People's Explanations of Robot Behavior Subtly Reveal Mental State Inferences

It has long been assumed that when people observe robots they intuitively ascribe mind and intentionality to them, just as they do to humans. However, much of this evidence relies on experimenter-provided questions or self-reported judgments. We propose a new way of investigating people's mental state ascriptions to robots by carefully studying explanations of robot behavior. Since people's explanations of human behavior are deeply grounded in assumptions of mind and intentional agency, explanations of robot behavior can reveal whether such assumptions similarly apply to robots. We designed stimulus behaviors that were representative of a variety of robots in diverse contexts and ensured that people saw the behaviors as equally intentional, desirable, and surprising across both human and robot agents. We provided 121 participants with verbal descriptions of these behaviors and asked them to explain in their own words why the agent (human or robot) had performed them. To systematically analyze the verbal data, we used a theoretically grounded classification method to identify core explanation types. We found that people use the same conceptual toolbox of behavior explanations for both human and robot agents, robustly indicating inferences of intentionality and mind. But people applied specific explanatory tools at somewhat different rates and in somewhat different ways for robots, revealing specific expectations people hold when explaining robot behaviors.