=Paper=
{{Paper
|id=Vol-3794/paper05
|storemode=property
|title=Investigating the relationship between empathy and attribution of
mental states to robots
|pdfUrl=https://ceur-ws.org/Vol-3794/paper5.pdf
|volume=Vol-3794
|authors=Alberto Lillo,Alessandro Saracco,Elena Siletto,Claudio Mattutino,Cristina Gena
|dblpUrl=https://dblp.org/rec/conf/rfh/LilloSSMG24
}}
==Investigating the relationship between empathy and attribution of
mental states to robots==
https://ceur-ws.org/Vol-3794/paper5.pdf
Investigating the relationship between empathy and
attribution of mental states to robots
Alberto Lillo1 , Alessandro Saracco1 , Elena Siletto1 , Claudio Mattutino1 and Cristina Gena1
1
Department of Computer Science, University of Turin, Italy
Abstract
This paper describes an experimental evaluation aimed at detecting the users’ perception of the robot’s empathic abilities
during a conversation. The results have been then analyzed to search for a possible relationship between the perceived
empathy and the attribution of mental states to the robot, namely the user’s perception of the robot’s mental qualities as
compared to humans. The involved sample consisted of 68 subjects, including 34 adults and 34 between teenagers and
children. By conducting the experiment with both adult and child participants, make possible to compare the results obtained
from each group and identify any differences in perception between the various age groups.
Keywords
Human Robot Interaction, Empathy, Mental State Attribution
1. Introduction of human emotions? And how does robotic empathy
impact human perception of these new social entities?
The advent of Artificial Intelligence and robotic technolo- Simultaneously, advancements in HRI research highlight
gies has ushered in an era of extraordinary potential for the importance of developing increasingly intuitive and
humanity, radically transforming the way we live, work, natural human-robot interfaces [11, 12, 13], enabling fluid
and interact. Within this context, Human-Robot Interac- and bidirectional communication. In this sense, robot
tion (HRI) [1] emerges as a critically important field of design cannot overlook a holistic approach that consid-
study, at the crossroads of technological innovation and ers not only functional aspects but also emotional and
humanistic understanding. As technological progress relational ones, designing machines capable of ”under-
leaps forward, crucial issues arise not only about how standing” and adapting to the human context in which
robots can assist humans in their daily activities, such they are inserted.
as work [2], school [3], home [4], cleaning and caring
for vulnerable persons [5], but also about how they can
harmoniously integrate into the social dynamics that 2. State of the art
characterise our existence. Thus, the primary challenge
of HRI is not merely technical but also profoundly rela- Empathy is an intrinsically human capacity to perceive
tional: how to design robots that are not perceived as and respond to others’ emotions, represents one of the
mere machines, but as social companions, capable of em- fundamental pillars in the advancement of Human-Robot
pathy and meaningful interaction with human beings Interaction (HRI). Historically confined to human interac-
[6, 7], supporting them in their daily lives and their pref- tions, the concept of empathy has progressively extended
erential choices [8]. into the HRI field, aiming to make machines not only
This question opens the way for a broader reflection on more intelligent but also more sensitive to the human
the meaning of empathy in the robotic domain [9, 10] emotional context. In scientific and technological liter-
and the role it can play in facilitating an effective and ature, empathy in robots has emerged as a crucial area
positive social integration of robots. Empathy, tradition- of research, reflecting a paradigm shift from mere func-
ally understood as the ability to comprehend and share tional efficiency towards the socio-emotional integration
the feelings of others, becomes a desirable quality for of robots into society.
