Socialization between toddlers and robots at an early childhood education center

A Novel Implementation of a Social Robot for Sustainable Human Engagement in Homecare Services for Ageing Populations

This research addresses the rapid aging phenomenon prevalent in Asian societies, which has led to a significant increase in elderly individuals relocating to nursing homes due to health-related issues. This trend has resulted in social isolation and loneliness among the elderly, contributing to physical and mental ailments such as hypertension and cardiovascular diseases, as highlighted by the World Health Organization (WHO). To mitigate these issues, the research proposes leveraging technology, specifically the integration of robotics, to alleviate the caregiver shortage and enhance human interaction for the elderly. The novel approach involves developing a social robot designed to bridge the gap between humans and machines, combining knowledge from embedded systems, robotics, and essential soft skills for effective interaction. The authors found that this technological solution holds promise in addressing the caregiver shortage and improving the well-being of elderly individuals by reducing their sense of isolation, fostering better mental and physical health outcomes, and potentially transforming the landscape of elderly care through innovative technological applications. Future work includes expanding pilot studies and collaborating with healthcare institutions to further validate the effectiveness of the solution.

Propensity to trust shapes perceptions of comforting touch between trustworthy human and robot partners

Touching a friend to comfort or be comforted is a common prosocial behaviour, firmly based in mutual trust. Emphasising the interactive nature of trust and touch, we suggest that vulnerability, reciprocity and individual differences shape trust and perceptions of touch. We further investigate whether these elements also apply to companion robots. Participants (n = 152) were exposed to four comics depicting human-human or human-robot exchanges. Across conditions, one character was sad, the other initiated touch to comfort them, and the touchee reciprocated the touch. Participants first rated trustworthiness of a certain character (human or robot in a vulnerable or comforting role), then evaluated the two touch phases (initiation and reciprocity) in terms of interaction realism, touch appropriateness and pleasantness, affective state (valence and arousal) attributed to the characters. Results support an interactive account of trust and touch, with humans being equally trustworthy when comforting or showing vulnerability, and reciprocity of touch buffering sadness. Although these phenomena seem unique to humans, propensity to trust technology reduces the gap between how humans and robots are perceived. Two distinct trust systems emerge: one for human interactions and another for social technologies, both necessitating trust as a fundamental prerequisite for meaningful physical contact.

Touching a Mechanical Body: The Role of Anthropomorphic Framing in Physiological Arousal When Touching a Robot

The growing prevalence of social robots in various fields necessitates a deeper understanding of touch in Human-Robot Interaction (HRI). This study investigates how human-initiated touch influences physiological responses during interactions with robots, considering factors such as anthropomorphic framing of robot body parts and attributed gender. Two types of anthropomorphic framings are applied: the use of anatomical body part names and assignment of male or female gender to the robot. Higher physiological arousal was observed when touching less accessible body parts than when touching more accessible body parts in both conditions. Results also indicate that using anatomical names intensifies arousal compared to the control condition. Additionally, touching the male robot resulted in higher arousal in all participants, especially when anatomical body part names were used. This study contributes to the understanding of how anthropomorphic framing and gender impact physiological arousal in touch interactions with social robots, offering valuable insights for social robotics development.

Feeling with a robot-the role of anthropomorphism by design and the tendency to anthropomorphize in human-robot interaction

The implementation of anthropomorphic features in regard to appearance and framing is widely supposed to increase empathy towards robots. However, recent research used mainly tasks that are rather atypical for daily human-robot interactions like sacrificing or destroying robots. The scope of the current study was to investigate the influence of anthropomorphism by design on empathy and empathic behavior in a more realistic, collaborative scenario. In this online experiment, participants collaborated either with an anthropomorphic or a technical looking robot and received either an anthropomorphic or a technical description of the respective robot. After the task completion, we investigated situational empathy by displaying a choice-scenario in which participants needed to decide whether they wanted to act empathically towards the robot (sign a petition or a guestbook for the robot) or non empathically (leave the experiment). Subsequently, the perception of and empathy towards the robot was assessed. The results revealed no significant influence of anthropomorphism on empathy and participants' empathic behavior. However, an exploratory follow-up analysis indicates that the individual tendency to anthropomorphize might be crucial for empathy. This result strongly supports the importance to consider individual difference in human-robot interaction. Based on the exploratory analysis, we propose six items to be further investigated as empathy questionnaire in HRI.

