Seriously, What Did One Robot Say to the Other? Being Left out From Communication by Robots Causes Feelings of Social Exclusion

While humans actually need some overt communication channel to transmit information, be it verbally or nonverbally, robots could use their network connection to transmit information quickly to other robots. This raises the question how this covert robot-robot communication is perceived by humans. The current study investigates how transparency about communication happening between two robots affects humans’ trust in and perception of these robots as well as their feeling of being included/excluded in the interaction. Three different robot-robot communication styles were analyzed: silent, robotic language, and natural language. Results show that when robots transmit information in a robotic language (beep sounds) this leads to lower trust and more feelings of social exclusion than in the silent (i.e., covert) or natural language conditions. Results support the notion that humans are over-sensitive to signs of ostracism which seems to be detected in this style of overt but nonhuman robot-robot communication

Uncanniliy Human - Experimental Investigation of the Uncanny Valley Phenomenon

Seit seiner Einführung in den wissenschaftlichen Diskurs im Jahr 1970 (Mori, 1970; Mori et al., 2012) ist das Uncanny Valley eine der meist diskutierten und referenzierten Theorien in der Robotik. Obwohl die Theorie vor mehr als 40 Jahren postuliert wurde, wurde sie kaum empirisch untersucht. Erst in den letzten sieben Jahren haben Wissenschaftler aus dem Bereich Robotik, aber auch aus anderen Disziplinen, angefangen, das Uncanny Valley systematischer zu erforschen. Allerdings blieben bisher viele Fragen offen. Einiger dieser Fragen wurden in dem vorliegenden Forschungsprojekt im Rahmen von vier aufeinander aufbauenden Studien untersucht. Der Schwerpunkt der Arbeit liegt auf der systematischen Untersuchung des Einflusses von statischen und dynamischen Merkmalen von Robotern, wie etwa dem Design bzw. Erscheinungsbild und der Bewegung, auf die Wahrnehmung und Evaluation von diesen Robotern. Eine Besonderheit der vorliegenden Arbeit ist der multi-methodologische Ansatz, bei dem die durch verschiedenste Methoden und Messinstrumente beobachteten Effekte auf ihre Relevanz für die Uncanny Valley Theorie hin untersucht wurden. Zudem wurden die in der bisherigen Literatur postulierten Erklärungsansätze für den Uncanny Valley Effekt empirisch getestet. In der ersten Studie wurde anhand von qualitativen Interviews, in denen Probanden Bilder und Videos von humanoiden und androiden Robotern gezeigt wurden, untersucht, wie Probanden sehr menschenähnliche Roboter evaluieren, ob sie emotionale Reaktionen zeigen, und wie ihre Einstellungen gegenüber diesen Robotern sind. Die Ergebnisse zeigen, dass emotionale Reaktion, wenn überhaupt vorhanden, individuell sehr verschieden ausfallen. Das Erscheinungsbild der Roboter war sehr wichtig, denn bestimmte Designmerkmale wurden mit bestimmten Fähigkeiten gleichgesetzt. Ein menschliches Erscheinungsbild ohne Funktionalität wurde eher negativ bewertet. Zudem schienen die Probanden bei androiden Robotern dieselben Maßstäbe zur Bewertung von Attraktivität anzulegen wie sie dies bei Menschen tun. Die Analyse zeigte auch die Relevanz der Bewegungen der Roboter und des Kontextes, in welchem der jeweilige Roboter präsentiert wurde. Es wurde erste Evidenz gefunden für die Annahme, dass Menschen Unsicherheit verspüren bei der Kategorisierung von androiden Robotern als entweder Roboter oder Mensch. Zudem fühlten sich die Probanden unwohl bei dem Gedanken, dass Roboter sie ersetzten könnten. Die zweite Studie untersuchte den Einfluss von robotischer Bewegung. In einem quasi-experimentellen Feldexperiment wurden Passanten mit dem androiden