack (see 1(d)). Its left
hand has a touch sensor. When the participants took the coin, the robot
registered the event and thanked the participant. This was done to ensure that
the experimenter will not miss one participant taking the coin as the robot’s
hand was not visible through the robot’s camera and thus not visible to the
experimenter.

##### Wizard-of-Oz Guidelines.

Given the short time available for the recruitment but also that the resources
necessary to have a robot behaving autonomously, the researchers decided to
conduct the study using the Wizard-of-Oz method. According to WoZ guidelines
proposed by Riek [39], some points need to be discussed concerning the wizard.
The sole Wizard was the main researcher and therefore knew about the
experiment’s hypotheses. He followed a protocol to not be biased. Previous
training was made in a pilot study for the wizard to be familiar with the
settings. The wizard was requested to run the behavior boxes as fast as if
they had performed these behaviors in real life. The Wizard script (see
Appendix B) was very specific and consistent for all participants. In the case
of the Wizard making errors, the participant’s data was not recorded or a
comment was reported in the data collected from the participants. However, it
was expected that if some errors were going to be made, the wizard was
supposed to keep interacting with the potential participants to see how they
act even if they were not going to be included in the data (for not having
respected the protocol).

As the robot’s battery can not be left on for long without recharging, Nao was
constantly being charged via its charger. The cabling is not expected
influence the perceived autonomy of the robot [40] Both the computer and the
robot were connected to the same WiFi using a router. While the computer was
physically connected to the router using an Ethernet cable, Nao was connected
in wireless. This configuration was chosen to make the robot’s non-autonomy
less obvious and allow the Wizard operating several meters from the robot. In
this way, the experimenter controlled the robot in real-time via Choregraphe.
Also, to not create a habituation effect or people guessing a study was
running since people would have seen the robot continuously at the same place
in the same building, the robot changed its location regularly. To make the
interaction more natural and that people perceive the robot answered and
behaved adequately with participants, the experimenter had access in real-time
to the robot’s cameras on the Choregraphe platform. The Wizard did not need to
look at people in real-life but used the robot’s cameras for it. When an
action was required (e.g., the robot had to extend its arm and talk), the
experimenter just had to double-click on the corresponding box “start” button.

####  2.2.5 Procedure

The study design followed the one of Vonasch et al. [50] as much as possible,
except that whereas in the original study the agent (a person) approached
participants, this was not done in the present study because the robot could
not walk. Instead, the robot called out to potential participants as they
walked by. To keep the procedure as similar as possible between conditions,
the human in this study did the same. There was one other difference between
this study and the original. In [50], only individuals were contacted. In the
current study, groups of people were also contacted because because alone
people were usually distracted by their phones or listening to music.
Participants had no prior knowledge that they would be taking part in a study.
Thus, their informed consent was not obtained prior to their participation.
The experiment lasted approximately 2 minutes.

##### Experimenter Position

The experimenter was positioned several meters from the robot, but close
enough to hear any conversations people might have with the robot. He sat at a
table with his back to the participants who would be interacting with the
robot. He used the computer with Choregraphe. So, not only did he look like a
student studying his lessons, but he also had a good view of the situation,
without the robot being in the way.

##### Scenario (Before Consent)

Participants were called out by the human or the robot positioned on a table.
The conditions shown in Figures 1 represent the situation encountered by the
participants. The robot is positioned on the table, holding a container
containing several cookies. We chose to position the robot on a table so that
it would be at approximately head height with the participants. The robot had
to be approached by the participants as it was unable to walk correctly around
the campus. To maintain consistency between conditions, the human also called
out to the participants without moving towards them as well. In both
conditions, human and robot, the agent will say the following sentence:

> “Hi, excuse me, I’ve just been having food with my friends and we had some
> cookies left over. Would you like one? [I’ll pay you 2 dollars if you eat
> it.]”

In the “Cookie” condition, participants only had to take the cookie or not. In
the “Cookie + $2” condition, participants had to accept both offers, the
cookie and a $2 coin. In the robot cookie + $2 condition, when the participant
took the coin, the robot automatically thanked him. Except this behavior,
every robot behavior were triggered using the Wizard of Oz protocol.

It’s important to note that the “robot” condition was setup in accordance with
the Wizard of Oz protocol. The robot displayed full autonomy while the
experimenter controlled the robot remotely. The experimenter kept an eye on
the situation. In this way, the experimenter had access to the live stream on
his computer, thanks to Nao’s cameras positioned at the forehead and mouth.
The wizard could hear the conversations participants had with the robot. To do
so, the Wizard was hidden from people’s view when they were interacting with
the robot but close enough to hear what they were saying to the robot. The
video was not recorded, this simply enabled the experimenter to control the
robot’s behavior in the best possible way, to achieve a more natural
interaction, and to know what the robot should do in real time.

##### Justification and Demographics (After Consent)

After participants accepted or rejected the offer, the experimenter (in the
human conditions) or students who volunteered to help (in the robot
conditions) explained them this was a study in psychology. They received a
debriefing of the study and had the option of withdrawing their participation
without affecting the compensation received. When participants agreed to keep
their participation available for the study, the experimenter asked them to
explain their choice and to provide some demographic information (i.e.,
gender, age, field of study).

##  3 Results

###  3.1 Descriptive Analyses

####  3.1.1 Part A: Screen Embodiment

Among the 481 participants, 15 participants have been removed from the data
analyses because did not meet the attention check justification (n = 1) or had
dietary restrictions (n = 14) leading to a final sample size of 466
participants. Their age ranged from 18 to 78 $(M=38.31,SD=13.01,Median=36)$.
The gender distributions is as follows: 232 were males, 228 were females, and
6 said having a different gender. Genders were almost equally distributed
between conditions (see Table 1). All participants were American. 9% (n = 42)
declared having a Spanish, Latin, or Hispanic origin. Race is distributed as
follows: 73.39% of participants were White or Caucasian, 11.80% were Black or
African American, 1.72% were American Indian/Native American or Alaska Native,
14.81% were Asian, 0.43% were Native Hawaiian or Other Pacific Islander, 2.36%
were had another race and 0.86% of the participants preferred to not answer
the question.

Table 1: Gender Counts per Screen Embodied Agent and Offer

| Human | Robot
---|---|---
| Cookie | CD | Cookie | CD
Male | 53 | 62 | 57 | 60
Female | 61 | 56 | 54 | 57
Other | 3 | 0 | 2 | 1

44footnotetext: Note: CD: Cookie + $2.

####  3.1.2 Part B: Physical Embodiment

27 participants were removed from the data analyses according to the following
criteria: 11 participants did not accept the cookie due to dietary
restrictions, 9 guessed it was a study, 5 did not follow the protocol or
misunderstood the interaction or the offer, one person ran away when they saw
the experimenter approaching them and therefore did not consent, and one
participant was a university student but still minor. The final sample was
composed of 224 participants.

The participants’ age in the final sample ranged from 18 to 61
$(M=22.5,SD=6.09,Median=20)$. While 2.68% (n = 6) were University Staff,
97.32% (n = 218) were students. The gender distribution is shown in Table 2.
Their field of study was distributed as follows: 62 in Engineering, 59 in
Science, 35 in Commerce and Related, 17 in Law, 16 in Teaching and Language, 4
in Technology, and 25 had another field of study.

Table 2: Gender Counts per Physically Embodied Agent and Offer

| Human | Robot
---|---|---
| Cookie | CD | Cookie | CD
Male | 38 | 32 | 26 | 30
Female | 16 | 22 | 29 | 30
Other | 0 | 0 | 1 | 0

44footnotetext: Note: CD: Cookie + $2.

###  3.2 Predictors of Offer Acceptance

Three-way interactions logistic regression analyses were conducted using the R
software to examine the relationship between the Agent, their Embodiment, and
the Offer the Agents made, on the probability of Rejecting (0) or Accepting
(1) the Offer. The Agent was either a Human (0) or a Robot (1). They were
either screen (0) or physically (1) embodied. The Offer was either a Cookie
(0) or a Cookie + $2 (1). When agents were physically embodied , 12
participants took the cookie but not the coin in the “Cookie + $2” offer. They
were not removed from analyses and were counted as 1 (i.e., “Yes, accepted the
offer”) since they chose to eat the cookie, which was the purpose of the
analyses and research questions. Note that excluding these participants from
data analyses provided the same results overall. For clarity, “accepting or
rejecting the offer” will still be used to explain the results. The model
equation is shown in in subsection 3.2). The output of the model is shown in
in Table 3 and the plot of the data per condition in Figure 3. The logistic
regression model shows significant results for each factor. 95% confidence
intervals (95%CI) are shown in the figure. Any overlap of these CI between any
conditions indicated the absence of significant differences between these
conditions.