Robots with tears can convey enhanced sadness and elicit support intentions

The behaviour of shedding tears is a unique human expression of emotion. Human tears have an emotional signalling function that conveys sadness and a social signalling function that elicits support intention from others. The present study aimed to clarify whether the tears of robots have the same emotional and social signalling functions as human tears, using methods employed in previous studies conducted on human tears. Tear processing was applied to robot pictures to create pictures with and without tears, which were used as visual stimuli. In Study 1, the participants viewed pictures of robots with and without tears and rated the intensity of the emotion experienced by the robot in the picture. The results showed that adding tears to a robot's picture significantly increased the rated intensity of sadness. Study 2 measured support intentions towards a robot by presenting a robot's picture with a scenario. The results showed that adding tears to the robot's picture also increased the support intentions indicating that robot tears have emotional and social signalling functions similar to those of human tears.

Revisiting the video deficit in technology-saturated environments: Successful imitation from people, screens, and social robots

The "video deficit" is a well-documented effect whereby children learn less well about information delivered via a screen than the same information delivered in person. Research suggests that increasing social contingency may ameliorate this video deficit. The current study instantiated social contingency to screen-based information by embodying the screen within a socially interactive robot presented to urban Australian children with frequent exposure to screen-based communication. We failed to document differences between 22- to 26-month-old children's (N = 80) imitation of screen-based information embedded in a social robot and in-person humans. Furthermore, we did not replicate the video deficit with children imitating at similar levels regardless of the presentation medium. This failure to replicate supports the findings of a recent meta-analysis of video deficit research whereby there appears to be a steady decrease over time in the magnitude of the video deficit effect. We postulate that, should the video deficit effect be truly dwindling in effect size, the video deficit may soon be a historical artifact as children begin perceiving technology as relevant and meaningful in everyday life more and more. This research finds that observational-based learning material can be successfully delivered in person, via a screen, or via a screen embedded in a social robot.

Learning from others is good, with others is better: the role of social interaction in human acquisition of new knowledge

Learning in humans is highly embedded in social interaction: since the very early stages of our lives, we form memories and acquire knowledge about the world from and with others. Yet, within cognitive science and neuroscience, human learning is mainly studied in isolation. The focus of past research in learning has been either exclusively on the learner or (less often) on the teacher, with the primary aim of determining developmental trajectories and/or effective teaching techniques. In fact, social interaction has rarely been explicitly taken as a variable of interest, despite being the medium through which learning occurs, especially in development, but also in adulthood. Here, we review behavioural and neuroimaging research on social human learning, specifically focusing on cognitive models of how we acquire semantic knowledge from and with others, and include both developmental as well as adult work. We then identify potential cognitive mechanisms that support social learning, and their neural correlates. The aim is to outline key new directions for experiments investigating how knowledge is acquired in its ecological niche, i.e. socially, within the framework of the two-person neuroscience approach. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Precuneus brain response changes differently during human-robot and human-human dyadic social interaction

Human–human interactions (HHI) and human–robot interactions (HRI) are compared to identify differences between cognitive processes reflecting bonding in social interactions with natural and artificial agents. We capitalize on a unique corpus of neuroimaging data (fMRI) recorded while participants freely discussed with another human or a conversational robotic head, in order to study a crucial parameter of human social cognition, namely that social interactions are adaptive bidirectional processes that evolve over time. We used linear statistics to identify regions of the brain where activity changes differently when participants carry out twelve one-minute conversations, alternating between a human and a robotic interlocutor. Results show that activity in the posterior cingulate cortex, a key region associated with social cognition, increases over time in HHI but not in HRI. These results are interpreted as reflecting a process of strengthening social bonding during repeated exchanges when the interacting agent is a human, but not a robot.