Roboter Geminoid HI-1 konfrontiert, der sich entweder still verhielt oder Bewegungsverhalten zeigte. Die Interaktionen wurden analysiert hinsichtlich des nonverbalen Verhaltens der Passanten (z.B. auf den Roboter gerichtete Aufmerksamkeit, interpersonale Distanz zum Roboter). Die Resultate zeigen, dass das Verhalten der Passanten von dem Verhalten des Roboters beeinflusst wurde, zum Beispiel waren die Interaktionen länger, die Probanden stellten mehr Blickkontakt her und testeten die Fähigkeiten des Roboters wenn dieser Bewegungsverhalten zeigte. Zudem diente das Verhalten des Roboters als Hinweisreiz für die richtige Kategorisierung des Roboters als solchen. Der Aspekt des Erscheinungsbildes wurde in der dritten Studie systematisch untersucht. Zu diesem Zweck wurden in einem webbasierten Fragebogen 40 standardisierte Bilder von Robotern evaluiert, um die Evaluation beeinflussende Designmerkmale zu identifizieren. Eine Clusteranalyse ergab sechs Cluster von Robotern, die auf sechs Dimensionen unterschiedlich bewertet wurden. Mögliche Beziehungen zwischen Designmerkmalen und Evaluationen der Cluster wurden aufgezeigt und diskutiert. Zudem wurde die Aussagekraft des Uncanny Valley Graphen untersucht. Ausgehend von Mori’s Überlegungen ist der Uncanny Valley Effekt eine kubische Funktion. Demnach müssten sich die Daten am besten durch eine kubische Funktion erklären lassen. Die Ergebnisse zeigten allerdings eine bessere Modellpassung für lineare oder quadratische Zusammenhänge. In der letzten Studie wurden perzeptions-orientiert und evolutionsbiologische Erklärungsansätze für das Uncanny Valley systematisch getestet. In dieser Studie wurden Daten aus Selbstauskunft, Verhaltensdaten und funktionelle Bildgebung kombiniert, um zu untersuchen ob sich die Effekte auf Basis der Selbstauskunft und der Verhaltensdaten erklären lassen durch a) zusätzliche Verarbeitungsleistung während der Perzeption von Gesichtern, b) automatisch ablaufende Prozesse sozialer Kognition, oder c) eine Überempfindlichkeit des sogenannten Verhaltensimmunsystems (behavioral immune system). Die Ergebnisse unterstützen die perzeptions-orientierten Erklärungen für den Uncanny Valley Effekt. Zum einen scheinen die Verhaltenseffekte durch neuronale Prozesse während der Wahrnehmung von Gesichtern begründet zu sein. Zum anderen gibt es Befunde, die auf eine kategoriale Wahrnehmung von Robotern und Menschen hinweisen. Evolutionsbiologische Erklärungen konnten durch die vorliegenden Daten nicht gestützt werden.Since its introduction into scientific discourse in 1970 (Mori, 1970; Mori et al., 2012) the uncanny valley has been a highly discussed and referenced theory in the field of robotics. Although the theory was postulated more than 40 years ago, it has barely been tested empirically. However, in the last seven years robot scientists addressed themselves to the task of investigating the uncanny valley more systematically. But there are still open questions, some of which have been addressed within this research in the course of four consecutive studies. This project focussed on the systematic investigation of how static and dynamic characteristics of robots such as appearance and movement determine evaluations of and behavior towards robots. The work applied a multi-methodological approach and the various observed effects were examined with regard to their importance for the assumed uncanny valley. In addition, previously proposed explanations for the uncanny valley effect were tested. The first study utilized qualitative interviews in which participants were presented with pictures and videos of humanoid and android robots to explore participants’ evaluations of very human-like robots, their attitudes about these robots, and their emotional reactions towards these robots. Results showed that emotional