$\displaystyle\text{logit}(\mathbb{P}(\text{Choice}=1))=$
$\displaystyle\beta_{0}+\beta_{1}\cdot\text{Agent}+\beta_{2}\cdot\text{Offer}+\beta_{3}\cdot\text{Embodiment}$
$\displaystyle\quad+\beta_{4}\cdot(\text{Agent}\cdot\text{Offer})+\beta_{5}\cdot(\text{Agent}\cdot\text{Embodiment})+\beta_{6}\cdot(\text{Offer}\cdot\text{Embodiment})$
$\displaystyle\quad+\beta_{7}\cdot(\text{Agent}\cdot\text{Offer}\cdot\text{Embodiment})$
Table 3: Coefficients of the Three-Way Interactions Logistic Regression Model.

Effect | Estimate | Standard Error | z value | $Pr(>|z|)$
---|---|---|---|---
(Intercept) | -1.0647 | 0.2117 | -5.029 | $<.001^{*}$
Agent | 1.1888 | 0.2835 | 4.193 | $<.001^{*}$
Offer | -0.9406 | 0.3548 | -2.651 | $<.001^{*}$
Embodiment | 1.6756 | 0.3550 | 4.720 | $<.001^{*}$
Agent $\times$ Offer | 0.4040 | 0.4436 | 0.911 | .362
Agent $\times$ Embodiment | -1.3644 | 0.4862 | -2.806 | .005∗
Offer $\times$ Embodiment | -0.8189 | 0.5553 | -1.475 | .140
Agent $\times$ Offer $\times$ Embodiment | 0.0729 | 0.7304 | 0.100 | .921

Note: *: $p<.05$

Note: CD corresponds to the Cookie + $2 condition. Errors bars are 95%
confidence intervals (95%CI).

Figure 3: Plot of the Probability of Accepting the Offer as a Function of the
Agent, the Offer, and the Agent Embodiment.

Main effects are directly obtained from Table 3. The two-way interaction Agent
$\times$ Embodiment was also significant ($p=.005$). To understand all the
effects better, post-hoc analyses were conducted using the emmeans
package555https://cran.r-project.org/web/packages/emmeans/index.html. Tests
were performed on the log odds ratio scale.

####  3.2.1 Offer: Cookie and Cookie + $2

The odds of accepting the offer were 2.56 times higher ($CI95\%[1.30,5.27]$)
when the offer was a cookie compared to when it was a cookie associated with
$2. For each Agent $\times$ Embodiment pairwise comparisons, the odds of
accepting the Offer were higher when the offer was a cookie compared to a
cookie associated with $2. The odds ratio (OR = Cookie/(Cookie + $2)) of each
pairwise comparison were the followings. When the agent was a human screen
embodied, the OR of the offer was 2.56 ($p=.008$) whereas the OR of the offer
for a physically embodied human was 5.81 ($p<.001$). When the agent was a
screen embodied agent, the OR was 1.71 ($p=.044$) whereas the OR for the
physically embodied robot was 3.61 ($p=.001$).

####  3.2.2 Agent: Human and Robot

The odds of accepting the offer were 3.28 times higher ($CI95\%[1.89,5.58]$)
when the Robot was making the offer compared to the Human making an offer.
While no significant difference has been found between the Human and the Robot
when they were physically embodied, neither for the Cookie condition
($p=.657$) nor the Cookie + $2 condition ($p=.478$), the odds of accepting the
offer were higher for the screen embodied robot compared to the screen
embodied human when the offer was a cookie (OR = 3.27, $p<.001$) but also a
cookie + $2 (OR = 4.93, $p<.001$).

####  3.2.3 Embodiment: Screen and Physical

The odds of accepting the offer were 5.34 times higher ($CI95\%[2.70,10.90]$)
when agents were physically embodied compared to screen embodied. Only the
embodiment of the human agent influenced the odds of accepting the offer. The
odds of accepting the cookie were 5.35 times higher when the human was
physically embodied compared to screen embodied ($p<.001$). The odds of
accepting the cookie associated with a monetary outcome were 2.35 times higher
for the physically embodied human compared to the screen embodied one. These
differences have not been found when the agent was a human ($p=.349$ for the
cookie condition and $p=.199$ for the cookie + $2 condition).

####  3.2.4 Gender Analyses

Exploratory analyses have been conducted to explore potential gender
differences for both embodiment levels on the Choice dependent variable. When
agents were screen embodied, an ANOVA was conducted
($M_{male}=0.39,SD_{male}=0.03,M_{female}=0.25,SD_{female}=0.03,M_{other}=0.67,SD_{other}=0.19$)
and showed a Gender effect, $F(2,463)=6.77,p=.001,\eta^{2}=0.028$. Post-hoc
analyses showed that males were more likely to accept the offer than females,
$t(463)=3.20,p=.004,d=0.30,95\%CI[0.11,0.48]$.

When agents were physically embodied, analyses were conducted using a chi-
square test to explore potential Choice differences between males
($M=0.50,SD=0.50$) and females ($M=0.37,SD=0.49$). The third gender
(identified as “Other”) was removed from data analyses as only one participant
declared having a non-binary gender. Data analyses did not show any
significant difference between these two groups, ($\chi^{2}(1)=3.69,p=.055$)
although notably if there was a gender difference, it was in the same
direction as in the screen embodied condition.

###  3.3 Decision-Making Criteria

Two coders individually categorized the participants’ justifications to
understand the criteria participants were using to make their decisions. The
inter-reliability between both coders was satisfactory ($\kappa=0.78$ and
$\kappa=0.71$ respectively for the screen and physically embodied agents
studies). Disagreement between both coders was resolved by discussing until a
complete agreement ($\kappa=1.00$) was achieved. The criteria and their sum
are shown in Table 4.

Participants mainly justified their choice using 10 criteria gathered into
seven categories. These categories and their definitions (used for the coders
to categorize the data) were: (i) Phantom Costs (i.e., suspicion, something is
weird, the cookie might be poisoned etc), (ii) Excitement (i.e., people being
happy because of the cookie or the dollars directly such as getting free
food), (iii) Hunger (i.e., being or not hungry), (iv) Agent (i.e., everything
related to the Agents such as trusting them, considering them as cute or
dodgy, a kind gesture etc), (v) Sanitation (i.e., not clean, cookies not
sealed etc), (vi) Food from Strangers (i.e., not accepting food from stranger
as a rule of thumb), and (vii) Etiquette (i.e., altruism, participants who
felt bad accepting the money, accepting the offer to be polite, etc). Some
other information were provided by the participants and are categorized as
“Other.”

Table 4: Sum of the Decision Criteria Specified by the Participants to Make
Their Decisions According to the Agent, the Embodiment, and the Offer

| Screen | Physical
---|---|---
| Human | Robot | Human | Robot
Criteria | Cookie | CD | Cookie | CD | Cookie | CD | Cookie | CD
Phantom Costs | 35 | 82 | 22 | 51 | 4 | 22 | 7 | 28
Excitement | 19 | 13 | 42 | 41 | 19 | 7 | 13 | 11
Hungry | 0 | 1 | 3 | 0 | 6 | 3 | 5 | 2
Not Hungry | 0 | 0 | 0 | 1 | 8 | 6 | 6 | 5
Good Agent | 16 | 0 | 17 | 5 | 18 | 0 | 19 | 6
Bad Agent | 5 | 4 | 7 | 17 | 2 | 7 | 4 | 8
Sanitation | 12 | 5 | 13 | 14 | 2 | 0 | 4 | 8
Food from Strangers | 69 | 57 | 24 | 14 | 2 | 3 | 1 | 0
Etiquette | 11 | 0 | 4 | 0 | 1 | 5 | 2 | 4
Other | 24 | 5 | 23 | 16 | 6 | 16 | 9 | 13

Note: CD corresponds to the Cookie + $2 condition. Values are the number of
participants who reported such criteria in their choice justification.

Analyses of Variance (ANOVA) have been conducted to explore the effect of the
Agent, the Offer, and the Embodiment on the perception of each of the ten
criteria described in Table 4. Plots are shown in Figure 4. Results are
discussed below. Student t-tests with Tukey correction have been conducted to
understand better the direction of the effects and the interaction effects.