Facial Emotion Expressions in Human–Robot Interaction: A Survey

Facial expressions are an ideal means of communicating one’s emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In the case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real-time will be covered. For robotic facial expression generation, hand-coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real-time is comparatively lower. In the case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically. In this overview, state-of-the-art research in facial emotion expressions during human–robot interaction has been discussed leading to several possible directions for future research.

Endowing a NAO Robot With Practical Social-Touch Perception

Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.

Speed Dating with Voice User Interfaces: Understanding How Families Interact and Perceive Voice User Interfaces in a Group Setting

As voice-user interfaces (VUIs), such as smart speakers like Amazon Alexa or social robots like Jibo, enter multi-user environments like our homes, it is critical to understand how group members perceive and interact with these devices. VUIs engage socially with users, leveraging multi-modal cues including speech, graphics, expressive sounds, and movement. The combination of these cues can affect how users perceive and interact with these devices. Through a set of three elicitation studies, we explore family interactions (N = 34 families, 92 participants, ages 4–69) with three commercially available VUIs with varying levels of social embodiment. The motivation for these three studies began when researchers noticed that families interacted differently with three agents when familiarizing themselves with the agents and, therefore, we sought to further investigate this trend in three subsequent studies designed as a conceptional replication study. Each study included three activities to examine participants’ interactions with and perceptions of the three VUIS in each study, including an agent exploration activity, perceived personality activity, and user experience ranking activity. Consistent for each study, participants interacted significantly more with an agent with a higher degree of social embodiment, i.e., a social robot such as Jibo, and perceived the agent as more trustworthy, having higher emotional engagement, and having higher companionship. There were some nuances in interaction and perception with different brands and types of smart speakers, i.e., Google Home versus Amazon Echo, or Amazon Show versus Amazon Echo Spot between the studies. In the last study, a behavioral analysis was conducted to investigate interactions between family members and with the VUIs, revealing that participants interacted more with the social robot and interacted more with their family members around the interactions with the social robot. This paper explores these findings and elaborates upon how these findings can direct future VUI development for group settings, especially in familial settings.

Tactile Interaction with a Humanoid Robot: Effects on Physiology and Subjective Impressions

This study investigated how touching and being touched by a humanoid robot affects human physiology, impressions of the interaction, and attitudes towards humanoid robots. 21 healthy adult participants completed a 3 (touch style: touching, being touched, pointing) × 2 (body part: hand vs buttock) within-subject design using a Pepper robot. Skin conductance response (SCR) was measured during each interaction. Perceived impressions of the interaction (i.e., friendliness, comfort, arousal) were measured per questionnaire after each interaction. Participants’ demographics and their attitude towards robots were also considered. We found shorter SCR rise times in the being touched compared to the touching condition, possibly reflecting psychological alertness to the unpredictability of robot-initiated contacts. The hand condition had shorter rise times than the buttock condition. Most participants evaluated the hand condition as most friendly and comfortable and the robot-initiated interactions as most arousing. Interacting with Pepper improved attitudes towards robots. Our findings require future studies with larger samples and improved procedures. They have implications for robot design in all domains involving tactile interactions, such as caring and intimacy.

The early ontogeny of infants' imitation of on screen humans and robots

Traditionally, infants have learned how to interact with objects in their environment through direct observations of adults and peers. In recent decades these models have been available over different media, and this has introduced non-human agents to infants' learning environments. Humanoid robots are increasingly portrayed as social agents in on screen, but the degree to which infants are capable of observational learning from screen-based robots is unknown. The current study thus investigated how well 1- to 3-year-olds (N = 230) could imitate on-screen robots relative to on-screen and live humans. Participants exhibited an imitation deficit for robots that varied with age. Furthermore, the well-known video deficit did not replicate as expected, and was weak and transient relative to past research. Together, the findings documented here suggest that infants are learning from media in ways that differ from past generations, but that this new learning is nuanced when novel technologies are involved.

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.