experiences, if existent, were very individual. The robots’ appearance was of great importance for the participants, because certain characteristics were equalized with certain abilities, merely human appearance without a connected functionality was not appreciated, and human rules of attractiveness were applied to the android robots. The analysis also demonstrated the importance of the robots’ movements and the social context they were placed in. First evidence was found supporting the assumption that participants experienced uncertainty how to categorize android robots (as human or machine) and that they felt uncomfortable at the thought to be replaced by robots. The influence of movement, as one of the important factors in the uncanny valley hypothesis, was examined in the second study. In a quasi-experimental observational field study people were confronted with the android robot Geminoid HI-1 either moving or not moving. These interactions between humans and the android robot were analyzed with regard to the participants’ nonverbal behavior (e.g. attention paid to the robot, proximity). Results show that participants’ behavior towards the android robot was influenced by the behavior the robot displayed. For instance, when the robot established eye-contact participants engaged in longer interactions, also established more eye-contact and tried to test the robots’ capabilities. The robot’s behavior served as cue for the participants to categorize the robot as such. The aspect of robot appearances was examined systematically in the third study in order to identify certain robot attractiveness indices or design characteristics which determine how people perceive robots. A web-based survey was conducted with standardized pictures of 40 different mechanoid, humanoid and android robots. A cluster analysis revealed six clusters of robots which were rated significantly different on six dimensions. Possible relationships of design characteristics and the evaluation of robots have been outlined. Moreover, it has been tested whether the data of this study can best be explained by a cubic funtion as would be suggested by the graph proposed by Mori. Results revealed that the data can be best explained by linear or quadratic relationships. The last study systematically tested perception-oriented and evolutionary-biological approaches for the uncanny valley. In this multi-methodological study, self-report and behavioral data were combined with functional magnetic resonance imaging techniques in order to examine whether the observed effects in self-report and behavior occur due to a) additional processing during face perception of human and robotic stimuli, b) automatically elicited processes of social cognition, or c) oversensitivity of the behavioral immune system. The study found strong support for perception-oriented explanations for the uncanny valley effect. First, effects seem to be driven by face perception processes. Further, there were indicators for the assumption that categorical perception takes place. In the contrary, evolutionary-biological driven explanations assuming that uncanny valley related reactions are due to oversensitivity of the behavioral immune system were not supported by this work. Altogether, this dissertation explored the importance of characteristics of robots which are relevant for the uncanny valley hypothesis. Uncanny valley related responses were examined using a variety of measures, for instance, self-reporting, behavior, and brain activation, allowing conclusions with regard to the influence of the choice of measurements on the detection of uncanny valley related responses. Most importantly, explanations for the uncanny valley were tested systematically and support was found for cognitive-oriented and perception-oriented explanations