  * $\bullet$

Phantom Costs. There is a main effect of the Offer
($F(1,682)=82.24,p<.001,\eta^{2}=0.103$) and the Embodiment
($F(1,682)=14.42,p<.001,\eta^{2}=0.018$), as well as an interaction effect
Agent $\times$ Embodiment ($F(1.682)=10.95,p<.001,\eta^{2}=0.014$) on the
report of phantom costs by participants. More specifically, participants
reported more often phantom costs when (i) the cookie was associated with a
monetary outcome ($mean=0.52,SD=0.50$) compared to a cookie alone
($mean=0.20,SD=0.40$), $t(682)=-9.07,p<.001,d=0.74$, but also when (ii) agents
were screen embodied ($mean=0.41,SD=0.49$) compared to physically embodied
($mean=0.27,SD=0.45$), $t(682)=3.80,p<.001,d=0.31$. Post-hoc analyses have
also been conducted to understand better the Agent $\times$ Embodiment
interaction effect. Participants reported more phantom costs when the human
was screen embodied compared to physically embodied
($t(682)=4.97,p<.001,d=0.58$) but also when the screen embodied human was
making the offer compared to a screen embodied robot
($t(682)=4.47,p<.001,d=0.41$) or a physically embodied robot
($t(682)=4.00,p<.001,d=0.45$).

  * $\bullet$

Excitement. The results showed main effects for the Agent
($F(1,682)=7.90,p=0.005,\eta=0.011$) and Offer
($F(1,682)=6.59,p=0.010,\eta=0.009$) factors as well as a Agent $\times$
Embodiment interaction effect ($F(1,682)=14.34,p<.001,\eta^{2}=0.020$) on the
Excitement reported by the participants about the offer. More specifically,
people reported more excitement about the offer when (i) it was offered by a
robot ($mean=0.31,SD=0.46$) compared to a human ($mean=0.17,SD=0.38$),
$t(682)=-2.81,p=.005,d=0.23$, but also when (ii) the offer was a cookie alone
($mean=0.27,SD=0.45$) compared to a cookie + $2 ($mean=0.21,SD=0.41$),
$t(682)=2.57,p=.010,d=0.21$. Post-hoc analyses have also been conducted to
understand better the Agent $\times$ Embodiment interaction effect.
Participants reported more often being excited about the offer when the screen
embodied robot was making the offer ($mean=0.36,SD=0.03$) compared to when it
was a screen embodied human ($mean=0.14,SD=0.03$),
$t(682)=-5.79,p<.001,d=0.54$. People also reported more excitement about the
offer when the robot was screen embodied ($mean=0.36,SD=0.03$) compared to
physically embodied ($mean=0.21,SD=0.04$), $t(682)=3.21,p=.008,d=0.37$.

  * $\bullet$

Hunger. Concerning the fact of being hungry, the results showed a main effect
of the Offer ($F(1,682)=5.82,p=.016,\eta^{2}=0.008$) and the Embodiment
($F(1,682)=22.42,p<.001,\eta^{2}=0.031$) on the perception of hunger by
participants. More specifically, people reported being hungry more often when
the offer was a cookie alone ($mean=0.04,SD=0.2$) compared to when it was
associated with money ($mean=0.02,SD=0.13$), $t(682)=2.41,p=.016,d=0.20$.
People also reported more often being hungry when agents were physically
embodied ($mean=0.07,SD=0.26$) compared to screen embodied
($mean=0.009,SD=0.09$), $t(682)=-4.74,p<.001,d=0.39$. Concerning the fact of
not being hungry, the results showed a main effect of the Embodiment
($F(1,682)=54.33,p<.001,\eta^{2}=0.073$). More specifically, participants
reported more often not being hungry when agents were physically embodied
($mean=0.11,SD=0.32$) compared to screen embodied ($mean=0.002,SD=0.05$),
$t(682)=-7.37,p<.001,d=0.6$.

  * $\bullet$

Character. Results showed a significant effect of the Offer
($F(1,682)=68.31,p<.001,\eta^{2}=0.087$) and the Embodiment
($F(1,682)=20.07,p<.001,\eta^{2}=0.026$) as well as their interaction
($F(1,682)=10.98,p<.001,\eta^{2}=0.014$) on positive perceptions of the agent.
More specifically, people reported more positive information concerning the
agent when the offer was a cookie alone ($mean=0.21,SD=0.41$) compared to a
cookie associated with $2 ($mean=0.03,SD=0.18$), $t(682)=8.26,p<.001,d=0.67$.
More positive information about the agent were reported by participants when
the agent was physically embodied ($mean=0.19,SD=0.40$) compared to screen
embodied ($mean=0.08,SD=0.27$), $t(682)=-4.48,p<.001,d=0.36$. Post-hoc
analyses have also been conducted to understand better the Offer $\times$
Embodiment interaction effect. All effects were significant except the one
involving the embodiment effect with the Cookie + $2 offer ($p=.840$). More
specifically, participants reported more often positive information about the
physically embodied agent offering a cookie ($mean=0.34,SD=0.03$) compared to
screen embodied agents making the same offer ($mean=0.14,SD=0.02$),
$t(682)=-5.47,p<.001,0.63$. Screen embodied agents offering a cookie alone
were also received more positive information from participants
($mean=0.14,SD=0.02$) compared to when they offered a cookie associated with
$2 ($mean=0.02,SD=0.02$), $t(682)=4.34,p<.001,d=0.40$. Participants also
reported more often positive perceptions of the screen embodied agents
offering a cookie alone ($mean=0.14,SD=0.02$) compared to the physically
embodied agents offering a cookie and $2 ($mean=0.05,SD=0.03$),
$t(682)=2.69,p=.037,d=0.31$. The opposite conditions are also different:
participants reported more often positive perceptions of the physically
embodied agents offering a cookie alone ($mean=0.34,SD=0.03$) compared to the
screen embodied agents offering a cookie and $2 ($mean=0.02,SD=0.02$),
$t(682)=8.98,p<.001,d=1.04$. Results showed an effect of the Offer factors on
the negative perceptions about the agent that participants reported,
$F(1,682)=6.90,p=.009,\eta^{2}=0.010$. The Agent factor is barely not
significant, $F(1,682)=3.73,p=.054$. More specifically, participants reported
more often negative information about the agent when the offer was a cookie +
$2 ($mean=0.10,SD=0.30$) compared to a simple cookie ($mean=0.05,SD=0.22$),
$t(682)=-2.63,p=.009,d=0.21$. While the Agent effect is not significant
following the rule of thumb of $\alpha=.05$, we consider important to report
the results. Participants reported more often negative information about the
robot ($mean=0.10,SD=0.30$) compared to the human ($mean=0.05,SD=0.22$),
$t(682)=-1.93,p=.054,d=0.16$.

  * $\bullet$

Sanitation. Results showed a main effect of the Agent on the perception of
sanitary issues, $F(1,682)=8.15,p=.004,\eta^{2}=0.012$. More specifically,
participants reported more sanitary issues when dealing with the robot
($mean=0.11,SD=0.32$) compared to the human ($mean=0.06,SD=0.23$),
$t(682)=-2.85,p=.004,d=0.23$.

  * $\bullet$

Food from Strangers. Results showed a main effect of the Agent
($F(1,682)=43.31,p<.001,\eta^{2}=0.052$), the Embodiment
($F(1,682)=113.69,p<.001,\eta^{2}=0.130$), and their interaction
($F(1,682)=30.24,p<.001,\eta^{2}=0.034$). More specifically, people reported
more often refusing food from strangers when the Agent was a Human
($mean=0.38,SD=0.49$) compared to a Robot ($mean=0.11,SD=0.32$),
$t(682)=6.73,p<.001,d=0.55$. People also reported more often refusing food
from strangers when the agents were screen embodied ($mean=0.35,SD=0.48$)
compared to when agents were physically embodied ($mean=0.03,SD=0.16$),
$t(682)=10.7,p<.001,d=0.87$. Post-hoc analyses have also been conducted to
understand better the Agent $\times$ Embodiment interaction effect. All
effects were significant except the difference between the physically embodied
human and robot, $t(682)=0.75,p=.877$. People declared more often refusing
food from strangers when the human making the offer was screen embodied
($mean=0.54,SD=0.02$) compared to physically embodied ($mean=0.05,SD=0.04$),
$t(682)=11.31,p<.001,d=1.32$. Refusing food from strangers was also specified
more often for screen embodied human ($mean=0.54,SD=0.02$) compared to a
screen embodied robot ($mean=0.17,SD=0.02$), $t(682)=10.74,p<.001,d=1.00$ and
also compared to a physically embodied robot ($mean=0.01,SD=0.03$),
$t(682)=12.47,p<.001,d=1.42$. People also declared more often refusing food
from strangers when a screen embodied robot was making the offer
($mean=0.17,SD=0.02$) compared to a physically embodied human
($mean=0.05,SD=0.04$) and a physically embodied robot ($mean=0.01,SD=0.03$),
respectively $t(682)=-2.74,p=.032,d=0.32$ and $t(682)=3.69,p=.001,d=0.42$.

