"Social" robots are psychological agents for infants: a test of gaze following

Social robots in research on social and cognitive development in infants and toddlers: A scoping review

There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and cognitive development in infants and toddlers aged between 2 and 35 months. Moreover, it aims to identify the research focus, findings, and reported gaps and challenges when using robots in research. We included empirical studies published between 1990 and May 29, 2023. We searched for literature in PsychINFO, ERIC, Web of Science, and PsyArXiv. Twenty-nine studies met the inclusion criteria and were mapped using the scoping review method. Our findings reveal that most studies were quantitative, with experimental designs conducted in a laboratory setting where children were exposed to physically present or virtual robots in a one-to-one situation. We found that robots were used to investigate four main concepts: animacy concept, action understanding, imitation, and early conversational skills. Many studies focused on whether young children regard robots as agents or social partners. The studies demonstrated that young children could learn from and understand social robots in some situations but not always. For instance, children's understanding of social robots was often facilitated by robots that behaved interactively and contingently. This scoping review highlights the need to design social robots that can engage in interactive and contingent social behaviors for early developmental research.

Children's anthropomorphism of inanimate agents

This review article examines the extant literature on animism and anthropomorphism in infants and young children. A substantial body of work indicates that both infants and young children have a broad concept of what constitutes a sentient agent and react to inanimate objects as they do to people in the same context. The literature has also revealed a developmental pattern in which anthropomorphism decreases with age, but social robots appear to be an exception to this pattern. Additionally, the review shows that children attribute psychological properties to social robots less so than people but still anthropomorphize them. Importantly, some research suggests that anthropomorphism of social robots is dependent upon their morphology and human-like behaviors. The extent to which children anthropomorphize robots is dependent on their exposure to them and the presence of human-like features. Based on the existing literature, we conclude that in infancy, a large range of inanimate objects (e.g., boxes, geometric figures) that display animate motion patterns trigger the same behaviors observed in child-adult interactions, suggesting some implicit form of anthropomorphism. The review concludes that additional research is needed to understand what infants and children judge as social agents and how the perception of inanimate agents changes over the lifespan. As exposure to robots and virtual assistants increases, future research must focus on better understanding the full impact that regular interactions with such partners will have on children's anthropomorphizing. This article is categorized under: Psychology > Learning Cognitive Biology > Cognitive Development Computer Science and Robotics > Robotics.

Do preschoolers trust a competent robot pointer?

How young children learn from different informants has been widely studied. However, most studies investigate how children learn verbally conveyed information. Furthermore, most studies investigate how children learn from humans. This study sought to investigate how 3-year-old children learn from, and come to trust, a competent robot versus an incompetent human when competency is established using a pointing paradigm. During an induction phase, a robot informant pointed at a toy inside a transparent box, whereas a human pointed at an empty box. During the test phase, both agents pointed at opaque boxes. We found that young children asked the robot for help to locate a hidden toy more than the human (ask questions) and correctly identified the robot to be accurate (judgment questions). However, children equally endorsed the locations pointed at by both the robot and the human (endorse questions). This suggests that 3-year-olds are sensitive to the epistemic characteristics of the informant even when its displayed social properties are minimal.

Ethical Considerations in Child-Robot Interactions

Social robots hold promise in augmenting education, rehabilitative care, and leisure activities for children. Despite findings suggesting various benefits of social robot use in schools, clinics, and homes, stakeholders have voiced concerns about the potential social and emotional effects of children engaging in long-term interactions with robots. Given the challenges of conducting large long-term studies of child-robot interaction (CRI), little is known about the impact of CRI on children's socio-emotional development. Here we summarize the literature on predictions and expectations of teachers, parents, therapists, and children regarding the effects of CRI on children's socio-emotional functioning and skill building. We then highlight the limited body of empirical research examining how CRI affects children's social behavior and emotional expression, and we provide a summary of available questionnaires for measuring socio-emotional constructs relevant to CRI. We conclude with design recommendations for research studies aimed at better understanding the effects of CRI, before social robots become more ubiquitous. This review is relevant to researchers, educators, roboticists, and clinicians interested in designing and using social robots with developmental populations.