robots as well, especially in areas where the human-robot Advances in artificial intelligence, particularly in ma-
relationship is crucial, such as elderly care, education, chine learning and computer vision, have led to the cre-
and therapeutic support. The design of empathic robots, ation of systems capable of recognizing certain emotional
however, raises complex questions, not only of a tech- states, paving the way for more natural and engaging
nological nature but also philosophical and ethical: is it robot-human interactions. However, deeply understand-
possible for a machine to possess a true understanding ing and genuinely responding to complex emotional dy-
namics remain ambitious goals, given the heterogeneity
and subtlety of human emotional expressions. Enabling
Workshop Robots for Humans 2024, Advanced Visual Interfaces, Aren-
robots to empathize presents significant challenges, such
zano, June 3rd, 2024
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License as accurately interpreting human emotional signals, in-
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
cluding facial expressions, gestures, and vocal prosody, empathic abilities during a conversation. The results
and generating appropriate behavioral responses. have been then analyzed to search for a possible
Studies have shown that robots that can adjust their be- relationship between the perceived empathy and the
haviour according to the affective state or personality attribution of mental states to the robot, namely the
of a user are more accepted as interaction partners [14] user’s perception of the robot’s mental qualities as
and are seen as more friendly, caring, sympathetic, sup- compared to humans. The involved sample consisted
portive and trustworthy. Therefore, several empathic of 68 subjects, including 34 adults and 34 between
models for social robots have been proposed [15, 16]. teenagers and children. By conducting the experiment
Succeeding in this daunting challenge can have profound with both adult and child participants, make possible
positive effects on users’ attitudes towards social robots. to compare the results obtained from each group and
Responding to the user’s affective experience in a socially identify any differences in perception between the
appropriate manner is considered crucial to achieving various age groups.
user trust and satisfaction. In one experiment it was Methodology and experimental design. To achieve
found that the ability of a robot to respond with its em- the most significant results, it’s crucial to adhere to a
pathic system in a situationally appropriate manner is clear and well-defined methodology. The experiment’s
more important for comforting the user than a sophisti- organization was meticulously planned to ensure its
cated and detailed recognition of affect [17]. effective and efficient execution, leading to valid and
However, reacting empathically requires the robot’s reliable outcomes. The planning process began with
recognition of the user’s emotional state. This knowledge identifying the user categories participating in the
is challenging as it requires an evaluation of a deeply per- experiment. The ‘adults’ category includes users aged
sonal and individual experience and for these reasons 18 and above, while the ‘children and young people’
errors are likely to occur. This reinforces the importance category comprises users aged 17 and below.
of understanding how people respond to empathic capa- Each user category, the adults and the youth and
bilities if a robot behaves incongruently with the user’s children, will be split into two groups: the control
emotional experience. Inaccurate emotional responses group and the experimental group. The control group
may indeed have negative consequences on users’ eval- won’t be subjected to any changes in the independent
uations of an agent. Furthermore, virtual agents that variable, serving as a key reference point. This setup
display emotions incongruent to the situation are also allows for the evaluation of the manipulation’s impact
less appreciated by users than those that do not express by comparing the results of the two groups.
any emotion at all [18]. Experimental group. Users in this group will interact
Research on the subject, however, has yet to fully uncover with an expressive robot that can respond to the user’s
the effects of empathic behaviour in different situations, emotions and express its own state. The robot will also
including possible inaccurate responses [18]. Accord- make movements during the conversation to facilitate
ing to several neurological and psychological researches non-verbal interaction.
([19, 20] the involvement of the mirror neuron system Control group. Users in the control group will interact
is implicated in neurocognitive functions, such as social with an apathetic robot, which is programmed to
cognition, language, empathy and the Theory of Mind complete tasks without showing empathy towards the
(ToM) [21, 22], which is a human-specific ability to at- user’s emotions. The robot will exhibit a less enthusiastic
tribute mental states - intentions, thoughts, desires and and more static demeanor, with no specific movements
emotions - to oneself and others to explain and predict be- to aid non-verbal interaction.
haviour. Specifically, attribution of mental states (AMS) Independent variable. The independent variable is the
has been defined as ”the cognitive ability to reflect on one’s social and emotional skill level that will be implemented
own and others’ mental states, such as beliefs, desires, feel- in the NAO virtual robot. Specifically, there are
ings, and intentions” [23]. In [24], the authors presented two conditions for the conduct of the experiment: i)
an experimental study showing that the humanoid robot Emotional and empathic robot; ii)n Apathetic robot.
NAO is able to stimulate the attribution of mental states In the course of the experiment, this variable will be
towards itself when it stimulate empathy. This result manipulated in order to test the interaction with the two
suggests a possible correlation between empathy toward types of robots and to record the differences in users’
the robot and humans’ attribution of mental states to it. perceptions.