  * $\bullet$

Etiquette. There is only a interaction effect between the Offer and the
Embodiment on the Etiquette behaviors, $F(1,682)=14.16,p<.001,\eta^{2}=0.020$.
Post-hoc analyses have been conducted to understand better this interaction.
When agents were screen embodied, results showed an Offer effect associated
with more Etiquette behaviors in the Cookie condition ($mean=0.05,SD=0.22$)
compared to the Cookie + $2 condition ($mean=0.03,SD=0.16$),
$t(682)=3.65,p=.002$. The Etiquette behaviors were also higher when physically
embodied agents made a Cookie + $2 offer ($mean=0.08,SD=0.02$) compared to the
same offer made by screen embodied agents ($mean=0.00,SD=0.01$),
$t(682)=-3.65,p=.002$.

Figure 4: Graphs Representing Each Criteria as a Function of the Agent, the
Offer, and the Embodiment.

###  3.4 Explanation and Intentionality

Analyses of Variance (ANOVA) have been conducted to see how both factors Agent
and Offer influenced each of the 7-point Likert scales. Plots are shown in
Figure 5. Note these questions have not been asked to participants who met a
physically embodied agent.

(a) I would eat the cookie.

(b) The person/robot had good intentions for offering me the cookie.

(c) The person/robot had hidden reasons for offering me the cookie.

(d) The person/robot gave a good explanation for why he/it offered the cookie.

Figure 5: Graphs Representing the Distribution of the Likert Scales.

When participants have been asked to rate on a 7-point Likert scale the
statement “I would eat the cookie” (5(a)), data analyses showed an Agent and
an Offer effect. In other words, participants were more likely to accept the
offer made by a robot ($mean=3.69,SD=0.15,95\%CI[3.40,3.99)]$) compared to a
human ($mean=2.17,SD=0.148,95\%CI[1.88,2.46)]$),
$F(1,462)=52.75,p<.001,\eta^{2}=0.100$. Participants were also more likely to
accept a cookie alone ($mean=3.25,SD=0.149,95\%CI[2.96,3.54)]$) compared to
when it was associated to a monetary outcome
($mean=2.61,SD=0.148,95\%CI[2.32,2.90)]$),
$F(1,462)=9.30,p=.002,\eta^{2}=0.0.18$.

When participants were asked to rate the statement “The person/robot had good
intentions for offering me the cookie” (5(b)), ANOVA analyses showed a main
effect of the Agent ($F(1,462)=18.9,p<.001,\eta^{2}=0.032$) and the Offer
($F(1,462)=99.9,p<.001,\eta^{2}=0.167$) as well as an interaction effect
between these two factors ($F(1,462)=16.9,p<.001,\eta^{2}=0.028$). Post-hoc
analyses with Tukey corrections showed that the human offering a cookie was
considered as having more good intentions
($mean=5.12,SD=0.132,95\%CI[4.86,5.38)]$) than the human offering the cookie
associated with a monetary outcome ($mean=3.25,SD=0.131,95\%CI[3.00,3.51)]$),
$t(462)=10.02,p<.001,d=1.31,95\%CI[1.04,1.58]$. The same conclusion can be
drawn for the robot offering a cookie
($mean=5.15,SD=0.134,95\%CI[4.89,5.41)]$) compared to the robot offering a
cookie with $2 ($mean=4.37,SD=0.131,95\%CI[4.11,4.63)]$),
$t(462)=4.14,p<.001,d=0.55,95\%CI[0.28,0.81]$. While no significant difference
was reported between the human and the robot when they both offer a cookie
($t(462)=-0.16,p=.999$), the robot was judged as having more good intentions
than the human when they both offered a cookie associated with a monetary
outcome ($t(462)=-6.02,p<.001,d=0.78,95\%CI[0.52,1.05]$).

When participants rated the statement “The person/robot had hidden reasons for
offering me the cookie” (5(c)), ANOVA analyses showed a main effect of the
Agent ($F(1,462)=35.8,p<.001,\eta^{2}=0.060$) and the Offer
($F(1,462)=116.58,p<.001,\eta^{2}=0.188$) as well as an interaction effect
between these two factors ($F(1,462)=5.52,p=.020,\eta^{2}=0.009$). Post-hoc
analyses with Tukey corrections showed that a human offering a cookie
associated with a monetary outcome ($mean=5.08,SD=0.132,95\%CI[4.82,5.34)]$)
was considered as having more hidden reasons than if it was a simple cookie
($mean=3.32,SD=0.133,95\%CI[3.06,3.59)]$),
$t(462)=-9.34,p<.001,d=1.22,95\%CI[0.95,1.49]$. The same is true for the robot
offering a cookie with $2 ($mean=3.97,SD=0.132,95\%CI[3.71,4.23)]$) compared
to a simple cookie ($mean=2.84,SD=0.135,95\%CI[2.57,3.11)]$),
$t(462)=-5.95,p<.001,d=0.78,95\%CI[0.52,1.05]$. While the difference between
both human and robot agents offering a cookie alone is barely not significant
($t(462)=2.55,p=.053,d=0.34,95\%CI[0.08,0.60]$), the human offering a cookie
and $2 was considered as having more hidden reasons to justify such behavior
compared to the robot offering the same offer,
$t(462)=5.93,p<.001,d=0.77,95\%CI[0.51,1.03]$.

When participants rated the statement “The person/robot gave a good
explanation for why he/it offered me the cookie” (5(d)), ANOVA analyses showed
a main effect of the Offer ($F(1,462)=74.73,p<.001,\eta^{2}=0.135$) and an
Agent $\times$ Offer interaction effect
($F(1,462)=14.92,p<.001,\eta^{2}=0.029$). No significant effect of the Agent
has been reported ($F(1,462)=1.75,p=.186$). As a reminder, the explanation
provided by the agents was that they were eating with friends and have left-
overs. Post-hoc analyses with Tukey corrections showed that the explanation
provided by a human offering the cookie alone was considered as a better
explanation ($mean=4.64,SD=0.167,95\%CI[4.31,4.97)]$) compared to when he was
offering the cookie associated with a monetary outcome
($mean=2.53,SD=0.166,95\%CI[2.20,2.85)]$),
$t(462)=8.97,p<.001,d=1.17,95\%CI[0.90,1.44]$. The same is true for the robot
offering a cookie alone ($mean=4.19,SD=0.170,95\%CI[3.86,4.53)]$) compared to
a cookie with $2 ($mean=3.42,SD=0.166,95\%CI[3.09,3.74)]$),
$t(462)=3.28,p=.006,d=0.43,95\%CI[0.17,0.69]$. While no significant difference
was found between both agents when they were both offering a cookie alone
($t(462)=1.87,p=.241$), offering a cookie associated with a monetary outcome
of $2 influenced the quality of the explanation according to the Agent: a
robot offering a cookie and $2 because it had left-overs after eating with
friends was considered as a better explanation compared to the human making
the same offer and giving the same explanation,
$t(462)=-3.78,p=.001,d=0.49,95\%CI[0.24,0.75]$.

Pearson’s correlation analyses have been conducted to explore the relationship
between the “Good intentions”, “Hidden reasons”, and “Good explanations” for
both agents separately.

For the Robot agent, results showed that the better the explanation was, the
better intentions people thought the robot had $(r(229)=.535,p<.001)$ and the
hidden reasons the robot had to make an offer $(r(229)=-.376,p<.001)$. The
robot having good intentions and hidden reasons were negatively and moderately
correlated where the better intentions the robot had, the less hidden reasons
to make an offer $(r(229)=-.654,p<.001)$.

