The influence of movement skill on action representations and identification of agency was investigated. Point-light displays were created of highly skilled gestures of thirteen orchestral conductors in visual, auditory, and audiovisual versions and compared to two control conditions . In subsequent experimental sessions, participants indicated whether displays presented them or other conductors, whether the soundtrack contained their or others’ musical interpretations, and rated the quality and emotional content of the gestures. Self-recognition was more accurate in conditions presenting highly skilled conducting (...) movements as compared to other displays. Participants judged the quality of their own movements to be better than those of others, independently of whether or not they recognized movement agency. Emotional content was perceived accurately across conditions both for own and others’ actions. These results point to the influence of dynamical characteristics of motor skill, rather than merely type of movement or emotional content, on action representations and self-other identification. (shrink)

This paper outlines the methodology and experiments associated with the reshaping of human intentions based on robot movements within Human-Robot Interactions. Although studies on estimating human intentions are well studied in the literature, reshaping intentions through robot-initiated interactions is a new significant branching in the field of HRI. In this paper, we analyze how estimated human intentions can intentionally change through cooperation with mobile robots in real Human-Robot environments. This paper proposes an intention-reshaping system that includes either the Observable Operator (...) Models or Hidden Markov Models to estimate human intention and decide which moves a robot should perform to reshape previously estimated human intentions into desired ones. At the low level, the system needs to track the locations of all mobile agents using cameras. We test our system on videos taken in a real HRI environment that has been developed as our experimental setup. The results show that OOMs are faster than HMMs and both models give correct decisions for testing sequences. (shrink)