Systematic Review of Affective Computing Techniques for Infant Robot Interaction

Research studies on social robotics and human-robot interaction have gained insights into factors that influence people’s perceptions and behaviors towards robots. However, adults’ perceptions of robots may differ significantly from those of infants. Consequently, extending this knowledge also to infants’ attitudes toward robots is a growing field of research. Indeed, infant-robot interaction (IRI) is emerging as a critical and necessary area of research as robots are increasingly used in social environments, such as caring for infants with all types of disabilities, companionship, and education. Although studies have been conducted on the ability of robots to positively engage infants, little is known about the infants’ affective state when interacting with a robot. In this systematic review, technologies for infant affective state recognition relevant to IRI applications are presented and surveyed. Indeed, adapting techniques currently employed for infant’s emotion recognition to the field of IRI results to be a complex task, since it requires timely response while not interfering with the infant’s behavior. Those aspects have a crucial impact on the selection of the emotion recognition techniques and the related metrics to be used for this purpose. Therefore, this review is intended to shed light on the advantages and the current research challenges of the infants’ affective state recognition approaches in the IRI field, elucidates a roadmap for their use in forthcoming studies as well as potentially provide support to future developments of emotion-aware robots.

Preschoolers' anthropomorphizing of robots: Do human-like properties matter?

Prior work has yielded contradicting evidence regarding the age at which children consistently and correctly categorize things as living or non-living. The present study tested children's animacy judgments about robots with a Naïve Biology task. In the Naïve Biology task, 3- and 5-year-olds were asked if robots, animals, or artifacts possessed mechanical or biological internal parts. To gauge how much children anthropomorphize robots in comparison to animals and artifacts, children also responded to a set of interview questions. To examine the role of morphology, two robots were used: a humanoid robot (Nao) and a non-humanoid robot (Dash). To investigate the role of dynamic characteristics, children saw one robot behave in a goal-directed manner (i.e., moving towards a ball) and one robot exhibit non-goal-directed behavior (i.e., moving away from a ball). Children of both age groups correctly attributed biological insides to the animal and mechanical insides to the artifact. However, 3-year-olds seemed confused about what belonged inside both robots and assigned biological and mechanical insides equally. In contrast, 5-year-olds correctly assigned mechanical insides to both robots, regardless of the robot's morphology or goal-directedness. Regarding the Animacy Interview, 3-year-olds performed at chance level when asked about the animacy of robots, animals, and artifacts. In contrast, 5-year-olds correctly attributed animacy to animals and accurately refrained from anthropomorphizing artifacts and the non-humanoid robot Dash. However, 5-year-olds performed at chance for Nao, suggesting they may be confused about the psychological properties of a human-looking robot. Taken together, these findings reveal a developmental transition during the preschool years in the attribution of biological and psychological properties to social robot.

Infants’ Prediction of Humanoid Robot’s Goal-Directed Action

Several studies have shown that infants anticipate human goal-directed actions, but not robot’s ones. However, the studies focusing on the robot goal-directed actions have mainly analyzed the effect of mechanical arms on infant’s attention. To date, the prediction of goal-directed actions in infants has not yet been studied when the agent is a humanoid robot. Given this lack of evidence in infancy research, the present study aims at analyzing infants’ action anticipation of both a human’s and a humanoid robot’s goal-directed action. Data were acquired on thirty 17-month-old infants, watching four video clips, where either a human or a humanoid robot performed a goal-directed action, i.e. reaching a target. Infants looking behavior was measured through the eye-tracking technique. The results showed that infants anticipated the goal-directed action of both the human and the robot and there were no differences in the anticipatory gaze behavior between the two agents. Furthermore, the findings indicated different attentional patterns for the human and the robot, showing a greater attention paid to the robot's face than the human’s face. Overall, the results suggest that 17-month-old infants may infer also humanoid robot’ underlying action goals.