For the Human agent, results showed that the better the explanation was, the
better intentions people thought the robot had $(r(233)=.660,p<.001)$ and the
hidden reasons the robot had to make an offer $(r(233)=-.557,p<.001)$. The
robot having good intentions and hidden reasons were negatively and strongly
correlated where the better intentions the robot had, the less hidden reasons
to make an offer $(r(233)=-.776,p<.001)$.

###  3.5 Antisocial and Social Behaviors

While using an online study with screen embodied agents allowed researchers to
specifically investigate what they were looking for, using physically embodied
agents led people to behave in some ways that were originally intended to be
taken into account.

These behaviors are qualitatively discussed in this section and can be divided
into social and antisocial behavior. The latter (i.e., antisocial behaviors)
fall into two categories: ignore and abuse. The former corresponds to actively
ignoring the agent (i.e., paying attention to the agent/hearing what the agent
is saying but choosing to ignore the agent and/or the interaction), while the
latter is divided into two sub-categories: touching (i.e., touching the agent)
and stealing (i.e., taking the agent’s offer without the agent having
interacted with the person).

####  3.5.1 Social Behaviors

Participants in this section were approached and consented to have their data
(i.e., gender) collected for data analysis and publication. Three persons (one
male and two females) waved at the robot when it greeted them. The male
returned several times each day to greet the robot and stand in front of it to
try to interact with it. However, he had been informed by the experimenter
that the robot was not autonomous. Several participants interacted with the
robot, trying to initiate a conversation with it (e.g., “What is your name?”,
“How are you?”, or “You want me to take a cookie?” after the robot made the
offer). One female even approached the robot to ask directly if she could have
a cookie. Another female specified having accepted the cookie from the robot
because it was polite.

####  3.5.2 Antisocial Behaviors

Note that participants discussed in this section were not approached by the
experimenter after having exhibited antisocial behaviors, except when they
have ignored the human agent.

##### Ignoring Behaviors

People actively paying attention to the agents but ignoring them (e.g.,
looking at them, listening and keep walking) were reported as ignoring
behaviors. These numbers only included those who looked at the agent after
he/it talked and waved to people by looking at them, and/or slow done looking
at the robot when it talked and finally continued walking then. Participants
with headphones or who did not look at the agent at all when he/it tried to
interact with people were not counted as we could not ensure they hard the
agent talking. When this behavior happened, the student volunteers were asked
to report this on their sheet. Many people ignored the agents when they
called. However, people were more likely to ignore the robot (n = 388) than
the human (n = 17, including 12 males and 5 females). For this reason, it was
challenging for the student volunteers to manage to go towards people in the
robot conditions and ask for their gender. For the human conditions, as this
was less frequent, the experimenter could ask them directly this information.
Chi-square analyses with continuity correction indicated a significant
difference between the human and the robot, $\chi^{2}(1)=173,p<.001$. The
effect size was large, as indicated by Cramer’s V = 0.518. It is interesting
to note that while some chose to simply ignore the robot, others (two males
and one female) chose to run away from the robot when it spoke or waved to
them. To do this, one person turned around and took a different route, while
most of the people fleeing the robot created a very high safety zone,
sometimes bypassing the robot by more than ten meters.

##### Abusive Behaviors

No abusive behavior was recorded for the two conditions involving the human
agent, whereas the conditions involving the robot led to two types of abuse:
stealing and touching the robot. No information regarding these people were
collected except their behaviors and presumed gender. Information on cookie
theft was collected in order to keep a record of the number of cookies still
available for the study and to assure that a sufficient number of cookies was
still in the container. Information relating to physical abuse of the robot
was collected so that the experimenters could keep track of the risks, and to
the extent of prevention, that one of these people might have caused the robot
to malfunction.

Twenty-one people (18 males and 3 females presumed) stole cookies from the
robot, while no one stole money. Nine people (6 males and 3 females presumed)
uninvited touched the robot. Among these nine persons, eight (6 males and 2
females presumed) went so far as to click the robot’s power button repeatedly.
When people touched or stole from the robot, Nao said “Do not touch me” and
“Do not take a cookie without my permission” respectively. While two people
quickly walked away after hearing this message, most of the others felt amused
and touched the robot more to the point that experimenters had to go to tell
them to stop touching the robot. Moreover, data visually showed that males
were more keen to abuse the robot compared to females, either by touching or
stealing from the robot.

##### Flight and Personal Space

Some people, after the robot attempted to interact with them, avoided the
robot by walking at sometimes more than 10 meters from it or make a U-turn.

##  4 Discussion

###  4.1 Phantom Costs

Phantom costs are based on the idea that people do not aim to only maximize
potential gains [50] but interact with other humans considering them as social
agents. Results show that phantom costs do occur when a human is interacting
with a human (RQ1) and a robot (RQ2). This means that when people are offered
something (here, a cookie) with or without a monetary outcome of $2, people
tend to less often accept the offer when it comes with extra money. This shows
that people naturally consider humans as social agents but do also with social
robots (see [29, 38].

The current research shows that people imagine phantom costs when they
consider explanations provided as insufficient. Therefore, money causes
suspicion and people start imagining hidden reasons to explain this extra
generosity, as reported by participants when they were asked to justify their
decisions. The perception of phantom costs leading to a money backfire effect
(i.e., people refuse an offer more often when associated with extra money) is
consistent with previous research conducted in human-human interaction [50].

Perception of phantom costs in overly generous offers without explicit
explanations for why the agent is doing this is consistent with the results
obtained in the screen embodied version of the study, as questions regarding
the quality of the explanation provided by the agents and the reasons why they
would have made such offer were only conducted for screen embodied agents.
Results show that for both agents, the more people would have eaten the
cookie, the more they considered the agent’s explanation to be good, and
imagined the agent to have good intentions and fewer hidden reasons. These
results support the Heuristic of Sufficient Explanation model [51] and are
consistent with previous work stating that people do attribute robots
intentions [23, 48].

As social robots are expected to be more and more present in our daily-lives
and are already used in healthcare services [35] but also to assist and serve
humans [54], it is important that people correctly understand robot’s
intentions and consider them as sufficient for people to not consider them as
threat and rather trust them [27]. A way to do so is by attributing them
mental states to understand and predict their behaviors [9]. As phantom costs
are found in both human and robot conditions, sufficient explanations provided
by humans have also to be provided by the robot to understand its actions and
intentions, especially when they are not understandable by themselves (see [1,
2, 22, 34, 44, 51]).

###  4.2 Agent: Human and Robot

An interesting part of the analyses corresponds to the social interaction
involving a human and a robot. Overall, people accepted more often an offer
made by a robot compared to a human. However, such difference is only found
when agents were screen embodied, regardless what the offer was, but not when
they were physically embodied. The Agent and the Offer variables independently
contribute to the likelihood of participants of accepting or refusing the
offer (RQ3). This means that the effect of the Agent on the choice people make
(i.e., accept or refuse the offer) does not change depending on the level of
the Offer.

The reason of these results might be related to the expectations of social
cues from the human and their absence in the screen embodied condition. A
physical interaction involves many social and non-verbal cues (e.g., gaze,
posture) whereas our screen embodied agents were limited in exhibiting such
cues. We might postulate that a dissonance occurs with the screen embodied
agent (as a picture) and what participants would have expected from a human
compared to a robot (i.e., more social cues from humans). If so, participants’
expectations about the screen embodied human were not met whereas no such high
expectations were intended to be present for the robot. On the contrary, when
agents were physically embodied, both the human and the robot might have met
people’s expectations regarding their social behaviors. This is relevant with
Thellman et al. [49] who specified that it is not the physical presence that
matters but the social presence. We consider that both matter but that they
should meet expectations people have towards them which also depends on the
physical presence. Xu et al. [56] found a significant difference in terms of
social presence in favor of a live interaction with a robot compared to a
picture associated with a textual scenario. It remains possible to think that
participants did not feel social presence of the robot even when it was
physically embodied. Another reason may be that people consider less risks
when interacting with the physically embodied human because of the context (a
person of student age, located at the University) while the context used in
the screen embodied study was simply to go for a walk, without specifying a
specific location. In the former case, it may appear easier to trust a student
at the University compared to a complete stranger when we are going for a
walk.

