Dyadic body competence predicts movement synchrony during the mirror game.

The process of synchronizing our body movements with others is known to enhance rapport, affect, and prosociality. Furthermore, emerging evidence suggests that synchronizing activities may enhance cognitive performance. Unknown, by contrast, is the extent to which people's individual traits and experiences influence their ability to achieve and maintain movement synchrony with another person, which is key for unlocking the social and affective benefits of movement synchrony. Here, we take a dyad-centered approach to gain a deeper understanding of the role of embodiment in achieving and maintaining movement synchrony. Using existing data, we explored the relationship between body competence and body perception scores at the level of the dyad, and the dyad's movement synchrony and complexity while playing a 2.5-min movement mirroring game. The data revealed that dyadic body competence scores positively correlate with movement synchrony, but not complexity, and that dyadic body perception scores are not associated with movement synchrony or complexity. Movement synchrony was greater when the more experienced member of the dyad was responsible for copying movements. Finally, movement synchrony and complexity were stable across the duration of the mirror game. These findings show that movement synchrony is sensitive to the composition of the dyad involved, specifically the dyad's embodiment, illuminating the value of dyadic approaches to understanding body movements in social contexts.

Beyond human-likeness: Socialness is more influential when attributing mental states to robots.

We sought to replicate and expand previous work showing that the more human-like a robot appears, the more willing people are to attribute mind-like capabilities and socially engage with it. Forty-two participants played games against a human, a humanoid robot, a mechanoid robot, and a computer algorithm while undergoing functional neuroimaging. We confirmed that the more human-like the agent, the more participants attributed a mind to them. However, exploratory analyses revealed that the perceived socialness of an agent appeared to be as, if not more, important for mind attribution. Our findings suggest top-down knowledge cues may be equally or possibly more influential than bottom-up stimulus cues when exploring mind attribution in non-human agents. While further work is now required to test this hypothesis directly, these preliminary findings hold important implications for robotic design and to understand and test the flexibility of human social cognition when people engage with artificial agents.

Evaluations of dyadic synchrony: observers' traits influence estimation and enjoyment of synchrony in mirror-game movements.

While evidence abounds that motor synchrony is a powerful form of 'social glue' for those involved, we have yet to understand how observers perceive motor synchrony: can observers estimate the degree of synchrony accurately? Is synchrony aesthetically pleasing? In two preregistered experiments (n = 161 each), we assess how accurately observers can estimate the degree of synchrony in dyads playing the mirror game, and how much observers enjoy watching these movements. We further assess whether accuracy and enjoyment are influenced by individual differences in self-reported embodied expertise (ability to reproduce movements, body awareness, body competence), psychosocial resources (extraversion, self-esteem), or social competencies (empathy, autistic traits), while objectively controlling for the degree of measured synchrony and complexity. The data revealed that observers' estimated synchrony with poor accuracy, showing a tendency to underestimate the level of synchrony. Accuracy for low synchrony improved with increasing body competence, while accuracy for high synchrony improved with increasing autistic traits. Observers' enjoyment of dyadic movements correlated positively with the degree of measured synchrony, the predictability of the movements, and the observer's empathy. Furthermore, very low enjoyment was associated with increased body perception. Our findings indicate that accuracy in perceiving synchrony is closely linked to embodiment, while aesthetic evaluations of action hinge on individual differences.

Perceptions of intelligence & sentience shape children's interactions with robot reading companions.

The potential for robots to support education is being increasingly studied and rapidly realised. However, most research evaluating education robots has neglected to examine the fundamental features that make them more or less effective, given the needs and expectations of learners. This study explored how children's perceptions, expectations and experiences are shaped by aesthetic and functional features during interactions with different robot 'reading buddies'. We collected a range of quantitative and qualitative measures of subjective experience before and after children read a book with one of three different robots. An inductive thematic analysis revealed that robots have the potential offer children an engaging and non-judgemental social context to promote reading engagement. This was supported by children's perceptions of robots as being intelligent enough to read, listen and comprehend the story, particularly when they had the capacity to talk. A key challenge in the use of robots for this purpose was the unpredictable nature of robot behaviour, which remains difficult to perfectly control and time using either human operators or autonomous algorithms. Consequently, some children found the robots' responses distracting. We provide recommendations for future research seeking to position seemingly sentient and intelligent robots as an assistive tool within and beyond education settings.

Dancing robots: Social interactions are performed, not depicted.

Clark and Fischer's depiction hypothesis is based on examples of western mimetic art. Yet social robots do not depict social interactions, but instead perform them. Similarly, dance and performance art do not rely on depiction. Kinematics and expressivity are better predictors of dance aesthetics and of effective social interactions. In this way, social robots are more like dancers than actors.

Autonomous social robots are real in the mind's eye of many.