Physiological arousal explains infant gaze following in various social contexts

Gaze following (GF) is fundamental to central aspects of human sociocognitive development, such as acquiring language and cultural learning. Studies have shown that infant GF is not a simple reflexive orientation to an adult's eye movement. By contrast, infants adaptively modulate GF behaviour depending on the social context. However, arguably, the neurophysiological mechanisms underlying contextual modulation of GF remain somewhat unexplored. In this study, we tested the proposition about whether the contextual modulation of infant GF is mediated by the infant's heart rate (HR), which indicates the infant's physiological arousal. Forty-one 6- to 9-month-old infants participated in this study, and infants observed either a reliable face, which looked towards the location of an object, or an unreliable face, which looked away from the location of an object. Thereafter, the infants watched a video of the same model making eye contact or not making any ostensive signals, before shifting their gaze towards one of the two objects. We revealed that reliability and eye contact acted independently to increase HR, which then fully mediates the effects of these social cues on the frequency of GF. Results suggest that each social cue independently enhances physiological arousal, which then accumulatively predicts the likelihood of infant GF behaviour.

Physiological arousal explains infant gaze following in various social contexts

Gaze following (GF) is fundamental to central aspects of human sociocognitive development, such as acquiring language and cultural learning. Studies have shown that infant GF is not a simple reflexive orientation to an adult's eye movement. By contrast, infants adaptively modulate GF behaviour depending on the social context. However, arguably, the neurophysiological mechanisms underlying contextual modulation of GF remain somewhat unexplored. In this study, we tested the proposition about whether the contextual modulation of infant GF is mediated by the infant's heart rate (HR), which indicates the infant's physiological arousal. Forty-one 6- to 9-month-old infants participated in this study, and infants observed either a reliable face, which looked towards the location of an object, or an unreliable face, which looked away from the location of an object. Thereafter, the infants watched a video of the same model making eye contact or not making any ostensive signals, before shifting their gaze towards one of the two objects. We revealed that reliability and eye contact acted independently to increase HR, which then fully mediates the effects of these social cues on the frequency of GF. Results suggest that each social cue independently enhances physiological arousal, which then accumulatively predicts the likelihood of infant GF behaviour.

Spontaneous perspective taking toward robots: The unique impact of humanlike appearance

As robots rapidly enter society, how does human social cognition respond to their novel presence? Focusing on one foundational social-cognitive capacity-visual perspective taking-seven studies reveal that people spontaneously adopt a robot's unique perspective and do so with patterns of variation that mirror perspective taking toward humans. As they do with humans, people take a robot's visual perspective when it displays goal-directed actions. Moreover, perspective taking is absent when the agent lacks human appearance, increases when the agent looks highly humanlike, and persists even when the humanlike agent is perceived as eerie or as obviously lacking a mind. These results suggest that visual perspective taking toward robots is consistent with a "mere appearance hypothesis"-a form of stimulus generalization based on humanlike appearance-rather than following an "uncanny valley" pattern or arising from mind perception. Robots' superficial human resemblance may trigger and modulate social-cognitive responses in human observers originally developed for human interaction.

Anthropomorphizing Technology: A Conceptual Review of Anthropomorphism Research and How it Relates to Children's Engagements with Digital Voice Assistants

‘Anthropomorphism’ is a popular term in the literature on human-technology engagements, in general, and child-technology engagements, in particular. But what does it really mean to ‘anthropomorphize’ something in today’s world? This conceptual review article, addressed to researchers interested in anthropomorphism and adjacent areas, reviews contemporary anthropomorphism research, and it offers a critical perspective on how anthropomorphism research relates to today’s children who grow up amid increasingly intelligent and omnipresent technologies, particularly digital voice assistants (e.g., Alexa, Google Assistant, Siri). First, the article reviews a comprehensive body of quantitative as well as qualitative anthropomorphism research and considers it within three different research perspectives: descriptive, normative and explanatory. Following a brief excursus on philosophical pragmatism, the article then discusses each research perspective from a pragmatistic viewpoint, with a special emphasis on child-technology and child-voice-assistant engagements, and it also challenges some popular notions in the literature. These notions include descriptive ‘as if’ parallels (e.g., child behaves ‘as if’ Alexa was a friend), or normative assumptions that human-human engagements are generally superior to human-technology engagements. Instead, the article reviews different examples from the literature suggesting the nature of anthropomorphism may change as humans’ experiential understandings of humanness change, and this may particularly apply to today’s children as their social cognition develops in interaction with technological entities which are increasingly characterized by unprecedented combinations of human and non-human qualities.