Children conform, adults resist: A robot group induced peer pressure on normative social conformity

People are known to change their behavior and decisions to conform to others, even for obviously incorrect facts. Because of recent developments in artificial intelligence and robotics, robots are increasingly found in human environments, and there, they form a novel social presence. It is as yet unclear whether and to what extent these social robots are able to exert pressure similar to human peers. This study used the Asch paradigm, which shows how participants conform to others while performing a visual judgment task. We first replicated the finding that adults are influenced by their peers but showed that they resist social pressure from a group of small humanoid robots. Next, we repeated the study with 7- to 9-year-old children and showed that children conform to the robots. This raises opportunities as well as concerns for the use of social robots with young and vulnerable cross-sections of society; although conforming can be beneficial, the potential for misuse and the potential impact of erroneous performance cannot be ignored.

Mind Meets Machine: Towards a Cognitive Science of Human-Machine Interactions

As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. Consequently, the need to understand the mechanisms supporting human-machine interactions is becoming increasingly pressing. We introduce a framework for studying the cognitive and brain mechanisms that support human-machine interactions, leveraging advances made in cognitive neuroscience to link different levels of description with relevant theory and methods. We highlight unique features that make this endeavour particularly challenging (and rewarding) for brain and behavioural scientists. Overall, the framework offers a way to study the cognitive science of human-machine interactions that respects the diversity of social machines, individuals' expectations and experiences, and the structure and function of multiple cognitive and brain systems.

Pragmatics in the False-Belief Task: Let the Robot Ask the Question!

The poor performances of typically developing children younger than 4 in the first-order false-belief task "Maxi and the chocolate" is analyzed from the perspective of conversational pragmatics. An ambiguous question asked by an adult experimenter (perceived as a teacher) can receive different interpretations based on a search for relevance, by which children according to their age attribute different intentions to the questioner, within the limits of their own meta-cognitive knowledge. The adult experimenter tells the child the following story of object-transfer: "Maxi puts his chocolate into the green cupboard before going out to play. In his absence, his mother moves the chocolate from the green cupboard to the blue one." The child must then predict where Maxi will pick up the chocolate when he returns. To the child, the question from an adult (a knowledgeable person) may seem surprising and can be understood as a question of his own knowledge of the world, rather than on Maxi's mental representations. In our study, without any modification of the initial task, we disambiguate the context of the question by (1) replacing the adult experimenter with a humanoid robot presented as "ignorant" and "slow" but trying to learn and (2) placing the child in the role of a "mentor" (the knowledgeable person). Sixty-two typical children of 3 years-old completed the first-order false belief task "Maxi and the chocolate," either with a human or with a robot. Results revealed a significantly higher success rate in the robot condition than in the human condition. Thus, young children seem to fail because of the pragmatic difficulty of the first-order task, which causes a difference of interpretation between the young child and the experimenter.

Are You Still With Me? Continuous Engagement Assessment From a Robot's Point of View

Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way toward in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behavior optimization. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilizing CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community, at https://github.com/LCAS/engagement_detector, as a tool to measure engagement in a variety of settings.

Preschoolers' Motivation to Over-Imitate Humans and Robots

From preschool age, humans tend to imitate causally irrelevant actions-they over-imitate. This study investigated whether children over-imitate even when they know a more efficient task solution and whether they imitate irrelevant actions equally from a human compared to a robot model. Five-to-six-year-olds (N = 107) watched either a robot or human retrieve a reward from a puzzle box. First a model demonstrated an inefficient (Trial 1), then an efficient (Trial 2), then again the inefficient strategy (Trial 3). Subsequent to each demonstration, children copied whichever strategy had been demonstrated regardless of whether the model was a human or a robot. Results indicate that over-imitation can be socially motivated, and that humanoid robots and humans are equally likely to elicit this behavior.

Robotic transformative service research: deploying social robots for consumer well-being during COVID-19 and beyond

Purpose

Besides the direct physical health consequences, through social isolation COVID-19 affects a considerably larger share of consumers with deleterious effects for their psychological well-being. Two vulnerable consumer groups are particularly affected: older adults and children. The purpose of the underlying paper is to take a transformative research perspective on how social robots can be deployed for advancing the well-being of these vulnerable consumers and to spur robotic transformative service research (RTSR).

