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.

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.

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.