###  4.3 Embodiment: Screen and Physical Agents

Focusing on the Embodiment factor, people were more likely to accept an offer
(a cookie alone or a cookie associated with $2) made by a physically embodied
human compared to a screen embodied human. No such difference was found when
the agent was a robot for both screen and physical embodiment levels. Phantom
costs occurred in both embodiment levels though, effect sizes of the Offer
effect (Cookie - (Cookie + $2)) were larger when agents were physically
embodied compared to screen embodied. This supports the results showing more
phantom costs described by participants in the screen conditions. While
Vonasch et al. [50] only used text-written scenario, Xu et al. [55] showed
that a text associated with a photo provides pretty good results when
assessing attitudes towards robots. The stimuli of this study were pictures
with text-written scenario and data analyses highlight phantom costs effects.
While no study, to our knowledge, has previously explored this human-robot
interaction in the literature, we can only compare the HHI results. These
results are consistent with Vonasch et al. [50]. In their study conducted in
person, Vonasch et al. [50] found that more people accepted the cookie alone
($M=39.4\%$) compared to when the offer was associated with money
($M=20.5\%$). Our results show stronger pieces of evidence as, for the human
conditions, 64.8% of participants have accepted to eat the cookie compared to
only 24.1% when the offer was associated with a monetary outcome of $2. These
results have to be put into perspective by the geographical location of the
participants: while Vonasch et al. [50] conducted their study in the USA
(specifically, in Chapel Hill, North Carolina), the present study was
conducted in New Zealand. The higher rate of acceptance in both human
conditions may reflect the higher level of interpersonal trust in New Zealand
than in the USA as shown in The Social Report 2016 – Te pūrongo oranga
tangata666https://socialreport.msd.govt.nz/social-connectedness/trust-in-
others.html; New Zealand is one of the safest countries in the world (4th
position) while the USA are at the 131st position according to the 2023 Global
Peace Index777https://www.visionofhumanity.org/maps/.

Combining the variables Agent, Offer, and Embodiment does not exhibit a
statistically significant impact on the likelihood of accepting the offer
(RQ4). In the same vein, combining the Embodiment and Offer variables does not
influence differently the likelihood of accepting the offer (RQ6) showing that
these two variables influence independently participants’ choice regardless
the agent embodiment.

###  4.4 Decision Criteria

Participants were asked to justify their decision to accept or refuse the
offer made by the agents to understand better decision-making processes
underpinning their choices. Participants use several reasons to explain their
choice of accepting or refusing the offer made by the agent (RQ5).

Reasons to accept the offer were to (i) be excited about the offer (the cookie
and/or the money), (ii) perceiving the agent as good (e.g., trust, the agent
perceived as trustful or kind), (iii) being hungry, or even (iv) the etiquette
(accepting the offer to not look rude). reasons to refuse the offer were (i)
to perceive phantom costs (i.e., finding something suspicious), but also (ii)
not being hungry, (iii) perceiving the agent as bad (e.g., not trustful, have
bad intentions), (iv) sanitation issues or simply (v) following the rule “no
food from strangers.”

Our results show that phantom costs perception, excitement about the offer,
and refusing food from strangers are major predictors of participants’
decisions. While the latter (refusing food from strangers) is mainly food in
the screen conditions, phantom costs perceptions and excitement are found in
both screen and physically embodied agents. These results support our
hypotheses and previous research [50] stating that phantom costs and being
excited about the offer are major predictors explaining why people make their
decisions to accept or refuse an offer. People perceive the agent more
positively when the offer is not associated with a monetary outcome compared
to when it is, regardless whether it is a human or a robot, and the reverse is
also true. This shows that if an offer is too generous, people tend to
attribute bad intentions to the agent and trust it less. The differences
between human and robot agents in terms of participants’ negative perceptions
are barely not significant ($p=.054$), but go in the direction of people
having more negative perceptions of the robot compared to the human agent.
This can be explained due to the eeriness people may feel when interacting
with a robot for the first time (see Belpaeme [4], Mori et al. [26], Paetzel-
Prüsmann [32]).

While the HOSE model expects people to try to find hidden reasons for events
for which they judge explanations to be insufficient, many participants judged
the interaction with Nao inconsistent with their knowledge and expectations as
robots do not eat and do not have friends. This inconsistency led people to
perceive phantom costs associated with the offer, but also had other
consequences. This absence of relevant information from the robot sometimes
mitigated the robot autonomy perceived by participants. This brought
participants to consider that cookies were made by someone else and people did
not trust the human behind all of this. Making a choice as a function on how
we perceive the agent making the offer is supported by the literature on
trust. Mou et al. [27] showed that attributing a mind to the robot pushes
people to trust it more. This seems to go against our results associated with
the decision-criteria participants specified having used.

It is worth specifying that participants qualitatively described more often
positive and negative perceptions of the robot than the human, this is likely
due to the the likelihood of participants to interact for the first time with
a robot and show curiosity or fear and thus increase the likelihood of
justifying their choice by describing how they perceived the robot. This
perception supports the HOSE model [50] as participants perceived more
negatively agents when the offer was associated with a monetary outcome while
no sufficient explanation was provided, and more positive perceptions of
agents when they just offered a cookie as explanations were more sufficient.
Despite the barely not significant difference in terms of bad perceptions of
the agent, it is possible to think that people might trust robots less than
humans because they are more easily attributing intentions to humans and these
intentions can predict/understand better their behaviors. Moreover, people
seem to trust more easily Nao by its robotic nature (e.g., “It is a robot. I
don’t think it would poison me.”, or “I don’t think a robot would lie to me or
have ill intentions.”). Some people have expectations about robots such as
respecting Asimov’s laws by being harmless to humans.

Etiquette behaviors (e.g., accepting the offer because the agent made it, it
would be impolite to refuse) are used by participants for the cookie condition
when agents are screen embodied and more in the cookie + $2 condition when
agents were physically embodied. This difference can be explained by
differences in the agent embodiment and how people perceive risks. We suggest
two reasons for this. First, in a hypothetical scenario (i.e., online setting
with the screen embodied agents), people might not perceive risks the same way
or will not attribute a same level of social norms towards humans and robot.
This relates to social trust which is defined as the expectation that people
will behave well without attempting to harm others (see Freitag and Bauer
[10]). Participants may simply not know robot’s intentions as they certainly
never interacted with one before. Participants may consider it more
disrespectful to ignore a human and refuse the offer he made compared to a
inanimate robot. This result may goes Second, participants may have been
excited about the fact of being paid to eat in the physically embodied agent
conditions but do not specify this to the experimenter when asked to explain
their behaviors.

####  4.4.1 Behaviors Towards Physically Embodied Agents

Because in the physical embodiment condition, participants were unaware that a
study was being conducted, it provided an opportunity to observe how people
might actually respond to a robot in the wild, rather than in tightly
controlled laboratory settings where everyone knows a study is being
conducted. Some people abused the robot, others were frightened by it, and
others ignored it, as well as many people responding to it as if it were a
social agent offering them something.

Several people abused the robot, including stealing from it and touching it
aggressively. These behaviors were more common among males than females. Prior
research has shown similar antisocial behaviors toward robots [6, 17, 30, 45].
However, most studies of abusive behavior towards robots have shown that such
behaviors are common among children [6, 30]. Our study shows that they can
also be found in young adults of university student age. Moreover, the robot
asked its abusers to stop abusing it, and people’s responses were mixed. After
the robot asked people not to steal or touch it, some people stopped, but
others laughed and continued to do so, sometimes until the experimenter had to
intervene. Thus, like with preventing abuse in humans, simply asking the
abuser to stop is not always sufficient. Asking may even encourage the abuse
to continue if the abuser is rewarded by thinking the robot’s protestations
are funny Ku et al. [17]. Therefore, Nao’s request could be seen as an
encouragement to further abuse the robot. Further studies seem necessary to
find the best way for the robot to discourage abusive behavior and to protect
itself. These abusive behaviors did not occur towards humans and may therefore
reflect the way people perceive the robot. We cannot know which of the
featural differences between humans and robots caused people to abuse the
robot but not the human. One possibility is the submissiveness of the Nao
robot [42], but it might also be its height, its novelty in the social
environment, or some other feature.

Our results have to be put in perspective with potential participants who
chose to simply ignore the agents and so neither accepted or refused the
offer. Very few participants ignored the human (n = 17), but hundreds (n =
388) ignored the robot. There are several potential reasons for this. One is
because the robot’s voice is not human, people may not have heard it, or may
not have recognized that it was talking to them specifically. Another reason
is people may have felt uncomfortable having an interaction with a robot
outside of a laboratory setting–they might not have known what to do or how to
talk with a robot. The social norms are unclear. They may have also worried
about being disrespectful to a human asking them a question, but felt not such
worries about disrespecting a robot. Or they may have assumed the robot was
not as technologically advanced as it appeared, so justified ignoring it
Schneider et al. [43].

