EEG theta and Mu oscillations during perception of human and robot actions - Urgen, Burcu A.; Plank, Markus; Ishiguro, Hiroshi; Poizner, Howard; Saygin, Ayse P. (Frontiers in Neurorobotics, 2013)
Perception of others’ actions supports important social skills, such as communication, intention understanding, and empathy. Are mechanisms of action processing in human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans so they can be used as stimuli to address such questions. Here, we recorded EEG during the observation of human and robot actions. Sensorimotor mu (8-13 Hz) rhythm has been linked to the motor simulation aspect of action processing (and to human mirror neuron system, MNS) and frontal theta (4-8 Hz) rhythm to semantic and memory-related aspects. We explored whether these measures exhibit selectivity for biological entities: for whether the motion and/or the visual appearance of the observed agent is biological. Participants watched videos of three agents performing the same actions. The first was a Human, and had biological motion and appearance. The other two were a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical motion and appearance. Observation of all agents induced significant attenuation in the power of mu oscillations that was equivalent for all agents. Thus, mu suppression, considered an index of the activity of the MNS, did not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta activity thus appears to be sensitive to visual appearance, suggesting artificial agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience thus can allow us to explore functional properties of action processing on the one hand, and help inform the design of social robots on the other.