This study investigates the influence of a robot's speech rate. In human communication, slow speech is considered boring, speech at normal speed is perceived as credible, and fast speech is perceived as competent. To seek the appropriate speech rate for robots, we test whether these tendencies are replicated in human-robot interaction by conducting an experiment with four rates of speech: fast, normal, moderately slow, and slow. Our experimental results reveal a rather surprising trend. Participants prefer normal and moderately slow speech (...) to fast speech. A robot that provides normal or moderately slow speech is perceived as competent. We further study how context affects this perception. In a situation where the robot and participants talk while walking, we found that slow speech was the most comprehensible. In addition, slow speech is subjectively perceived as good as moderately slow and normal speech. Keywords: Human-robot interaction; speech rate. (shrink)

Social robotics aims at designing robots capable of joint interaction with humans. On a conceptual level, sufficient mutual understanding is usually said to be a necessary condition for joint interaction. Against this background, the following questions remain open: in which sense is it legitimate to speak of human–robot joint interaction? What exactly does it mean to speak of humans and robots sufficiently understanding each other to account for human–robot joint interaction? Is such joint interaction effectively possible by reference, e.g., to (...) the mere ascription or simulation of understanding? To answer these questions, we first discuss technical approaches which aim at the implementation of certain aspects of human–human communication and interaction in social robots in order to make robots accessible and understandable to humans and, hence, human–robot joint interaction possible. Second, we examine the human tendency to anthropomorphize in this context, with a view to human understanding of and joint interaction with social robots. Third, we analyze the most prominent concepts of mutual understanding and their implications for human–robot joint interaction. We conclude that it is—at least for the time being—not legitimate to speak of human–robot joint interaction, which has relevant implications both morally and ethically. (shrink)

This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing (...) an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models. (shrink)

This article presents an applied discussion of the possibility of integrating conversation analysis (CA) methodology into that of machine learning. The aim is to improve the detection of that which resembles disengagement in the interaction between a robot and a human. We offer a novel analytical assemblage at the heart of the two disciplines, and namely on the level of the annotation schemes provided by conversation analysis transcription methods. First, we demonstrate that the need for a stable structure in establishing (...) an interaction scenario and in designing robot behaviours does not prevent the emergence of ordinariness or creativity among the participants engaged in this interaction. Secondly, based on an actual case, we emphasize the possibility of systematicness in CA transcription to support the choice (a) of the categories targeted by prediction methods and defined by the annotation scheme, and (b) of the verbal and non-verbal features used to create prediction models. (shrink)

Learning is a central component of human life and essential for personal development. Therefore, utilizing new technologies in the learning context and exploring their combined potential are considered essential to support self-directed learning in a digital age. A learning environment can be expanded by various technical and content-related aspects. Gamification in the form of elements from video games offers a potential concept to support the learning process. This can be supplemented by technology-supported learning. While the use of tablets is already (...) widespread in the learning context, the integration of a social robot can provide new perspectives on the learning process. However, simply adding new technologies such as social robots or gamification to existing systems may not automatically result in a better learning environment. In the present study, game elements as well as a social robot were integrated separately and conjointly into a learning environment for basic Spanish skills, with a follow-up on retained knowledge. This allowed us to investigate the respective and combined effects of both expansions on motivation, engagement and learning effect. This approach should provide insights into the integration of both additions in an adult learning context. We found that the additions of game elements and the robot did not significantly improve learning, engagement or motivation. Based on these results and a literature review, we outline relevant factors for meaningful integration of gamification and social robots in learning environments in adult learning. (shrink)

—Human eyes not only serve the function of enabling us “to see” something, but also perform the vital role of allowing us “to show” our gaze for non-verbal communication, such as through establishing eye contact and joint attention. The eyes of service robots should therefore also perform both of these functions. Moreover, they should be friendly in appearance so that humans may feel comfortable with the robots. Therefore we maintain that it is important to consider gaze communication capability and friendliness (...) in designing the appearance of robot eyes. In this paper, we propose a new robot face with rear-projected eyes for changing their appearance while simultaneously realizing the showing of gaze by incorporating stereo cameras. Additionally, we examine which shape of robot eyes is most suitable for gaze reading and gives the friendliest impression, through experiments where we altered the shape and iris size of robot eyes. Keywords: Gaze reading; facial design; projector camera system. (shrink)

We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development (...) of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses,the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human–robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience. (shrink)

The term embodiment identifies a theory that meaning and semantics cannot be captured by abstract, logical systems, but are dependent on an agent’s experience derived from being situated in an environment. This theory has recently received a great deal of support in the cognitive science literature and is having significant impact in artificial intelligence. Memetics refers to the theory that knowledge and ideas can evolve more or less independently of their human-agent substrates. While humans provide the medium for this evolution, (...) memetics holds that ideas can be developed without human comprehension or deliberate interference. Both theories have profound implications for the study of language—its potential use by machines, its acquisition by children and of particular relevance to this special issue, its evolution. This article links the theory of memetics to the established literature on semantic space, then examines the extent to which these memetic mechanisms might account for language independently of embodiment. It then seeks to explain the evolution of language through uniquely human cognitive capacities which facilitate memetic evolution. (shrink)

A novel social interaction is a dynamic process, in which participants adapt to, react to and engage with their social partners. To facilitate such interactions, people gather information relating to the social context and structure of the situation. The current study aimed to deepen the understanding of the psychological determinants of behavior in a novel social interaction. Three social robots and the participant interacted non-verbally according to a pre-programmed “relationship matrix” that dictated who favored whom. Participants' gaze was tracked during (...) the interaction and, using Bayesian inference models, resulted in a measure of participants' social information-gathering behaviors. Our results reveal the dynamics in a novel environment, wherein information-gathering behavior is initially predicted by psychological inflexibility and then, toward the end of the interaction, predicted by curiosity. These results highlight the utility of using social robots in behavioral experiments. (shrink)