Search

Why should an experimental psychologist bother entering a relatively novel field like social robotics and how could the scientific community in social robotics potentially gain from this? This paper highlights the theoretical and practical gains and challenges associated with an interdisciplinary approach when addressing current research questions in human–machine interaction, with a particular focus on social robotics. Three core issues are discussed from an experimental social psychological perspective: First, this paper focuses on the importance of a scientific, theory-driven approach to test causal relationships between key constructs of interest in the domain of human–machine interaction; second, it addresses the need for advancement in the realm of measurement, e.g., in terms of validity. To illustrate, this paper provides an overview of the author’s own research in the domain of human–robot interaction in which we have tried to live up to the aforementioned desiderata. Finally, following common Research-Development practice, we argue for bridging the gap between foundational and applied research by testing the validity of the theoretical assumptions and the robustness of our technical platforms in real-world human–machine interaction settings.

Imagined contact (IC), that is, mentally simulating an interaction with an outgroup member, reduces negative attitudes towards outgroup members, increases contact intentions, and reduces intergroup anxiety in human–human intergroup context. Our experiment tested the effectiveness of IC with a robot to improve human–robot interaction (HRI). Social psychological literature suggested that IC provides a behavioural script for an interaction. Hence, an imagined scenario similar to a real contact scenario should be more effective in eliciting the aforementioned positive effects. We therefore examined the effect of similarity between IC with a robot and the following actual HRI on interaction perception, and behaviours towards the robot. High similarity was expected to lead to a more positive perception of HRI and more positive interaction behaviour towards the robot (e.g. more social behaviour). Results showed that perceived HRI quality was evaluated more positively and participants displayed more social behaviour towards the robot when the imagined task resembled the HRI that followed, compared to when it did not resemble the subsequent HRI. When controlling for covariates, the effects on number of social behaviours and perceived interaction quality remained significant, however, there was no effect on the total amount of time spent producing social behaviours.This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Previous research on attitudes toward robots has emphasized the aspect of cultural differences regarding the acceptance of social robots in everyday life. Existing work has also focused on the importance of various other factors (e.g., demographic variables, interest in science and technology, prior robot experience) that predict robot acceptance. Specific robot types like service or healthcare robots have also been investigated. Nevertheless, more research is needed to substantiate the empirical evidence on the role of culture, robot type, and other predictors when researching attitudes toward robots. We did so by conducting a survey on attitudes toward education robots in the German context. Besides, in the present research, we investigated predictors of attitudes toward education robots. Contrary to previous findings, our results suggest that German respondents have neutral attitudes toward education robots. However, our data support the notion of relative reluctance to engage in learning processes that include robots. Regarding demographic variables and personality dispositions, our results show that gender, age, need for cognition , and technology commitment significantly predicted people's attitudes. Concerning potential areas of application, respondents could picture using education robots in domains related to science, technology, engineering, and mathematics and rejected education robots in fields of arts and social sciences.

Conversational corpora based on human-human dialogues have often been used for training of data-driven dialogue systems. However, human-human conversations might not be the optimal inputs for machine learning training aims used in HRI. This paper suggests the creation of a conversational corpus based on Human-Robot conversations as input for the training of a dialogue system used in future conversational robots. We propose that the significant differences between Human-Human Conversation (HHC) and Human-Robot Conversation (HRC) in terms of used language and other aspects (e.g., humanlikeness, embodiment, etc.) might affect the quality of the responses from a conversational robot. Hence, the use of HRCs as an input could improve the responses of the robots when the conversational machine learning system is trained using a more realistic model of HRI conversations rather than a HHI model. Future applications of conversational robots in education and health care could be enhanced by using an appropriate HRC corpus.

