Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to inter-act with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user’s needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots (...) were able to adequately demonstrate this, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye tracking, electroencephalography (EEG), or functional near-infrared spectroscopy (fNIRS) embedded in interactive human-robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive mechanisms involved in human-human interactions, and high-light the importance of perceiving others as intentional agents to activate these brain areas. We then discuss how attribution of intentionality can positively affect human-robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human-robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles. (shrink)

ABSTRACTIn our daily lives, we need to predict and understand others’ behavior in order to navigate through our social environment. Predictions concerning other humans’ behavior usually refer to th...

This study examined individual differences in sensitivity to human-like features of a robot’s behavior. The paradigm comprised a non-verbal Turing test with a humanoid robot. A “programmed” condition differed from a “human-controlled” condition by onset times of the robot’s eye movements, which were either fixed across trials or modeled after prerecorded human reaction times, respectively. Participants judged whether the robot behavior was programmed or human-controlled, with no information regarding the differences between respective conditions. Autistic traits were measured with the autism-spectrum (...) quotient questionnaire in healthy adults. We found that the fewer autistic traits participants had, the more sensitive they were to the difference between the conditions, without explicit awareness of the nature of the difference. We conclude that although sensitivity to fine behavioral characteristics of others varies with social aptitude, humans are in general capable of detecting human-like behavior based on very subtle cues. (shrink)

This study examined individual differences in sensitivity to human-like features of a robot’s behavior. The paradigm comprised a non-verbal Turing test with a humanoid robot. A “programmed” condition differed from a “human-controlled” condition by onset times of the robot’s eye movements, which were either fixed across trials or modeled after prerecorded human reaction times, respectively. Participants judged whether the robot behavior was programmed or human-controlled, with no information regarding the differences between respective conditions. Autistic traits were measured with the autism-spectrum (...) quotient questionnaire in healthy adults. We found that the fewer autistic traits participants had, the more sensitive they were to the difference between the conditions, without explicit awareness of the nature of the difference. We conclude that although sensitivity to fine behavioral characteristics of others varies with social aptitude, humans are in general capable of detecting human-like behavior based on very subtle cues. (shrink)

In our daily lives, we are continually involved in decision-making situations, many of which take place in the context of social interaction. Despite the ubiquity of such situations, there remains a gap in our understanding of how decision-making unfolds in social contexts, and how communicative signals, such as social cues and feedback, impact the choices we make. Interestingly, there is a new social context to which humans are recently increasingly more frequently exposed—social interaction with not only other humans but also (...) artificial agents, such as robots or avatars. Given these new technological developments, it is of great interest to address the question of whether—and in what way—social signals exhibited by non-human agents influence decision-making. The present study aimed to examine whether robot non-verbal communicative behavior has an effect on human decision-making. To this end, we implemented a two-alternative-choice task where participants were to guess which of two presented cups was covering a ball. This game was an adaptation of a “Shell Game.” A robot avatar acted as a game partner producing social cues and feedback. We manipulated robot's cues (pointing toward one of the cups) before the participant's decision and the robot's feedback (“thumb up” or no feedback) after the decision. We found that participants were slower (compared to other conditions) when cues were mostly invalid and the robot reacted positively to wins. We argue that this was due to the incongruence of the signals (cue vs. feedback), and thus violation of expectations. In sum, our findings show that incongruence in pre- and post-decision social signals from a robot significantly influences task performance, highlighting the importance of understanding expectations toward social robots for effective human–robot interactions. (shrink)