Teachers' visual processing of children's off-task behaviors in class: A comparison between teachers and student teachers

As teachers are responsible for responding instantaneously to students' statements and actions, the progress of the class, and their teaching purpose, they need to be able to engage in responsive teaching. Teachers obtain information about students' learning by observing them in the classroom, and subsequently make instructional decisions based on this information. Teachers need to be sensitive to student behaviors and respond accordingly, because there are students who follow the teacher's instructions and those who do not in every classroom. Skilled teachers may distribute their gaze over the entire class and discover off-task behaviors. So how does a teacher's visual processing and noticing ability develop? It is important to clarify this process for both experienced teachers and student teachers. Therefore, the purpose of this study was to investigate whether there is a difference in visual processing and the ability to notice off-task behaviors in class between teachers and student teachers through gaze analysis. Using an eye tracking device, 76 teachers and 147 student teachers were asked to watch a video, and gaze measurements were collected. In the video, students exhibiting off-task behaviors in class were prompted by their classroom teacher to participate in the lesson. After the video, the participants were asked if they could identify the students who had displayed off-task behaviors and whom the teachers had warned. The results showed that teachers gazed at students engaging in off-task behaviors in class more often and noticed them at a higher rate than student teachers did. These results may be attributed to differences in the experiences of visual processing of relevant information in the classroom between teachers and student teachers. Thus, the findings on teachers' visual processing by direct measurement of gaze will be able to contribute to teachers' development.

Constrained Choice: Children's and Adults' Attribution of Choice to a Humanoid Robot

Young children, like adults, understand that human agents can flexibly choose different actions in different contexts, and they evaluate these agents based on such choices. However, little is known about children's tendencies to attribute the capacity to choose to robots, despite increased contact with robotic agents. In this paper, we compare 5- to 7-year-old children's and adults' attributions of free choice to a robot and to a human child by using a series of tasks measuring agency attribution, action prediction, and choice attribution. In morally neutral scenarios, children ascribed similar levels of free choice to the robot and the human, while adults were more likely to ascribe free choice to the human. For morally relevant scenarios, however, both age groups considered the robot's actions to be more constrained than the human's actions. These findings demonstrate that children and adults hold a nuanced understanding of free choice that is sensitive to both the agent type and constraints within a given scenario.

Co-dependency of exchanged behaviors is a cue for agency attribution in 10-month-olds

Goal-directed social interactions (whether instrumental or communicative) involve co-dependent, partially predictable actions of interacting agents as social goals cannot be achieved by continuously exchanging the same, perfectly predictable, or completely random behaviors. We investigated whether 10-month-olds are sensitive to the co-dependence and degree of predictability in an interactive context where unfamiliar entities exchanged either perfectly predictable (identical), partially predictable (co-dependent), or non-predictable (random) signal sequences. We found that when—following the interactive exchanges—one of the entities turned in the direction of one of two lateral target objects, infants looked more at the indicated referent, but only in the partially predictable signals condition. This shows that infants attributed agency to the orienting entity and interpreted its turning action as a referential object-directed action. The present findings suggest that the co-dependency and partial predictability of exchanged behaviors can serve as an abstract structural cue to attribute intentional agency and recognize goal-directed social interactions.

The Perceptual Belief Problem

The explainability of robotic systems depends on people’s ability to reliably attribute perceptual beliefs to robots, i.e., what robots know (or believe) about objects and events in the world based on their perception. However, the perceptual systems of robots are not necessarily well understood by the majority of people interacting with them. In this article, we explain why this is a significant, difficult, and unique problem in social robotics. The inability to judge what a robot knows (and does not know) about the physical environment it shares with people gives rise to a host of communicative and interactive issues, including difficulties to communicate about objects or adapt to events in the environment. The challenge faced by social robotics researchers or designers who want to facilitate appropriate attributions of perceptual beliefs to robots is to shape human–robot interactions so that people understand what robots know about objects and events in the environment. To meet this challenge, we argue, it is necessary to advance our knowledge of when and why people form incorrect or inadequate mental models of robots’ perceptual and cognitive mechanisms. We outline a general approach to studying this empirically and discuss potential solutions to the problem.