###  4.5 Limitations

Some limitations have to be discussed. The first one corresponds to the
generalisability of the results to human-robot interaction. We have used a
specific robot (Nao) which has a certain degree of human-likeness, we expect
our results to be influenced by the degree of human-likeness of the robot
people will perceive. We cannot assure such conclusions are still true, at
least to the same extent, with non human-like robots. Also, while Vonasch et
al. [50] only recruited adults sitting alone outside, we have attempted this
but a huge proportion of participants who approached the robot were in group
as most of the people alone just ignored the robot. We cannot exclude
potential group biases in these robot conditions, but results are similar to
those of the physically human conditions and phantom costs did occur. A
strength of this is that he shows better how people behave and perceive robots
when alone and in groups. This difficulty to recruit people alone was not the
case for participants who approached the human. This may be due to discomfort
alone people felt to be called out by a robot for which they did not know its
intentions.

Then, our online study was conducted with Americans and the in person study
was conducted in New Zealand. This choice was made for pragmatic reasons: our
university is in New Zealand, but Prolific’s sample of New Zealanders was too
small to efficiently generate a suitable sample size, so we used Americans
instead. This also allowed us to test this effect across both cultures,
increasing generalisability of the results, but because we used different
methods (in person versus online) for the two samples, it does not allow us to
directly compare the two cultures in how people respond in these situations.
People in both cultures showed the effect, but it is not possible to know
whether the effect might be larger in one culture than the other.

Concerning the screen embodied agents version, one might criticize that
authors have chosen to use pictures over video and/or text-written
interactions. Xu et al. [56] indicated that the chosen media should depend on
the study purpose. The authors indicated that texts and pictures are low in
both interactivity and presentation fidelity. While video-based scenario shows
several aspect that cannot be shown with a picture (e.g., dynamic of the
conversation), we showed that the use of pictures is enough to trigger phantom
costs. Moreover, the effect also occurred in real life interactions with a
robot, suggesting the phantom costs effect is robust to both real interactions
and simulated text and photo based, and in both HHI and HRI.

In the same idea of comparing the embodiment, these studies were conducted
either online or in-person, which provided methodological tradeoffs. For
example, online participants were more likely to explain the reasoning behind
their choices (because that format allowed us to easily ask them to provide
reasons), whereas in-person participants rarely did (because we wanted the
interactions to be as realistic as possible). However, studying real in-person
behavior has substantial advantages in terms of how realistic people’s
responses are, and how likely they are to generalize to real world behavior.

Concerning the physically embodied agents part, as social robots such as Nao
are still rarely present in people’s life, participants who were in the robot
conditions may have interacted with a robot for the first time in their lives.
This first exposure might have implied a novelty effect (see Belpaeme [4] that
could have influenced the likelihood of accepting the offer. Some participants
were visibly excited by the idea that a robot was talking to them, and they
might therefore have been more likely to accept its offer, due to the novelty
and excitement of the situation. However, others were visibly apprehensive and
cautious about talking to the robot, presumably because it was an unfamiliar
situation for them. This might have made them less likely to accept its offer.
Overall, people were no more willing to accept the offer from a robot than
from a human, suggesting that neither the excited nor the apprehensive
participants predominated our sample enough to bias it in one direction or the
other.

In the original study Vonasch et al. [50], the agent was a human who walked
toward participants to make the offer. However, in this study, the robot could
not walk to approach people, so we had to design a different way for it to
engage participants: It called out to them instead. To be consistent across
conditions, in the human condition, the human also called out to participants
without walking toward them. This difference from the original paradigm may
have affected our results, though it is not possible to know which type of
engaging participants would make them more likely to accept versus reject the
offer.

Finally, a large portion of people who the robot called out to ignored the
robot. Thus, we do not know whether they would have accepted its offer had
they been offered it. The proportion of people ignoring the robot was
substantially more than those who ignored the human, so this likely was due to
some characteristic of those people. It is possible that if the people who
ignored the robot had been able to participate in the study, the proportion of
people accepting the offer might have been different. There is no way to know,
and no way to fix this issue without potentially undermining the ecological
validity of the situation, which is one of the major strengths of this
research.

The study had a very strong ecological validity for three main reasons. First,
participants were not aware beforehand that they were participating in a study
when they started to interact with the agent. Second, running the study with
physically embodied agents, especially with the robot, allowed the experiments
to report observational data (i.e., social and anti social behaviors).
Finally, even if the robot conditions were conducted using the Wizard-of-Oz
method, almost no participants reported the robot’s non-autonomy in their
comments.

##  5 Conclusion and Future Work

This study explored whether phantom costs can be found in an interaction
involving a human and a robot, where agents were either physically or screen
embodied. Phantom costs occur when people are suspicious and search for hidden
reasons for an event when explanations seem insufficient. Such behaviors are
evidence that people are social agents and not fully rational. To explore
this, a human or a robot offered people either a cookie or a cookie associated
with a monetary outcome of $2. The latter is an overly generous offer with no
known reason to explain it. Whether people accepted the offer was the
dependent variable. While rational behaviors associated with conventional
economic models would lead people to accept offers that seem more
advantageous, the Heuristic of Sufficient Explanation (HOSE) economic
decision-making model shows that people will engage themselves in social
cognition by attempting to understand the intentions of the person making such
generous offers.

Results show that phantom costs do occur in HHI and HRI in both embodiment
levels (screen and physically embodied agents), more frequent with screen
embodied agents though. Also, people tend to accept more often offers made by
a robot compared to a human. This shows that the HOSE model and the concept of
phantom costs can be extended to human-robot interaction.

This provides information on how people interact and perceive social robots
compared to humans.

This indicates that people consider both humans and robots as social agents
and behave socially but irrationally with both. This even if money has no real
value and meaning for the robot.

This provides information on how people interact and perceive social robots
compared to humans. People are able to imagine hidden reasons from robots
behaviors by attributing them intentions. Thus, we show that it is possible to
attribute positive and negative intentions to robots and that they influence
human decision-making processes.

Such information is important to be known as social robots are expected to be
used as assistants and companions by helping humans for several tasks in
different contexts such as healthcare services. In such contexts, robots it is
important to ensure that robots will provide sufficient explanation/reasons
justifying their behaviors to ensure people will accept behaviors and help
from them. This shows the importance of transparency by adequately justifying
robot’s behaviors to maintain trust and acceptance from people. Also, this
study joins several scholars’ side stating that using physically embodied
agents for HRI research influences behaviors differently than screen embodied
agents but also that both embodiment levels provide significant effects of the
variables of interest.

Further research could explore the impact of physical anthropomorphism on the
perception of phantom costs and decision-making processes in HRI. This could
also be done using different type of population and experts in robotics to see
the impact of the novelty effect on human’s behaviors. We could explore to
what extent people can show irrational behaviors towards robots using
different types of offers and explanations provided by the robot, which could
have more or less meaning for a robot. It might be also interesting to link
this better to the concept of intentionality and see how attributing
intentions and autonomy to the robot influences the perception of suspicious
behaviors (i.e., phantom costs) from the robot.

## Declarations

### Author contributions

Conceptualization: BL, CB, AV; Methodology: BL, CB, AV; Formal analysis and
investigation: BL; Writing - original draft preparation: BL; Writing - review
and editing: CB, AV; Resources: CB; Supervision: CB, AV.

### Acknowledgements

We would like to thank Elena Moltchanova for her support on the power
analysis, and Kimberley Penrose and Luke Goleman for their help in recruiting
participants and coding the data.

### Data availability

All data (raw and data analysis) will be made available in a repository upon
acceptance.

### Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

### Funding

No funds, grants, or other support was received.

### Compliance with Ethical Standards

This study was approved by the University of Canterbury Human Ethics Committee
(HREC 2023/93/LR-PS). The participants provided their verbal or written
informed consent to participate in this study.

##  Appendix A Data Analyses Including all Data

This Appendix corresponds to the analyses including the data described in the
main data analyses of the paper as well as the previous data with screen
embodied agents we considered they may influence the results due to their
limitations.