Clark and Fischer's dismissal of extant human-robot interaction research approaches limits opportunities to understand major variables shaping people's engagement with social robots. Instead, this endeavour categorically requires multidisciplinary approaches. We refute the assumption that people cannot (correctly or incorrectly) represent robots as autonomous social agents. This contradicts available empirical evidence, and will become increasingly tenuous as robot automation improves.

The computer, A choreographer? Aesthetic responses to randomly-generated dance choreography by a computer.

Is artificial intelligence (AI) changing our culture or creating its own? With advancements in AI and machine learning, artistic creativity is moving to a brave new world of possibility and complexity, while at the same time posing challenging questions, such as what defines something as art, what is the role of human creativity in an automated world, and do we evaluate artificial art in the same way as art made by humans? Across two pre-registered and statistically powered experiments we shed light on the nature of aesthetic responses toward computer-generated art by investigating observer prejudices against computer-generated dance choreography, and the impact of expertise and pre-conceived beliefs about the origin of artistic creation. Our results provide substantive evidence that an explicit bias exists among dance experts against computer-generated choreography. The mere belief about a dance work's origin biases aesthetic responses toward artworks among both dance experts and dance naïve participants. The implications of the current study serve to inform several disciplines across the arts and sciences including but not limited to empirical aesthetics, artificial intelligence, engineering, robotics, and social cognition and neuroscience. Along with physical form and content of artificial agents and art productions, the viewers' knowledge and attitudes toward artistic AI and artificial agents will need to be taken into consideration for effective human-computer/human-AI interactions.

The McNorm library: creating and validating a new library of emotionally expressive whole body dance movements.

The ability to exchange affective cues with others plays a key role in our ability to create and maintain meaningful social relationships. We express our emotions through a variety of socially salient cues, including facial expressions, the voice, and body movement. While significant advances have been made in our understanding of verbal and facial communication, to date, understanding of the role played by human body movement in our social interactions remains incomplete. To this end, here we describe the creation and validation of a new set of emotionally expressive whole-body dance movement stimuli, named the Motion Capture Norming (McNorm) Library, which was designed to reconcile a number of limitations associated with previous movement stimuli. This library comprises a series of point-light representations of a dancer's movements, which were performed to communicate to observers neutrality, happiness, sadness, anger, and fear. Based on results from two validation experiments, participants could reliably discriminate the intended emotion expressed in the clips in this stimulus set, with accuracy rates up to 60% (chance = 20%). We further explored the impact of dance experience and trait empathy on emotion recognition and found that neither significantly impacted emotion discrimination. As all materials for presenting and analysing this movement library are openly available, we hope this resource will aid other researchers in further exploration of affective communication expressed by human bodily movement.

Mobile fNIRS for exploring inter-brain synchrony across generations and time.

While still relatively rare, longitudinal hyperscanning studies are exceptionally valuable for documenting changes in inter-brain synchrony, which may in turn underpin how behaviors develop and evolve in social settings. The generalizability and ecological validity of this experimental approach hinges on the selected imaging technique being mobile-a requirement met by functional near-infrared spectroscopy (fNIRS). fNIRS has most frequently been used to examine the development of inter-brain synchrony and behavior in child-parent dyads. In this position paper, we contend that dedicating attention to longitudinal and intergenerational hyperscanning stands to benefit the fields of social and cognitive neuroscience more broadly. We argue that this approach is particularly relevant for understanding the neural mechanisms underpinning intergenerational social dynamics, and potentially for benchmarking progress in psychological and social interventions, many of which are situated in intergenerational contexts. In line with our position, we highlight areas of intergenerational research that stand to be enhanced by longitudinal hyperscanning with mobile devices, describe challenges that may arise from measuring across generations in the real world, and offer potential solutions.

Exploring behaviours perceived as important for human-Dog bonding and their translation to a robotic platform.

To facilitate long-term engagement with social robots, emerging evidence suggests that modelling robots on social animals with whom many people form enduring social bonds-specifically, pet dogs-may be useful. However, scientific understanding of the features of pet dogs that are important for establishing and maintaining social bonds remains limited to broad qualities that are liked, as opposed to specific behaviours. To better understand dog behaviours that are perceived as important for facilitating social bonds between owner and pet, we surveyed current dog owners (n = 153) with open-ended questions about their dogs' behaviours. Thematic analysis identified 7 categories of behaviours perceived as important to human-dog bonding, including: 1) attunement, 2) communication, 3) consistency and predictability, 4) physical affection, 5) positivity and enthusiasm, 6) proximity, and 7) shared activities. We consider the feasibility of translating these behaviours into a social robotic platform, and signpost potential barriers moving forward. In addition to providing insight into important behaviours for human-dog bonding, this work provides a springboard for those hoping to implement dog behaviours into animal-like artificial agents designed for social roles.

Human but not robotic gaze facilitates action prediction.