Does agency matter? Neural processing of robotic movements in 4- and 8-year olds

Despite the increase in interactions between children and robots, our understanding of children's neural processing of robotic movements is limited. The current study theorized that motor resonance hinges on the agency of an actor: its ability to perform actions volitionally. As one of the first studies with a cross-sectional sample of preschoolers and older children and with a specific focus on robotic action (rather than abstract non-human action), the current study investigated whether the perceived agency of a robot moderated children's motor resonance for robotic movements, and whether this changed with age. Motor resonance was measured using electroencephalography (EEG) by assessing mu power while 4 and 8-year-olds observed actions performed by agentic versus non-agentic robots and humans. Results show that older children resonated more strongly with non-agentic than agentic robotic or human movement, while no such differences were found for preschoolers. This outcome is discussed in terms of a predictive coding account of motor resonance. Importantly, these findings contribute to the existing set of studies on this topic by showing that, while keeping all kinematic information constant, there is a clear developmental difference in how children process robotic movement depending on the level of agency of a robot.

Are social robots ready yet to be used in care and therapy of autism spectrum disorder: A systematic review of randomized controlled trials

Autism is a neurodevelopmental disorder that affects the everyday life of people who have this lifelong condition. Robots hold great promise for uplifting therapy and care of the affected population. We searched Scopus, Medline, ScienceDirect, Web of Science, and PubMed databases for randomized controlled trials that had evaluated robot use in the therapy of people with autism, to see how effective social robots have been incorporated in autism care. Out of 240 papers initially identified, 19 satisfied the inclusion criteria and were fully evaluated. Overall, 10 different robots were utilized in the trials, out of which, four were non-humanoids. The number of papers with positive results for using robots on the main and secondary parameters was 11 and 5, respectively. Three papers reported that robot groups did not achieve better results than others. Robots in the papers included here were mainly added as the "entertainment agent" to elicit greater engagement from the participants, which is understandable, as robots at this stage might not be ready yet to deliver high-end care.

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.

The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot

Several studies have shown that the human gaze, but not the robot gaze, has significant effects on infant social cognition and facilitate social engagement. The present study investigates early understanding of the referential nature of gaze by comparing-through the eye-tracking technique-infants' response to human and robot's gaze. Data were acquired on thirty-two 17-month-old infants, watching four video clips, where either a human or a humanoid robot performed an action on a target. The agent's gaze was either turned to the target (congruent) or opposite to it (incongruent). The results generally showed that, independent of the agent, the infants attended longer at the face area compared to the hand and target. Additionally, the effect of referential gaze on infants' attention to the target was greater when infants watched the human compared to the robot's action. These results suggest the presence, in infants, of two distinct levels of gaze-following mechanisms: one recognizing the other as a potential interactive partner, the second recognizing partner's agency. In this study, infants recognized the robot as a potential interactive partner, whereas ascribed agency more readily to the human, thus suggesting that the process of generalizability of gazing behaviour to non-humans is not immediate.

Analysis of Facial Information for Healthcare Applications: A Survey on Computer Vision-Based Approaches

This paper gives an overview of the cutting-edge approaches that perform facial cue analysis in the healthcare area. The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g., the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.

Young Children's Indiscriminate Helping Behavior Toward a Humanoid Robot

Young children help others in a range of situations, relatively indiscriminate of the characteristics of those they help. Recent results have suggested that young children's helping behavior extends even to humanoid robots. However, it has been unclear how characteristics of robots would influence children's helping behavior. Considering previous findings suggesting that certain robot features influence adults' perception of and their behavior toward robots, the question arises of whether young children's behavior and perception would follow the same principles. The current study investigated whether two key characteristics of a humanoid robot (animate autonomy and friendly expressiveness) would affect children's instrumental helping behavior and their perception of the robot as an animate being. Eighty-two 3-year-old children participated in one of four experimental conditions manipulating a robot's ostensible animate autonomy (high/low) and friendly expressiveness (friendly/neutral). Helping was assessed in an out-of-reach task and animacy ratings were assessed in a post-test interview. Results suggested that both children's helping behavior, as well as their perception of the robot as animate, were unaffected by the robot's characteristics. The findings indicate that young children's helping behavior extends largely indiscriminately across two important characteristics. These results increase our understanding of the development of children's altruistic behavior and animate-inanimate distinctions. Our findings also raise important ethical questions for the field of child-robot interaction.