Design/methodology/approach

This paper follows a conceptual approach that integrates findings from various domains: service research, social robotics, social psychology and medicine.

Findings

Two key findings advanced in this paper are (1) a typology of robotic transformative service (i.e. entertainer, social enabler, mentor and friend) as a function of consumers' state of social isolation, well-being focus and robot capabilities and (2) a future research agenda for RTSR.

Practical implications

This paper guides service consumers and providers and robot developers in identifying and developing the most appropriate social robot type for advancing the well-being of vulnerable consumers in social isolation.

Originality/value

This study is the first to integrate social robotics and transformative service research by developing a typology of social robots as a guiding framework for assessing the status quo of transformative robotic service on the basis of which it advances a future research agenda for RTSR. It further complements the underdeveloped body of service research with a focus on eudaimonic consumer well-being.

A neurocognitive investigation of the impact of socializing with a robot on empathy for pain

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socializing intervention to probe the flexibility of empathy, a core component of social relationships, towards robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socializing with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socializing intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socializing. Whole-brain analysis showed that, before the socializing intervention, superior parietal and early visual regions are sensitive to novel agents, while after socializing, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socializing scan session. Together, these findings suggest that a short socialization intervention with a social robot does not lead to discernible differences in empathy towards the robot, as measured by behavioural or brain responses. We discuss the extent to which long-term socialization with robots might shape social cognitive processes and ultimately our relationships with these machines. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.

Now We’re Talking

This article presents a study in which we explored the effect of a social robot on the explanatory behavior of children (aged 6--10) while working on an inquiry learning task. In a comparative experiment, we offered children either a baseline Computer Aided Learning (CAL) system or the same CAL system that was supplemented with a social robot to verbally explain their thoughts to. Results indicate that when children made observations in an inquiry learning context, the robot was better able to trigger elaborate explanatory behavior. First, this is shown by a longer duration of explanatory utterances by children who worked with the robot compared to the baseline CAL system. Second, a content analysis of the explanations indicated that children who worked with the robot included more relevant utterances about the task in their explanation. Third, the content analysis shows that children made more logical associations between relevant facets in their explanations when they explained to a robot compared to a baseline CAL system. These results show that social robots that are used as extensions to CAL systems may be beneficial for triggering explanatory behavior in children, which is associated with deeper learning.

Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories

The ability to realize the individuals' impressions during the verbal communication allows social robots to significantly facilitate their social interactions in such areas as child education and elderly care. However, such impressions are highly subjective and internalized and therefore cannot be easily comprehended through behavioural observations. Although brain-machine interface suggests the utility of the brain information in human-robot interaction, previous studies did not consider its potential for estimating the internal impressions during verbal communication. In this article, we introduce a novel approach to estimation of the individuals' perceived difficulty of stories using the quantified information content of their prefrontal cortex activity. We demonstrate the robustness of our approach by showing its comparable performance in face-to-face, humanoid, speaker, and video-chat settings. Our results contribute to the field of socially assistive robotics by taking a step toward enabling robots determine their human companions' perceived difficulty of conversations, thereby enabling these media to sustain their communication with humans by adapting to individuals' pace and interest in response to conversational nuances and complexity.

Facial Pre-Touch Space Differentiates the Level of Openness Among Individuals

Social and cognitive psychology provide a rich map of our personality landscape. What appears to be unexplored is the correspondence between these findings and our behavioural responses during day-to-day life interaction. In this article, we utilize cluster analysis to show that the individuals' facial pre-touch space can be divided into three well-defined subspaces and that within the first two immediate clusters around the face area such distance information significantly correlate with their openness in the five-factor model (FFM). In these two clusters, we also identify that the individuals' facial pre-touch space can predict their level of openness that are further categorized into six distinct levels with a highly above chance accuracy. Our results suggest that such personality factors as openness are not only reflected in individuals' behavioural responses but also these responses allow for a fine-grained categorization of individuals' personality.