Do people ascribe intentions to humanoid robots as they would to humans or non-human-like animated objects? In six experiments, we compared people's ability to extract non-mentalistic (i.e., where an agent is looking) and mentalistic (i.e., what an agent is looking at; what an agent is going to do) information from gaze and directional cues performed by humans, human-like robots, and a non-human-like object. People were faster to infer the mental content of human agents compared to robotic agents. Furthermore, although the absence of differences in control conditions rules out the use of non-mentalizing strategies, the human-like appearance of non-human agents may engage mentalizing processes to solve the task. Overall, results suggest that human-like robotic actions may be processed differently from humans' and objects' behavior. These findings inform our understanding of the relevance of an object's physical features in triggering mentalizing abilities and its relevance for human-robot interaction.

The role of expertise and culture in visual art appreciation.

Is art appreciation universal? Previous evidence suggests a general preference for representational art over abstract art, and a tendency to like art originating from one's own culture more than another culture (an ingroup bias), modulated by art expertise. However, claims about universality are difficult given that most research has focused on Western populations. Across two pre-registered and statistically powered experiments, we explore the role of culture and art expertise in the aesthetic evaluation of Indian and Western paintings and dance depicting both abstract and representational content, by inviting expert and art-naïve Indian and Western participants to rate stimuli on beauty and liking. Results suggest an ingroup bias (for dance) and a preference for representational art (for paintings) exists, both modulated by art expertise. As predicted, the ingroup bias was present only in art-naïve participants, and the preference for representational art was lower in art experts, but this modulation was present only in Western participants. The current findings have two main implications: (1) they inform and constrain understanding of universality of aesthetic appreciation, cautioning against generalising models of empirical aesthetics to non-western populations and across art forms, (2) they highlight the importance of art experience as a medium to counter prejudices.

Resonance as a Design Strategy for AI and Social Robots.

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human-robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of "sympathetic resonance" as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human-robot interactions.

People's dispositional cooperative tendencies towards robots are unaffected by robots' negative emotional displays in prisoner's dilemma games.

The study explores the impact of robots' emotional displays on people's tendency to cooperate with a robot opponent in prisoner's dilemma games. Participants played iterated prisoner's dilemma games with a non-expressive robot (as a measure of cooperative baseline), followed by an angry, and a sad robot, in turn. Based on the Emotion as Social Information model, we expected participants with higher cooperative predispositions to cooperate less when a robot displayed anger, and cooperate more when the robot displayed sadness. Contrarily, according to this model, participants with lower cooperative predispositions should cooperate more with an angry robot and less with a sad robot. The results of 60 participants failed to support the predictions. Only the participants' cooperative predispositions significantly predicted their cooperative tendencies during gameplay. Participants who cooperated more in the baseline measure also cooperated more with the robots displaying sadness and anger. In exploratory analyses, we found that participants who accurately recognised the robots' sad and angry displays tended to cooperate less with them overall. The study highlights the impact of personal factors in human-robot cooperation, and how these factors might surpass the influence of bottom-up emotional displays by the robots in the present experimental scenario.

Empathy and Schadenfreude in Human-Robot Teams.

Intergroup dynamics shape the ways in which we interact with other people. We feel more empathy towards ingroup members compared to outgroup members, and can even feel pleasure when an outgroup member experiences misfortune, known as schadenfreude. Here, we test the extent to which these intergroup biases emerge during interactions with robots. We measured trial-by-trial fluctuations in emotional reactivity to the outcome of a competitive reaction time game to assess both empathy and schadenfreude in arbitrary human-human and human-robot teams. Across four experiments (total n = 361), we observed a consistent empathy and schadenfreude bias driven by team membership. People felt more empathy towards ingroup members than outgroup members and more schadenfreude towards outgroup members. The existence of an intergroup bias did not depend on the nature of the agent: the same effects were observed for human-human and human-robot teams. People reported similar levels of empathy and schadenfreude towards a human and robot player. The human likeness of the robot did not consistently influence this intergroup bias. In other words, similar empathy and schadenfreude biases were observed for both humanoid and mechanoid robots. For all teams, this bias was influenced by the level of team identification; individuals who identified more with their team showed stronger intergroup empathy and schadenfreude bias. Together, we show that similar intergroup dynamics that shape our interactions with people can also shape interactions with robots. Our results highlight the importance of taking intergroup biases into account when examining social dynamics of human-robot interactions.

Exploring the relationship between anthropomorphism and theory-of-mind in brain and behaviour.