Variability of signal sequences in turn-taking exchanges induces agency attribution in 10.5-mo-olds

Infants' sensitivity to contingent reactivity as an indicator of intentional agency has been demonstrated by numerous referential gaze-following studies. Here we propose that variability of signal sequences in a turn-taking exchange provides an informative cue for infants to recognize interactions that may involve communicative information transfer between agents. Our experiment demonstrates that based on the abstract structural cue of variability of exchanged signal sequences, 10.5-mo-olds gaze-followed an entity's subsequent object-orienting action to fixate the same object. This gaze-following effect did not depend on the specific acoustic features of the sound signals produced. However, no orientation following to target was induced when the exchanged signal sequences were identical, or when only a single entity produced the variable sound sequences. These results demonstrate infants' early sensitivity to detect signal variability in turn-taking interactions as a relevant feature of communicative information transfer, which induces them to attribute intentional agency and communicative abilities to the participating entities. However, when no variability was present in the exchanged signals, or when the variable signal sequences were produced by a single entity alone, infants showed no evidence of attributing agency. In sum, we argue that perceiving contingent turn-taking exchange of variable signal sequences induce 10.5-mo-old preverbal infants to recognize such interactions as potentially involving communicative information transmission and attribute agency to the participating entities even if both the entities and the signals they produce are unfamiliar to them.

Between living and nonliving: Young children's animacy judgments and reasoning about humanoid robots

Humanoid robots will become part of our everyday lives. They have biologically inspired features and psychologically complex properties. How will children interpret these ambiguous objects, discriminating between living and nonliving kinds? Do the biologically and psychologically inspired characteristics affect children’s understanding of the robots? How firm is the distinction that children make between living and nonliving objects? To address these questions, 120 children ranging three to five years initially viewed video clips that depicted humanoid robots interacting with a human experimenter on two different dimensions (mobility and, psychologically contingent behavior). The subjects then answered simple questions that probed their animacy judgments and property projections about the robot. The results showed that children’s animacy assessments about humanoid robots differed by age. When judging the robot’s life status, its mobility was important for four-year-olds and, the psychological contingency for five-year-olds. In terms of the robot’s reasoning abilities, the majority of four-year-old children clearly understood biological properties, regardless of the robots’ features. However, when reasoning about psychological properties, even five-year-olds occasionally relied on robots’ features such as their contingent behaviors. Moreover, the children attributed some but not all animate properties to the robots. Although rent findings show that children possess naïve theories, they do not seem to have a consistent and logical theory of “aliveness,” and they apparently develop the concept of a robot by acquiring knowledge about how this boundary object differs from living entities.

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.

Theory of Mind and Humanoid Robots From a Lifespan Perspective

Abstract. This review focuses on some relevant issues concerning the relationship between theory of mind (ToM) and humanoid robots. Humanoid robots are employed in different everyday-life contexts, so it seems relevant to question whether the relationships between human beings and humanoids can be characterized by a mode of interaction typical of the relationships between human beings, that is, the attribution of mental states. Because ToM development continuously undergoes changes from early childhood to late adulthood, we adopted a lifespan perspective. We analyzed contributions from the literature by organizing them around the partition between “mental states and actions” and “human-like features.” Finally, we considered how studying human–robot interaction, within a ToM context, can contribute to our understanding of the intersubjective nature of this interaction.