Child-Robot Relationship Formation: A Narrative Review of Empirical Research

This narrative review aimed to elucidate which robot-related characteristics predict relationship formation between typically-developing children and social robots in terms of closeness and trust. Moreover, we wanted to know to what extent relationship formation can be explained by children's experiential and cognitive states during interaction with a robot. We reviewed 86 journal articles and conference proceedings published between 2000 and 2017. In terms of predictors, robots' responsiveness and role, as well as strategic and emotional interaction between robot and child, increased closeness between the child and the robot. Findings about whether robot features predict children's trust in robots were inconsistent. In terms of children's experiential and cognitive states during interaction with a robot, robot characteristics and interaction styles were associated with two experiential states: engagement and enjoyment/liking. The literature hardly addressed the impact of experiential and cognitive states on closeness and trust. Comparisons of children's interactions with robots, adults, and objects showed that robots are perceived as neither animate nor inanimate, and that they are entities with whom children will likely form social relationships. Younger children experienced more enjoyment, were less sensitive to a robot's interaction style, and were more prone to anthropomorphic tendencies and effects than older children. Tailoring a robot's sex to that of a child mainly appealed to boys.

Empathic Robot for Group Learning

This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with groups of students? To answer these questions, we developed an autonomous robot with empathic competencies that is able to interact with a group of students in a learning activity about sustainable development. Two studies were conducted. The first study compares learning outcomes in children across three conditions: learning with an empathic robot; learning with a robot without empathic capabilities; and learning without a robot. The results show that the autonomous robot with empathy fosters meaningful discussions about sustainability, which is a learning outcome in sustainability education. The second study features groups of students who interact with the robot in a school classroom for 2 months. The long-term educational interaction did not seem to provide significant learning gains, although there was a change in game-actions to achieve more sustainability during game-play. This result reflects the need to perform more long-term research in the field of educational robots for group learning.

Multiscale Entropy Quantifies the Differential Effect of the Medium Embodiment on Older Adults Prefrontal Cortex during the Story Comprehension: A Comparative Analysis

Todays’ communication media virtually impact and transform every aspect of our daily communication and yet the extent of their embodiment on our brain is unexplored. The study of this topic becomes more crucial, considering the rapid advances in such fields as socially assistive robotics that envision the use of intelligent and interactive media for providing assistance through social means. In this article, we utilize the multiscale entropy (MSE) to investigate the effect of the physical embodiment on the older people’s prefrontal cortex (PFC) activity while listening to stories. We provide evidence that physical embodiment induces a significant increase in MSE of the older people’s PFC activity and that such a shift in the dynamics of their PFC activation significantly reflects their perceived feeling of fatigue. Our results benefit researchers in age-related cognitive function and rehabilitation who seek for the adaptation of these media in robot-assistive cognitive training of the older people. In addition, they offer a complementary information to the field of human-robot interaction via providing evidence that the use of MSE can enable the interactive learning algorithms to utilize the brain’s activation patterns as feedbacks for improving their level of interactivity, thereby forming a stepping stone for rich and usable human mental model.

Sensorimotor Oscillations During a Reciprocal Touch Paradigm With a Human or Robot Partner

Robots provide an opportunity to extend research on the cognitive, perceptual, and neural processes involved in social interaction. This study examined how sensorimotor oscillatory electroencephalogram (EEG) activity can be influenced by the perceived nature of a task partner – human or robot – during a novel “reciprocal touch” paradigm. Twenty adult participants viewed a demonstration of a robot that could “feel” tactile stimulation through a haptic sensor on its hand and “see” changes in light through a photoreceptor at the level of the eyes; the robot responded to touch or changes in light by moving a contralateral digit. During EEG collection, participants engaged in a joint task that involved sending tactile stimulation to a partner (robot or human) and receiving tactile stimulation back. Tactile stimulation sent by the participant was initiated by a button press and was delivered 1500 ms later via an inflatable membrane on the hand of the human or on the haptic sensor of the robot partner. Stimulation to the participant’s finger (from the partner) was sent on a fixed schedule, regardless of partner type. We analyzed activity of the sensorimotor mu rhythm during anticipation of tactile stimulation to the right hand, comparing mu activity at central electrode sites when participants believed that tactile stimulation was initiated by a robot or a human, and to trials in which “nobody” received stimulation. There was a significant difference in contralateral mu rhythm activity between anticipating stimulation from a human partner and the “nobody” condition. This effect was less pronounced for anticipation of stimulation from the robot partner. Analyses also examined beta rhythm responses to the execution of the button press, comparing oscillatory activity when participants sent tactile stimulation to the robot or the human partner. The extent of beta rebound at frontocentral electrode sites following the button press differed between conditions, with a significantly larger increase in beta power when participants sent tactile stimulation to a robot partner compared to the human partner. This increase in beta power may reflect greater predictably in event outcomes. This new paradigm and the novel findings advance the neuroscientific study of human–robot interaction.

