To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socializing intervention to probe the flexibility of empathy, a core component of social relationships, towards robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socializing with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socializing intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socializing. Whole-brain analysis showed that, before the socializing intervention, superior parietal and early visual regions are sensitive to novel agents, while after socializing, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socializing scan session. Together, these findings suggest that a short socialization intervention with a social robot does not lead to discernible differences in empathy towards the robot, as measured by behavioural or brain responses. We discuss the extent to which long-term socialization with robots might shape social cognitive processes and ultimately our relationships with these machines. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Understanding human social interactions with robots is important for designing robots for social tasks. Here, we investigate undergraduate participants’ situational cooperation tendencies towards a robot opponent using prisoner’s dilemma games. With two conditions where incentives for cooperative decisions were manipulated to be high or low, we predicted that people would cooperate more often with the robot in high-incentive conditions. Our results showed incentive structure did not predict human cooperation overall, but did influence cooperation in early rounds, where participants cooperated significantly more in the high-incentive condition. Exploratory analyses revealed other two behavioural tendencies: (1) participants played a tit-for-tat strategy against the robot (whose decisions were random); and (2) participants only behaved prosocially toward the robot when they had achieved high scores themselves. Our findings highlight ways in which social behaviour toward robots might differ from social behaviour toward humans, and inform future work on human–robot interactions in collaborative contexts.

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Background: As research examining human-robot interaction moves from the laboratory to the real world, studies seeking to examine how people interact with robots face the question of which robotic platform to employ to collect data in situ. To facilitate the study of a broad range of individuals, from children to clinical populations, across diverse environments, from homes to schools, a robust, reproducible, low-cost and easy-to-use robotic platform is needed. Methods: We describe how a commercially available off-the-shelf robot, Cozmo, can be used to study embodied human-robot interactions in a wide variety of settings, including the user’s home. We describe the steps required to use this affordable and flexible platform for longitudinal human-robot interaction studies. First, we outline the technical specifications and requirements of this platform and accessories. We then show how log files containing detailed data on the human-robot interaction can be collected and extracted. Finally, we detail the types of information that can be retrieved from these data. Results: We present findings from a validation that mapped the behavioural repertoire of the Cozmo robot and introduce an accompanying interactive emotion classification tool to use with this robot. This tool combined with the data extracted from the log files can provide the necessary details to understand the psychological consequences of long-term interactions. Conclusions: This low-cost robotic platform has the potential to provide the field with a variety of valuable new possibilities to study the social cognitive processes underlying human-robot interactions within and beyond the research laboratory, which are user-driven and unconstrained in both time and place.

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