Human infants' understanding of social imitation: Inferences of affiliation from third party observations

Imitation is ubiquitous in positive social interactions. For adult and child observers, it also supports inferences about the participants in such interactions and their social relationships, but the origins of these inferences are obscure. Do infants attach social significance to this form of interaction? Here we test 4- to 5.5-month-old infants' interpretation of imitation, asking if the imitative interactions they observe support inferences of social affiliation, across 10 experimental conditions that varied the modality of the imitation (movement vs. sound), the roles of specific characters (imitators vs. targets), the number of characters in the displays (3 vs. 5), and the number of parties initiating affiliative test events (1 vs. 2). These experiments, together with one experiment conducted with 12-month-old infants, yielded three main findings. First, infants expect that characters who engaged in imitation will approach and affiliate with the characters whom they imitated. Second, infants show no evidence of expecting that characters who were targets of imitation will approach and affiliate with their imitators. Third, analyzing imitative interactions is difficult for young infants, whose expectations vary in strength depending on the number of characters to be tracked and the number of affiliative actors to be compared. These findings have implications for our understanding of social imitation, and they provide methods for advancing understanding of other aspects of early social cognitive development.

Generation of Bystander Robot Actions Based on Analysis of Relative Probability of Human Actions

This paper describes a method of rule extraction for generation of appropriate actions by a robot in a multiparty conversation based on the relative probability of human actions in a similar situation. The proposed method was applied to a dataset collected from multiparty interactions between two robots and one human subject who took on the role of supporting one robot. By computing the relative occurrence probabilities of human actions after the execution of the robots’ actions, twenty rules describing human behavior in such a role were identified. To evaluate the rules, the human role was filled by a new bystander robot and other subjects were asked to report their impressions of video clips in which the bystander robot acted or did not act in accordance with the rules. The reported impressions and a quantitative analysis of the rules suggest that the behavior of listening and the supporting role that the subjects play can be reproduced by a bystander robot acting in accordance with the rules identified by the proposed method.

Robot education peers in a situated primary school study: Personalisation promotes child learning

The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated. Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed. Two autonomous robots were embedded within two matched classrooms of a primary school for a continuous two week period without experimenter supervision to act as learning companions for the children for familiar and novel subjects. Results suggest that while children in both personalised and non-personalised conditions learned, there was increased child learning of a novel subject exhibited when interacting with a robot that personalised its behaviours, with indications that this benefit extended to other class-based performance. Additional evidence was obtained suggesting that there is increased acceptance of the personalised robot peer over a non-personalised version. These results provide the first evidence in support of peer-robot behavioural personalisation having a positive influence on learning when embedded in a learning environment for an extended period of time.

Do infants perceive the social robot Keepon as a communicative partner?

This study investigates if infants perceive an unfamiliar agent, such as the robot Keepon, as a social agent after observing an interaction between the robot and a human adult. 23 infants, aged 9-17 month, were exposed, in a first phase, to either a contingent interaction between the active robot and an active human adult, or to an interaction between an active human adult and the non-active robot, followed by a second phase, in which infants were offered the opportunity to initiate a turn-taking interaction with Keepon. The measured variables were: (1) the number of social initiations the infant directed toward the robot, and (2) the number of anticipatory orientations of attention to the agent that follows in the conversation. The results indicate a significant higher level of initiations in the interactive robot condition compared to the non-active robot condition, while the difference between the frequencies of anticipations of turn-taking behaviors was not significant.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

Common and distinct networks for self-referential and social stimulus processing in the human brain

Self-referential processing is a complex cognitive function, involving a set of implicit and explicit processes, complicating investigation of its distinct neural signature. The present study explores the functional overlap and dissociability of self-referential and social stimulus processing. We combined an established paradigm for explicit self-referential processing with an implicit social stimulus processing paradigm in one fMRI experiment to determine the neural effects of self-relatedness and social processing within one study. Overlapping activations were found in the orbitofrontal cortex and in the intermediate part of the precuneus. Stimuli judged as self-referential specifically activated the posterior cingulate cortex, the ventral medial prefrontal cortex, extending into anterior cingulate cortex and orbitofrontal cortex, the dorsal medial prefrontal cortex, the ventral and dorsal lateral prefrontal cortex, the left inferior temporal gyrus, and occipital cortex. Social processing specifically involved the posterior precuneus and bilateral temporo-parietal junction. Taken together, our data show, not only, first, common networks for both processes in the medial prefrontal and the medial parietal cortex, but also, second, functional differentiations for self-referential processing versus social processing: an anterior-posterior gradient for social processing and self-referential processing within the medial parietal cortex and specific activations for self-referential processing in the medial and lateral prefrontal cortex and for social processing in the temporo-parietal junction.