The Effect of Personalization in Longer-Term Robot Tutoring

The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization system designed for longer-term personalization that orders curriculum based on an adaptive Hidden Markov Model (HMM) that evaluates students’ skill proficiencies. We present a study investigating the effectiveness of this system in a five-session interaction with a robot tutor, taking place over the course of 2 weeks. Our system is evaluated in the context of native Spanish-speaking first-graders interacting with a social robot tutor while completing an English Language Learning educational task. Participants either received lessons: (1) ordered by our adaptive HMM personalization system which selects a lesson based on a skill that the individual participant needs more practice with (“personalized condition”) or (2) ordered randomly from among the lessons the participant had not yet seen (“non-personalized condition”). We found that participants who received personalized lessons from the robot tutor outperformed participants who received non-personalized lessons on a post-test by 2.0 standard deviations on average, corresponding to a mean learning gain in the 98th percentile.

A neurocognitive investigation of the impact of socialising with a robot on empathy for pain.. [DOI: 10.1101/470534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socialising intervention to probe the flexibility of empathy, a core component of social relationships, toward robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socialising with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socialising intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socialising. Whole-brain analysis showed that, before the socialising intervention, superior parietal and early visual regions are sensitive to novel agents, while after socialising, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socialising scan session. Together, these findings suggest that a short socialisation intervention with a social robot does not lead to discernible differences in empathy toward the robot, as measured by behavioural or brain responses. We discuss the extent to which longer term socialisation with robots might shape social cognitive processes and ultimately our relationships with these machines.

Learning by Teaching with Humanoid Robot: A New Powerful Experimental Tool to Improve Children’s Learning Ability

Browsing the literature shows that an increasing number of authors choose to use the learning by teaching approach in the field of educational robotics. The goal of this paper is, on the one hand, to produce a review of articles describing the effects of this approach on learning and, on the other hand, to review the literature in order to explore the characteristics at the core of this approach. We will only focus on the work using a humanoid robot. The areas of learning studied are writing, reading, vocabulary, and reasoning, but also there are some metacognitive abilities like task commitment and mental state attribution. Their targets are from very young children to preadolescents. We can already notice some studies on pupils with special educational needs. In all of these domains, the results show a nonnegligible effect of learning by teaching both on learning and on metacognitive abilities. If the concept of learning by teaching is clear, a careful investigation of the different studies shows that experimental paradigms do not use the same basic characteristics. For some, it is the robot’s weakness, the care that must be given to it, which is the main requirement for the approach, while for others it is the unbalanced distribution of knowledge which is at the heart of it. The learning by teaching approach we will study has two components: the robot and the child tutor. The characteristics of the robot and what is asked of the child to accomplish his or her task of the tutor will be analyzed.

A break in parental interaction does not affect the temporal dependency of infant social engagement, but disrupts non-social engagement

Infant looking patterns during interaction offer an early window into social and nonsocial engagement. Recent evidence indicates that infant looks exhibit temporal dependency-one look duration predicts the next look duration. It is unknown, however, whether temporal dependency emerges as infants structure their own looking or whether it is influenced by interaction. We examined whether a perturbation of social interaction affected temporal dependency. Using the Face-to-Face/Still-Face procedure, we compared temporal dependency during parental interaction (the Face-to-Face & Reunion episodes) to parental non-responsiveness (the Still-Face episode). Overall, the durations of successive infant looks were predictable; past behavior constrained current behavior. The duration of one look at the parent (Face Look) predicted the duration of the next Face Look. Likewise, the duration of a look at any place that was not the parent's face (Away Look) predicted the duration of the next Away Look. The temporal dependency of Face Looks (social engagement) was unaffected by the Still-Face perturbation, but the temporal dependency of Away Looks (nonsocial engagement) declined during the Still-Face. Infant temporal structuring of engagement during social looking is not dependent on parental interaction while the disruption of interaction affects infants' structuring of their own non-social engagement.