Dependent variables. The primary dependent variable
is the users’ reactions to the different experimental
3. The experiment conditions, encompassing all measurements and obser-
vations of users’ responses post-interaction with the
In this Section we introduce an experimental evaluation
robot. These responses will be primarily gauged through
aimed at detecting the users’ perception of the robot’s
a structured interview. Another potential dependent
variable is task performance, with a focus on whether the five mental state categories. T-tests will be calculated for
user successfully completes the intended task. External both the experimental and control groups as a whole,
variables that could impact the results should also be and separately for children and adults. This allows for
considered. Analyzing the dependent variables’ data data interpretation across both groups and distinct age
will enable the assessment of the independent variable categories.
manipulation’s effect on the research.
Sample Selection. When conducting experiments, it’s
crucial to choose a representative sample to prevent 3.1. Experimental plan
bias. However, for this experiment, the sample wasn’t
randomly selected but was chosen from a readily Each participant, numbered 1 to 34 based on their par-
available and willing group. Particularly, most children ticipation order, will be randomly assigned to either the
in the sample were from a cooperative dance school. experimental or control group. A random number gen-
Therefore, while the sample isn’t fully representative erator was used for this assignment, with the first 17
of all user categories, it provides a solid foundation for numbers allocated to the experimental group and the
future research. remaining 17 to the control group. This procedure ap-
Measurement and Instruments. The data collection plies to both ‘adults’ and ‘children and young people’
for the dependent variables will utilize quantitative categories. The test will proceed in multiple stages. Each
measurements, which allows for numerical data col- of these steps has a precise objective and are designed to
lection and subsequent statistical analysis. Initially, be able to collect valid and reliable data:
a questionnaire was selected as the most appropriate Introduction. In this initial stage, the user will be
method for collecting this data. However, considering greeted and given a brief introduction. Only essential
the administration method, a structured interview was details for interacting with NAO will be provided at this
introduced instead. This method, unlike questionnaires, time, while answers to additional inquiries will be de-
provides the opportunity to clarify questions, aiding ferred until the experiment’s conclusion.
participants, including children, to fully understand Interaction and task. In this stage, the user will interact
and comfortably participate in data collection. The with the NAO virtual robot. The session will start with
interview process involves asking the user to score each an introduction between the robot and the user. Follow-
question on a rating scale. Specific tools have been ing topics will include interests and family, culminating
utilized for this process. Batson’s self-assessment: This in the final task.
assessment [25] asks participants to rate their experience Gathering quantitative information. Once the test
of specific emotions on a scale from 1 to 5. For the Batson has been completed, the user will immediately be given
self-assessment, users will score their emotions on a a questionnaire containing the necessary quantitative
scale from ‘Not at all’ to ‘Totally’. The assessment will measurements, namely, the Batson self-assessment [25]
evaluate 23 emotions, expressed through the following and the AMS questionnaire [26, 27].
adjectives: frightened, suffering, sympathetic, sensitive, Closing. In this last phase we will move on to the final
agitated, cordial, worried, stressed, sad, compassionate, greetings and thanks, answering users’ questions and
upset, tender, distressed, impressed, downhearted, curiosities.
depressed, afflicted, annoyed, kind, melancholic, moved,
and uncomfortable. 3.2. Data Analysis
AMS-Q questionnaire: the administration of this ques-
tionnaire [26, 27] will allow us to perceive the degree to Upon completion of the experiment and data collection,
which users attribute mental states to the NAO robot. various quantitative analyses will be conducted using
The test consists of 25 questions and asks users to rate Excel. For the Batson self-assessment results, the mean
whether they think the robot (e.g. ”can you understand?”, score and standard deviation for each emotion will be
”can it decide?”, ”can you tell a lie?”, ”can you try to do calculated for each group. A T-test will then be performed
something?”). to determine if there’s a significant difference between
Upon completion of the experiment and data collection, the means of the experimental and control groups. The
various quantitative analyses will be conducted using AMS questionnaire results will undergo a similar analysis,
Excel. For the Batson self-assessment results [25], the but will first be divided into five mental state categories.
mean score and standard deviation for each emotion T-tests will be calculated for both the experimental and
will be calculated for each group. A T-test will then be control groups as a whole, and separately for children
performed to determine if there’s a significant difference and adults. This allows for data interpretation across
between the means of the experimental and control both groups and distinct age categories.