###  A.1 New Participants

Without describing previous participants of the main paper, 485 participants
were recruited via the Prolific platform to take part in this online study.
The only inclusion criterion was to be fluent in English. Among the 485
participants, 4 participants were automatically not included in our dataset by
Prolific (timed-out), 10 participants were removed from the data analyses
because they did not meet the attention check justification (n = 2) or had
dietary restrictions (n = 8) leading to a final sample size of 471
participants for this online study. The gender distribution of this final
sample (N = 471) was as follows: 235 were males, 222 were females, and 14
announced having a different gender. Their age ranged from 20 to 75
($Median=29,M=32.41,SD=10.94$). While all participants claimed to be fluent in
English, the geographical distribution was broad with 27 different countries
(see Figure 6). Most participants were from South Africa (about 26%), followed
by Mexico (about 19%), UK and Northern Ireland (about 12%).

Figure 6: Country Distribution of the Participants of the Screen Embodiment
Part.

###  A.2 Results

Logistic regression analyses have been conducted using the R software to
examine the relationship between the Agent, the Embodiment, and the Offer the
Agents made, on the probability of Rejecting (0) or Accepting (1) the Offer.
The Agent was either a Human (0) or a Robot (1). They were either screen (0)
or physically (1) embodied. The Offer was either a Cookie (0) or a Cookie + $2
(1). The equation is shown in subsection A.2 and the coefficients table in
Table 5. Main effects are directly obtained from Table 5. The interactions are
explored using the emmeans package.

$\displaystyle\text{logit}(\mathbb{P}(\text{Choice}=1))=$
$\displaystyle\beta_{0}+\beta_{1}\cdot\text{Agent}+\beta_{2}\cdot\text{Offer}+\beta_{3}\cdot\text{Embodiment}$
$\displaystyle\quad+\beta_{4}\cdot(\text{Agent}\cdot\text{Offer})+\beta_{5}\cdot(\text{Agent}\cdot\text{Embodiment})+\beta_{6}\cdot(\text{Offer}\cdot\text{Embodiment})$
$\displaystyle\quad+\beta_{7}\cdot(\text{Agent}\cdot\text{Offer}\cdot\text{Embodiment})$
Table 5: Coefficients of the Three-Way Interactions Logistic Regression Model.

Effect | Estimate | Standard Error | z value | $Pr(>|z|)$
---|---|---|---|---
(Intercept) | -1.0872 | 0.1505 | -7.222 | $<.001^{*}$
Agent | 1.0699 | 0.2001 | 5.346 | $<.001^{*}$
Offer | -0.6657 | 0.2370 | -2.808 | .005∗
Embodiment | 1.6982 | 0.3223 | 5.269 | $<.001^{*}$
Agent $\times$ Offer | 0.1813 | 0.3027 | 0.599 | .549
Agent $\times$ Embodiment | -1.2454 | 0.4429 | -2.812 | .005∗
Offer $\times$ Embodiment | -1.0939 | 0.4886 | -2.239 | .025∗
Agent $\times$ Offer $\times$ Embodiment | 0.2956 | 0.6545 | 0.452 | .651

Note: *: $p<.05$

####  A.2.1 Phantom Costs

The odds of accepting the offer were 1.94 times higher ($CI95\%[1.23,3.12]$)
when the offer was a cookie compared to when it was a cookie associated with
$2. For each Agent $\times$ Embodiment, the odds of accepting the Offer were
higher when the offer was a cookie compared to a cookie associated with $2.
The odds ratio (OR = Cookie/(Cookie + $2)) of each pairwise comparison were
the followings: 1.95 for the screen embodied human, 1.62 for the screen
embodied robot, 5.81 for the physically embodied human, and 3.61 for the
physically embodied robot.

####  A.2.2 Agent: Human and Robot

The odds of accepting the offer were 2.91 times higher ($CI95\%[1.98,4.33]$)
when the Robot was making the offer compared to when the Human was making the
offer. While no difference has been found between the Human and the Robot when
they were physically embodied, neither for the cookie (p = .657) nor the
cookie + $2 condition ($p=.478$), the odds of accepting the offer were higher
for the screen embodied robot compared to the screen embodied human when the
offer was a cookie (OR = 2.91, $p<.001$) but also a cookie + $2 (OR = 3.50,
$p<.001$).

####  A.2.3 Embodiment: Screen and Physical

The odds of accepting the offer were 5.46 times higher ($CI95\%[2.94,10.45]$)
when agents were physically embodied compared to screen embodied. Only the
embodiment of the human influenced the odds of accepting the offer. The odds
of accepting the offer were 5.46 times higher when the human was physically
embodied compared to screen embodied ($p<.001$). The only difference in terms
of significance with the main data analyses of this paper is that here, we
have a significant Offer $\times$ Embodiment interaction effect ($p=.025$)
associated with the absence of a previous significant result: the odds of
accepting the cookie + $2 did not significantly differ between both levels of
the human embodiment ($p=0.010$).

####  A.2.4 Gender Analyses

Exploratory analyses have been conducted with all the data to explore
potential gender differences for both embodiment levels on the probability of
accepting the offer. To do so, ANOVA were conducted and did not show any
significant effect of the Gender ($F(2,718)=0.59,p=.557$).

##  Appendix B Wizard-of-Oz Script

##### Overall Preparation:

  1. 1.

Take all the material in the room.

  2. 2.

Go to one of the locations, close to a power socket.

  3. 3.

Turn on the laptop, the WiFi router, and the robot. The robot should be
charging every time it is on.

  4. 4.

Connect the laptop to the WiFi router via the Ethernet cable. Verify it is
connected to NaoNetwork. If not, restart.

  5. 5.

Launch Choregraph 2.1.4.

  6. 6.

Connect Nao to Choregraph: on Choregraph, click on “Connection” and “Connect
to”, write the IP address the robot provides.

  7. 7.

Check the robot is connected, and that you see the cameras of the robot on
Choregraph.

  8. 8.

Deactivate “Autonomous Life” if still on: press the heart symbol above the
robot view. The heart should be empty.

  9. 9.

Click on “Run”. If not working, restart Choregraph (also the robot if still
not working).

  10. 10.

Run “Stand Up.”

  11. 11.

Run “Eye LEDs.”

  12. 12.

Run “Ext. R arm” to extend the right arm.

  13. 13.

Place the stick in the right hand of the robot. Attach the box in the position
indicated by the Velcro fasteners. Check it is well attached.

  14. 14.

Run “Init” to position Nao in its initial position.

  15. 15.

Add three cookies in the container. If running “Cookie Dollars” condition,
attach the $2 coin on the back of the robot’s left hand.

  16. 16.

Check the sound is high enough for people to hear it.

##### Participant Arriving:

  1. 1.

Move the head of the robot (using the boxes on the screen or moving manually
the head clicking on its head on Choregraph). Continue to follow the
participant during the interaction.

  2. 2.

When someone is close to the robot, run “Hi”, “Hey”, or “Hello.”

##### If Cookie Condition:

  1. 1.

Run “Cookie.” If the participant does not understand, run “Repeat Cookie” (up
to two times).

  2. 2.

If the person understood, go to the next point. If the person did not
understand: approach them, thank them + debriefing + demographic data (specify
in the experimenter’ notes that this participant did not follow the protocol).

  3. 3.

If the participant does not take the cookie: Run “Not interested” to end the
interaction.

  4. 4.

If the participant takes the cookie Run “Thanks” to end the interaction.

  5. 5.

Approach the participant. Thank them + debriefing + ask for demographic data.
If the experimenter has any comment, write in the experimenter’s note column.

##### If Cookie + $2 Condition:

  1. 1.

Run “Cookie Dollars.” If the participant does not understand, run “Repeat
Cookie Dollars” (up to two times).

  2. 2.

If the person understood, go to the next point. If the person did not
understand: approach them, thank them + debriefing + demographic data (specify
in the experimenter’ notes that this participant did not follow the protocol).

  3. 3.

If the participant does not take the cookie: Run “Not interested” to end the
interaction.

  4. 4.

If the person takes the coin, it should automatically say “Thank you”, if not:
run it (clicking on the “Cookie Dollars” box, and the output of “Tactile L.
Hand.”

  5. 5.

Approach the participant. Thank them + debriefing + ask for demographic data.
If the experimenter has any comment, write in the experimenter’s note column.

##### After the Interaction:

  1. 1.

Run “Init” to put the robot in its initial position.

  2. 2.

Add a cookie (and a coin if “Cookie Dollars”) for the next participant.

##### Some Extra:

Some boxes are available to make the interaction more natural according to
what the participant says such as “Yes”, “No”, “I did not hear” (if the
experimenter did not hear what the person said to the robot), “Thanks”, or
“Not interested.”

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