Abstract
Understanding and ascribing to others’ intentions and beliefs based on the observed behavior is a key aspect of people’s everyday social lives. This is crucial also in Human-Robot Interaction because both humans and robots need to make sense of each others’ behavior in collaborative settings. Such a complex mechanism is known as the Theory of Mind (ToM), and it still holds secrets, although it has been investigated in HRI for several years.This study focuses on the second-order ToM attributions using an inverted Sally-Anne paradigm, a well-established False Belief task. The humanoid robot Pepper, equipped with vision algorithms, assumes the role of Anne and predicts where Sally (a human researcher) will search for a ball, contingent on Sally’s presence or absence during its relocation by a neutral experimenter. Two scenarios are tested: Sally exits the room (false belief) or observes the relocation (true belief). We had two experimental conditions, where the Pepper robot exhibited passive (monotonic voice, rigid gestures) and active (dynamic voice, fluid gestures) behaviors, respectively. Participants, as external observers, watch video recordings of the interactions and answer structured questions to assess how behavioral cues influence robots’ ToM attributions.Results showed that people tended to ascribe high-level ToM skills to the active robot rather than to the passive one, highlighting the importance of designing robots with appropriate expressive behaviors. By examining how humans interpret a robot’s capacity for second-order ToM, this work advances our understanding of the cognitive assumptions people make about artificial agents and offers a foundation for developing socially intelligent systems that can seamlessly integrate into collaborative environments.