Previous research has shown that gender-related stereotypes are even applied to robots. In HRI, a robot’s appearance, for instance, visual facial gender cues such as hairstyle of a robot have successfully been used to elicit gender-stereotypical judgments about male and female prototypes, respectively. To complement the set of features to visually indicate a robot’s gender, we explored the impact of waist-to-hip ratio (WHR) and shoulder width (SW) in robot prototypes. Specifically, we investigated the effect of male vs. female appearance on perceived robot gender, the attribution of gender stereotypical traits, the robots’ suitability for stereotypical tasks, and participants’ trust toward the robots. Our results have demonstrated that the manipulation of WHR and SW correctly elicited gendered perceptions of the two prototypes. However, the perception of male robot gender did not affect the attribution of agentic traits and cognitive trust. Nevertheless, participants tended to rate the male robot as more suitable for stereotypically male tasks. In line with our predictions, participants preferred to use the female robot shape for stereotypically female tasks. They tended to attribute more communal traits and showed more affective trust toward the robot that was designed with a female torso versus a male robot torso. These results demonstrate that robot body shape activates stereotypes toward robots. These in turn, deeply impact people’s attitudes and trust toward robots which determine people’s motivation to engage in HRI.

The majority of EU citizens report a positive view about robots in general according to recent EU-level survey data. However, does this also hold true for attitudes towards service robots in particular? To investigate this research question, we conducted a survey on a representative sample from Germany. Importantly, we explored the role of demographic variables, interest in science and technology, and personality traits associated with psychological anthropomorphism in determining perceived acceptance of service robots in everyday life. Overall, attitudes towards service robots turned out to be relatively positive. Moreover, demographic variables (e.g., gender, occupation), interest in science and technology, and dispositional correlates of anthropomorphism had a significant impact on attitudes towards such assistive technology.

Social robots are machines developed to interact with humans. Unlike other technological devices, their presence in society requires accepting and treating them as social agents. This has important implications in terms of social changes for humans’ personal and social identity, and social interactions. We aim to explain the core features that characterize social robots by highlighting what makes them distinct from other types of innovative technology. Equally important, we illustrate how social psychology can provide a useful perspective to understand human-robot interactions. To do so, we focus on studies that have investigated the role of intergroup relations and social identity in the context of human-machine interactions to demonstrate that robots may comprise a new type of social outgroup in future society.

In the present study, we investigated teachers’ attitudes toward teaching with education robots and robot-mediated learning processes. We further explored predictors of attitudes, and investigated teachers’ willingness to use robots in diverse learning settings. To do so, we conducted a survey with 59 German school teachers. Our results suggest that teachers held rather negative attitudes toward education robots. Further, our findings indicate a positive association between technology commitment and teachers’ attitudes. Teachers reported a preferable use of robots in domains related to science, technology, engineering, and mathematics (STEM). Regarding expectations toward the future use of education robots, teachers mentioned their motivational potential, using robots as information source, or easy handling. Teachers’ concerns, however, were associated with the disruption of teaching processes, additional workload, or the fear that robots might replace interpersonal relationships. Implications of our findings for theory and design of education robots are discussed.

In an online survey, we studied user expectations and preferences for functions and apps in the context of a smart apartment. Furthermore, we explored which type of interface users would choose for an interaction with the smart apartment. Equally important, we investigated users’ acceptance of a service robot in the smart home. Results showed high levels of acceptance for both, the smart apartment and the robot, although the preferred interface for the apartment was context dependent. We discuss implications of the current survey and highlight key aspects to be taken into consideration when developing innovation technology for the home context.

In an experiment, we tested whether the gender typicality of a human–robot interaction (HRI) task would affect the users’ performance during HRI and the users’ evaluation, acceptance and anthropomorphism of the robot. $$N = 73$$ participants (38 females and 35 males) performed either a stereotypically male or a stereotypically female task while being instructed by either a ‘male’ or a ‘female’ robot. Results revealed that gender typicality of the task significantly affected our dependent measures: More errors occurred when participants collaborated with the robot in the context of a stereotypically female work domain. Moreover, when participants performed a typically female task with the robot they were less willing to accept help from the robot in a future task and they anthropomorphized the robot to a lower extent. These effects were independent of robot and participant gender. Our findings demonstrate that the gender typicality of HRI tasks substantially influences HRI as well as humans’ perceptions and acceptance of a robot.