The process of understanding the minds of other people, such as their emotions and intentions, is mimicked when individuals try to understand an artificial mind. The assumption is that anthropomorphism, attributing human-like characteristics to non-human agents and objects, is an analogue to theory-of-mind, the ability to infer mental states of other people. Here, we test to what extent these two constructs formally overlap. Specifically, using a multi-method approach, we test if and how anthropomorphism is related to theory-of-mind using brain (Experiment 1) and behavioural (Experiment 2) measures. In a first exploratory experiment, we examine the relationship between dispositional anthropomorphism and activity within the theory-of-mind brain network (n = 108). Results from a Bayesian regression analysis showed no consistent relationship between dispositional anthropomorphism and activity in regions of the theory-of-mind network. In a follow-up, pre-registered experiment, we explored the relationship between theory-of-mind and situational and dispositional anthropomorphism in more depth. Participants (n = 311) watched a short movie while simultaneously completing situational anthropomorphism and theory-of-mind ratings, as well as measures of dispositional anthropomorphism and general theory-of-mind. Only situational anthropomorphism predicted the ability to understand and predict the behaviour of the film's characters. No relationship between situational or dispositional anthropomorphism and general theory-of-mind was observed. Together, these results suggest that while the constructs of anthropomorphism and theory-of-mind might overlap in certain situations, they remain separate and possibly unrelated at the personality level. These findings point to a possible dissociation between brain and behavioural measures when considering the relationship between theory-of-mind and anthropomorphism.

Dissociating embodiment and emotional reactivity in motor responses to artworks.

Perceiving art is known to elicit motor cortex activation in an observer's brain. This motor activation has often been attributed to a covert approach response associated with the emotional valence of an art piece (emotional reaction hypothesis). However, recent accounts have proposed that aesthetic experiences could be grounded in the motor simulation of actions required to produce an art piece and of the sensorimotor states embedded in its subject (embodied aesthetic hypothesis). Here, we aimed to test these two hypotheses by assessing whether motor facilitation during artwork perception mirrors emotional or motor simulation processes. To this aim, we capitalized on single pulse transcranial magnetic stimulation revealing a two-stage motor coding of emotional body postures: an early, non-specific activation related to emotion processing and a later action-specific activation reflecting motor simulation. We asked art-naïve individuals to rate how much they liked a series of pointillist and brushstroke canvases; photographs of artistic gardens served as control natural stimuli. After an early (150 ms) or a later (300 ms) post-stimulus delay, motor evoked potentials were recorded from wrist-extensor and finger muscles that were more involved in brushstroke- and pointillist-like painting, respectively. Results showed that observing the two canvas styles did not elicit differential motor activation in the early time window for either muscle, not supporting the emotional reaction hypothesis. However, in support of the embodied aesthetic hypothesis, we found in the later time window greater motor activation responses to brushstroke than pointillist canvases for the wrist-extensor, but not for the finger muscle. Furthermore, this muscle-selective facilitation was associated with lower liking ratings of brushstroke canvases and with greater empathy dispositions. These findings support the claim that simulation of the painter's movements is crucial for aesthetic experience, by documenting a link between motor simulation, dispositional empathy, and subjective appreciation in artwork perception.

"Hit the Robot on the Head With This Mallet" - Making a Case for Including More Open Questions in HRI Research.

Researchers continue to devise creative ways to explore the extent to which people perceive robots as social agents, as opposed to objects. One such approach involves asking participants to inflict 'harm' on a robot. Researchers are interested in the length of time between the experimenter issuing the instruction and the participant complying, and propose that relatively long periods of hesitation might reflect empathy for the robot, and perhaps even attribution of human-like qualities, such as agency and sentience. In a recent experiment, we adapted the so-called 'hesitance to hit' paradigm, in which participants were instructed to hit a humanoid robot on the head with a mallet. After standing up to do so (signaling intent to hit the robot), participants were stopped, and then took part in a semi-structured interview to probe their thoughts and feelings during the period of hesitation. Thematic analysis of the responses indicate that hesitation not only reflects perceived socialness, but also other factors including (but not limited to) concerns about cost, mallet disbelief, processing of the task instruction, and the influence of authority. The open-ended, free responses participants provided also offer rich insights into individual differences with regards to anthropomorphism, perceived power imbalances, and feelings of connection toward the robot. In addition to aiding understanding of this measurement technique and related topics regarding socialness attribution to robots, we argue that greater use of open questions can lead to exciting new research questions and interdisciplinary collaborations in the domain of social robotics.

Watch and Learn: The Cognitive Neuroscience of Learning from Others' Actions.

The mirror neuron system has dominated understanding of observational learning from a cognitive neuroscience perspective. Our review highlights the value of observational learning frameworks that integrate a more diverse and distributed set of cognitive and brain systems, including those implicated in sensorimotor transformations, as well as in more general processes such as executive control, reward, and social cognition. We argue that understanding how observational learning occurs in the real world will require neuroscientific frameworks that consider how visuomotor processes interface with more general aspects of cognition, as well as how learning context and action complexity shape mechanisms supporting learning from watching others.

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.