The Importance of Being Consistent: Attribution of Mental States in Strategic Human-Robot Interactions.

This article investigates the attribution of mental state (AMS) to an anthropomorphic robot by humans in a strategic interaction. We conducted an experiment in which human subjects are paired with either a human or an anthropomorphic robot to play an iterated Prisoner's Dilemma game, and we tested whether AMS is dependent on the robot "consistency," that is, the correspondence between the robot's verbal reaction and its behavior after a nonoptimal social outcome of the game is obtained. We find that human partners are attributed a higher mental state level than robotic partners, regardless of the partner's consistency between words and actions. Conversely, the level of AMS assigned to the robot is significantly higher when the robot is consistent in its words and actions. This finding is robust to the inclusion of psychological factors such as risk attitude and trust, and it holds regardless of subjects' initial beliefs about the adaptability of the robot. Finally, we find that when the robot apologizes for its behavior and defects in the following stage, the epistemic component of the AMS significantly increases.

Machines Like Us and People Like You: Toward Human-Robot Shared Experience.

In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, construction, entertainment, sports, and many others. However, current social robotics is still far from the general abilities we expect in a robot collaborator. This limitation is more evident when robots are faced with real-life contexts and activities occurring over long periods. In this article, we argue that human-robot collaboration is more than just being able to work side by side on complementary tasks: collaboration is a complex relational process that entails mutual understanding and reciprocal adaptation. Drawing on this assumption, we propose to shift the focus from "human-robot interaction" to "human-robot shared experience." We hold that for enabling the emergence of such shared experiential space between humans and robots, constructs such as coadaptation, intersubjectivity, individual differences, and identity should become the central focus of modeling. Finally, we suggest that this shift in perspective would imply changing current mainstream design approaches, which are mainly focused on functional aspects of the human-robot interaction, to the development of architectural frameworks that integrate the enabling dimensions of social cognition.

Emerging Adults' Expectations About the Next Generation of Robots: Exploring Robotic Needs Through a Latent Profile Analysis.

The investigation of emerging adults' expectations of development of the next generation of robots is a fundamental challenge to narrow the gap between expectations and real technological advances, which can potentially impact the effectiveness of future interactions between humans and robots. Furthermore, the literature highlights the important role played by negative attitudes toward robots in setting people's expectations. To better explore these expectations, we administered the Scale for Robotic Needs and performed a latent profile analysis to describe different expectation profiles about the development of future robots. The profiles identified through this methodology can be placed along a continuum of robots' humanization: from a group that desires mainly the technical features to a group that imagines a humanized robot in the future. Finally, the analysis of emerging adults' knowledge about robots and their negative attitudes toward robots allowed us to understand how these affect their expectations.