Social robots for education: A review

Social robots can be used in education as tutors or peer learners. They have been shown to be effective at increasing cognitive and affective outcomes and have achieved outcomes similar to those of human tutoring on restricted tasks. This is largely because of their physical presence, which traditional learning technologies lack. We review the potential of social robots in education, discuss the technical challenges, and consider how the robot's appearance and behavior affect learning outcomes.

Bodily-Contact Communication Medium Induces Relaxed Mode of Brain Activity While Increasing Its Dynamical Complexity: A Pilot Study

We present the results of the analysis of the effect of a bodily-contact communication medium on the brain activity of the individuals during verbal communication. Our results suggest that the communicated content that is mediated through such a device induces a significant effect on electroencephalogram (EEG) time series of human subjects. Precisely, we find a significant reduction of overall power of the EEG signals of the individuals. This observation that is supported by the analysis of the permutation entropy (PE) of the EEG time series of brain activity of the participants suggests the positive effect of such a medium on the stress relief and the induced sense of relaxation. Additionally, multiscale entropy (MSE) analysis of our data implies that such a medium increases the level of complexity that is exhibited by EEG time series of our participants, thereby suggesting their sustained sense of involvement in their course of communication. These findings that are in accord with the results reported by cognitive neuroscience research suggests that the use of such a medium can be beneficial as a complementary step in treatment of developmental disorders, attentiveness of schoolchildren and early child development, as well as scenarios where intimate physical interaction over distance is desirable (e.g., distance-parenting).

Friendship with a robot: Children's perception of similarity between a robot's physical and virtual embodiment that supports diabetes self-management

OBJECTIVE

The PAL project develops a conversational agent with a physical (robot) and virtual (avatar) embodiment to support diabetes self-management of children ubiquitously. This paper assesses 1) the effect of perceived similarity between robot and avatar on children's' friendship towards the avatar, and 2) the effect of this friendship on usability of a self-management application containing the avatar (a) and children's motivation to play with it (b).

METHODS

During a four-day diabetes camp in the Netherlands, 21 children participated in interactions with both agent embodiments. Questionnaires measured perceived similarity, friendship, motivation to play with the app and its usability.

RESULTS

Children felt stronger friendship towards the physical robot than towards the avatar. The more children perceived the robot and its avatar as the same agency, the stronger their friendship with the avatar was. The stronger their friendship with the avatar, the more they were motivated to play with the app and the higher the app scored on usability.

CONCLUSION

The combination of physical and virtual embodiments seems to provide a unique opportunity for building ubiquitous long-term child-agent friendships.

PRACTICE IMPLICATIONS

an avatar complementing a physical robot in health care could increase children's motivation and adherence to use self-management support systems.

Children's Imaginaries of Human-Robot Interaction in Healthcare

This paper analyzes children’s imaginaries of Human-Robots Interaction (HRI) in the context of social robots in healthcare, and it explores ethical and social issues when designing a social robot for a children’s hospital. Based on approaches that emphasize the reciprocal relationship between society and technology, the analytical force of imaginaries lies in their capacity to be embedded in practices and interactions as well as to affect the construction and applications of surrounding technologies. The study is based on a participatory process carried out with six-year-old children for the design of a robot. Imaginaries of HRI are analyzed from a care-centered approach focusing on children’s values and practices as related to their representation of care. The conceptualization of HRI as an assemblage of interactions, the prospective bidirectional care relationships with robots, and the engagement with the robot as an entity of multiple potential robots are the major findings of this study. The study shows the potential of studying imaginaries of HRI, and it concludes that their integration in the final design of robots is a way of including ethical values in it.