groups. The AMS questionnaire [26, 27] results will
undergo a similar analysis, but will first be divided into
4. Creating the personality of the 5. Results and Comparisons
virtual robot NAO 5.1. ’Children and Youth’ category
The process of defining the personality of NAO consti- Comparing the Batson self-assessment results, both
tuted the first step necessary for the implementation of the experimental and control groups reported minimal
the robot. For this implementation, we focused on the negative emotions, with over 88% stating they felt
creation of a Personas. This step then made it possible ‘Not at all’ distressed, worried, stressed, sad, upset,
to program the robot and write dialogues that were con- downhearted, depressed, distressed, and annoyed.
sistent with each other and with the robot’s personality. However, differences emerged in positive emotions, with
In order to describe and frame the desired personality of 100% of the experimental group feeling ‘Totally’ nice and
the robot, the Big Five [28] test was performed. Thanks kind, compared to 76.5% in the control group. The T-test
to this test, it was possible to optimise and think about showed minimal significance levels for ‘Sympathetic’
character traits. In a first step, the job that NAO could (t-stat=-2.135, df=32, p<0.05) and ‘Kind’ (t-stat=-2.063,
hypothetically perform was chosen. The professional df=32, p<0.05). Regarding the AMS questionnaire,
figure identified was the teacher. This, it was assessed, similar average scores were observed in all dimensions
would fit well with the envisaged personality and the for both groups: epistemic (7.5 vs. 8.9), emotional (7.5 vs.
subsequent task. 7.7), desires and intentions (8.8 vs. 9.7), imagination (7.6
During the following phase, the main characteristics to vs. 8.8), and perceptual (6.8 vs. 6.7). The T-test did not
be attributed to the robot were chosen, which were con- reveal any significance, but it’s noteworthy that high
sidered fundamental, such as the traits: calm, patient and average scores were obtained in each dimension for both
wise. Once these elements had been identified, the test groups.
was completed. The results obtained as follows.
Emotional Stability: 37 out of 120.
The robot was chosen to have a very positive attitude
that rarely experiences negative emotions. He is charac- 5.2. ”Adult” Category
terised by a sunny and patient manner. These traits were Comparing the Batson Self-Assessment results, both
also favoured with a view to safe interaction. the experimental and control groups reported minimal
Extraversion: 88 out of 120. negative emotions, with over 82% stating they felt ‘Not
As discussed extensively in chapter one, robots need to be at all’ scared, hurt, sad, upset, distressed, downhearted,
able to communicate and engage in interaction with ease depressed, annoyed, melancholic, and uncomfortable.
in order to be recognised as social agents. This character However, differences emerged in positive emotions.
trait emerged from this reflection. In particular, high The experimental group reported total sympathy for
scores in this category describe a sociable and assertive the robot by 64.75%, while the control group reported
personality. This could lead NAO to make friends very 41.25%.
quickly and relate to users. The experimental group, which interacted with a
Openness to experience: 88 out of 120. more emotional robot, reported feeling somewhat more
In order to model a robot that would then be credible sensitive, with only 23.5% saying ‘Not at all’, compared to
when tested with users, it was decided not to attribute 53% in the control group. This difference was significant.
characteristics to NAO that could be considered unthink- The experimental group also reported feeling more
able. Its character was, therefore, calibrated to be based friendly towards the robot, with 82.4% reporting ‘Totally’,
on facts. compared to only 29.5% in the control group. This
Conscientiousness: 106 out of 120. emotion was found to be significant (t-stat=-2.212, df=32,
This is the category where NAO scored the highest, since p<0.05). For the feeling ‘impressed’, no subjects in the
it has been designed to be a responsible, organised and experimental group reported ‘Not at all’ and 41.25%
disciplined robot. Furthermore, questions concerning indicated ‘Very much’. In contrast, 64.7% of the subjects
levels of confidence in one’s own abilities were always in the control group answered ‘Not at all’ and none
answered as ’agree’ and ’very agree’. indicated ‘Totally’. This emotion was also found to be
Agreeableness: 101 out of 120. significant (t-stat=-5.944, df=32, p=0.000001). Regarding
In terms of values, sincerity and the spirit of cooperation the AMS questionnaire, divergent averages were noted
were favored. Those who score high on this trait are also for all dimensions between the control and experimental
characterized by kindness and altruism. group: epistemic (5.5 vs. 8.2), emotional (1.9 vs. 7.5),
desires and intentions (4.0 vs. 7.9), imagination (2.0 vs.
6.9), and perceptual (2.4 vs. 5.6). Upon applying the
T-test, the dimensions epistemic (t-stat=-2.81, df=32,
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