Model-observer similarity, error modeling and social learning in rhesus macaques

Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals’ own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a ‘stimulus-enhancing’ human actively drawing the animal’s attention to one object of the pair without actually performing the task, and a ‘monkey-like’ human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The ‘monkey-like’ human model improved the animals’ subsequent object discrimination learning as much as a conspecific did, whereas the ‘stimulus-enhancing’ human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and ‘monkey-like’ models, while exacerbating the adverse effect of the ‘stimulus-enhancing’ model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models’ influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as ‘like-me’ and that, once this condition is fulfilled, any agent can become an effective model.

Preverbal infants expect members of social groups to act alike

The short ontogenetic time courses of conformity and stereotyping, both evident in the preschool years, point to the possibility that a central component of human social cognition is an early developing expectation that social group members will engage in common behaviors. Across a series of experiments, we show that by 7 months of age preverbal infants differentiate between actions by individuals that are and are not consistent with the actions of their social group members. Infants responded to group-inconsistent actions only in a social context: they failed to distinguish the same behavioral differences when presented with collections of nonsocial agents or inanimate objects. These results suggest that infants expect social group membership and behavior to covary, before extensive intergroup experience or linguistic input. This expectation is consistent with the socially motivated imitation and stereotyping evident in toddlers and preschoolers, and may play a role in the early emergence of one or both of these aspects of social behavior and cognition.

An Integrated Framework for Robust Human-Robot Interaction

Developments in sensor technology and sensory input processing algorithms have enabled the use of mobile robots in real-world domains. As they are increasingly deployed to interact with humans in our homes and offices, robots need the ability to operate autonomously based on sensory cues and high-level feedback from non-expert human participants. Towards this objective, this chapter describes an integrated framework that jointly addresses the learning, adaptation, and interaction challenges associated with robust human-robot interaction in real-world application domains. The novel probabilistic framework consists of: (a) a bootstrap learning algorithm that enables a robot to learn layered graphical models of environmental objects and adapt to unforeseen dynamic changes; (b) a hierarchical planning algorithm based on partially observable Markov decision processes (POMDPs) that enables the robot to reliably and efficiently tailor learning, sensing, and processing to the task at hand; and (c) an augmented reinforcement learning algorithm that enables the robot to acquire limited high-level feedback from non-expert human participants, and merge human feedback with the information extracted from sensory cues. Instances of these algorithms are implemented and fully evaluated on mobile robots and in simulated domains using vision as the primary source of information in conjunction with range data and simplistic verbal inputs. Furthermore, a strategy is outlined to integrate these components to achieve robust human-robot interaction in real-world application domains.

What are you or who are you? The emergence of social interaction between dog and an unidentified moving object (UMO)

Robots offer new possibilities for investigating animal social behaviour. This method enhances controllability and reproducibility of experimental techniques, and it allows also the experimental separation of the effects of bodily appearance (embodiment) and behaviour. In the present study we examined dogs' interactive behaviour in a problem solving task (in which the dog has no access to the food) with three different social partners, two of which were robots and the third a human behaving in a robot-like manner. The Mechanical UMO (Unidentified Moving Object) and the Mechanical Human differed only in their embodiment, but showed similar behaviour toward the dog. In contrast, the Social UMO was interactive, showed contingent responsiveness and goal-directed behaviour and moved along varied routes. The dogs showed shorter looking and touching duration, but increased gaze alternation toward the Mechanical Human than to the Mechanical UMO. This suggests that dogs' interactive behaviour may have been affected by previous experience with typical humans. We found that dogs also looked longer and showed more gaze alternations between the food and the Social UMO compared to the Mechanical UMO. These results suggest that dogs form expectations about an unfamiliar moving object within a short period of time and they recognise some social aspects of UMOs' behaviour. This is the first evidence that interactive behaviour of a robot is important for evoking dogs' social responsiveness.