BUSSARD -- Better Understanding Social Situations for Autonomous Robot Decision-Making

We report on our effort to create a corpus dataset of different social context situations in an office setting for further disciplinary and interdisciplinary research in computer vision, psychology, and human-robot-interaction. For social robots to be able to behave appropriately, they need to be aware of the social context they act in. Consider, for example, a robot with the task to deliver a personal message to a person. If the person is arguing with an office mate at the time of message delivery, it might be more appropriate to delay playing the message as to respect the recipient's privacy and not to interfere with the current situation. This can only be done if the situation is classified correctly and in a second step if an appropriate behavior is chosen that fits the social situation. Our work aims to enable robots accomplishing the task of classifying social situations by creating a dataset composed of semantically annotated video scenes of office situations from television soap operas. The dataset can then serve as a basis for conducting research in both computer vision and human-robot interaction.Comment: In SCRITA 2023 Workshop Proceedings (arXiv:2311.05401) held in conjunction with 32nd IEEE International Conference on Robot & Human Interactive Communication, 28/08 - 31/08 2023, Busan (Korea

Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley.

Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The uncanny valley (UV) hypothesis predicts that humans prefer anthropomorphic agents but reject them if they become too humanlike-the so-called UV reaction. Using fMRI, we investigated neural activity when subjects evaluated artificial agents and made decisions about them. Across two experimental tasks, the ventromedial prefrontal cortex (VMPFC) encoded an explicit representation of subjects' UV reactions. Specifically, VMPFC signaled the subjective likability of artificial agents as a nonlinear function of humanlikeness, with selective low likability for highly humanlike agents. In exploratory across-subject analyses, these effects explained individual differences in psychophysical evaluations and preference choices. Functionally connected areas encoded critical inputs for these signals: the temporoparietal junction encoded a linear humanlikeness continuum, whereas nonlinear representations of humanlikeness in dorsomedial prefrontal cortex (DMPFC) and fusiform gyrus emphasized a human-nonhuman distinction. Following principles of multisensory integration, multiplicative combination of these signals reconstructed VMPFC's valuation function. During decision making, separate signals in VMPFC and DMPFC encoded subjects' decision variable for choices involving humans or artificial agents, respectively. A distinct amygdala signal predicted rejection of artificial agents. Our data suggest that human reactions toward artificial agents are governed by a neural mechanism that generates a selective, nonlinear valuation in response to a specific feature combination (humanlikeness in nonhuman agents). Thus, a basic principle known from sensory coding-neural feature selectivity from linear-nonlinear transformation-may also underlie human responses to artificial social partners.SIGNIFICANCE STATEMENT Would you trust a robot to make decisions for you? Autonomous artificial agents are increasingly entering our lives, but how the human brain responds to these new artificial social partners remains unclear. The uncanny valley (UV) hypothesis-an influential psychological framework-captures the observation that human responses to artificial agents are nonlinear: we like increasingly anthropomorphic artificial agents, but feel uncomfortable if they become too humanlike. Here we investigated neural activity when humans evaluated artificial agents and made personal decisions about them. Our findings suggest a novel neurobiological conceptualization of human responses toward artificial agents: the UV reaction-a selective dislike of highly humanlike agents-is based on nonlinear value-coding in ventromedial prefrontal cortex, a key component of the brain's reward system

Comparing the Effects of Social Robots and Virtual Agents on Exercising Motivation

Schneider S, Kummert F. Comparing the Effects of Social Robots and Virtual Agents on Exercising Motivation. In: Social Robotics. Lecture Notes in Computer Science. Vol 11357. Cham: Springer International Publishing; 2018: 451-461

Customer’s Acceptance of Humanoid Robots in Services: The Moderating Role of Risk Aversion

The emerging introduction of humanoid robots in service encounters is becoming a reality in the present and the short-term. Owing to this unstoppable advance, there is a need to better understand customers’ perceptions and reactions toward humanoid agents in service encounters. To shed some light on this underexplored phenomenon, this research investigates how the interaction between robot and customer’s features may contribute to a successful introduction of this disruptive innovation. Results of an empirical study with a sample of 168 US customers reveal that customer’s perceptions of robot’s human-likeness increase the intentions to use humanoid service robots. Interestingly, customers’ risk aversion moderates this relationship. Specifically, the study found that highly risk-averse customers tend to avoid using humanoids when they are perceived as highly mechanical-like. The discussion highlights the main contributions of the research, which combine previous knowledge on human–robot interaction and risk aversion from a marketing approach. Managerial implications derived from the research findings and the avenues opened for further research are described at the end

Emotional design and human-robot interaction

Recent years have shown an increase in the importance of emotions applied to the Design field - Emotional Design. In this sense, the emotional design aims to elicit (e.g., pleasure) or prevent (e.g., displeasure) determined emotions, during human product interaction. That is, the emotional design regulates the emotional interaction between the individual and the product (e.g., robot). Robot design has been a growing area whereby robots are interacting directly with humans in which emotions are essential in the interaction. Therefore, this paper aims, through a non-systematic literature review, to explore the application of emotional design, particularly on Human-Robot Interaction. Robot design features (e.g., appearance, expressing emotions and spatial distance) that affect emotional design are introduced. The chapter ends with a discussion and a conclusion.info:eu-repo/semantics/acceptedVersio

A systematic review of attitudes, anxiety, acceptance, and trust towards social robots

As social robots become more common, there is a need to understand how people perceive and interact with such technology. This systematic review seeks to estimate people’s attitudes toward, trust in, anxiety associated with, and acceptance of social robots; as well as factors that are associated with these beliefs. Ninety-seven studies were identified with a combined sample of over 13,000 participants and a standardized score was computed for each in order to represent the valence (positive, negative, or neutral) and magnitude (on a scale from 1 to − 1) of people’s beliefs about robots. Potential moderating factors such as the robots’ domain of application and design, the type of exposure to the robot, and the characteristics of potential users were also investigated. The findings suggest that people generally have positive attitudes towards social robots and are willing to interact with them. This finding may challenge some of the existing doubt surrounding the adoption of robotics in social domains of application but more research is needed to fully understand the factors that influence attitudes