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      EEG theta and Mu oscillations during perception of human and robot actions

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          Abstract

          The perception of others’ actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8–13 Hz) and frontal theta (4–8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does 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 thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other.

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          Most cited references60

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          Seeing or doing? Influence of visual and motor familiarity in action observation.

          The human brain contains specialized circuits for observing and understanding actions. Previous studies have not distinguished whether this "mirror system" uses specialized motor representations or general processes of visual inference and knowledge to understand observed actions. We report the first neuroimaging study to distinguish between these alternatives. Purely motoric influences on perception have been shown behaviorally, but their neural bases are unknown. We used fMRI to reveal the neural bases of motor influences on action observation. We controlled for visual and knowledge effects by studying expert dancers. Some ballet moves are performed by only one gender. However, male and female dancers train together and have equal visual familiarity with all moves. Male and female dancers viewed videos of gender-specific male and female ballet moves. We found greater premotor, parietal, and cerebellar activity when dancers viewed moves from their own motor repertoire, compared to opposite-gender moves that they frequently saw but did not perform. Our results show that mirror circuits have a purely motor response over and above visual representations of action. We understand actions not only by visual recognition, but also motorically. In addition, we confirm that the cerebellum is part of the action observation network.
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            Socially intelligent robots: dimensions of human-robot interaction.

            Social intelligence in robots has a quite recent history in artificial intelligence and robotics. However, it has become increasingly apparent that social and interactive skills are necessary requirements in many application areas and contexts where robots need to interact and collaborate with other robots or humans. Research on human-robot interaction (HRI) poses many challenges regarding the nature of interactivity and 'social behaviour' in robot and humans. The first part of this paper addresses dimensions of HRI, discussing requirements on social skills for robots and introducing the conceptual space of HRI studies. In order to illustrate these concepts, two examples of HRI research are presented. First, research is surveyed which investigates the development of a cognitive robot companion. The aim of this work is to develop social rules for robot behaviour (a 'robotiquette') that is comfortable and acceptable to humans. Second, robots are discussed as possible educational or therapeutic toys for children with autism. The concept of interactive emergence in human-child interactions is highlighted. Different types of play among children are discussed in the light of their potential investigation in human-robot experiments. The paper concludes by examining different paradigms regarding 'social relationships' of robots and people interacting with them.
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              Premotor cortex and the recognition of motor actions.

              In area F5 of the monkey premotor cortex there are neurons that discharge both when the monkey performs an action and when he observes a similar action made by another monkey or by the experimenter. We report here some of the properties of these 'mirror' neurons and we propose that their activity 'represents' the observed action. We posit, then, that this motor representation is at the basis of the understanding of motor events. Finally, on the basis of some recent data showing that, in man, the observation of motor actions activate the posterior part of inferior frontal gyrus, we suggest that the development of the lateral verbal communication system in man derives from a more ancient communication system based on recognition of hand and face gestures.
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                Author and article information

                Journal
                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                1662-5218
                13 November 2013
                2013
                : 7
                : 19
                Affiliations
                [1] 1Department of Cognitive Science, University of California San Diego San Diego, CA, USA
                [2] 2Qualcomm Institute, California Institute of Telecommunications and Information Technology, University of California San Diego San Diego, CA, USA
                [3] 3Institute for Neural Computation, University of California San Diego San Diego, CA, USA
                [4] 4Osaka University Osaka, Japan
                [5] 5Advanced Telecommunications Research Keihanna Science City, Japan
                [6] 6Neurosciences Program, University of California San Diego San Diego, CA, USA
                Author notes

                Edited by: Alex Pitti, Université de Cergy-Pontoise, France

                Reviewed by: Michel Hoen, Université Claude Bernard Lyon 1, France; Peter Marshall, Temple University, USA; Guillaume Dumas, Florida Atlantic University, USA

                *Correspondence: Burcu A. Urgen and Ayse P. Saygin, Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0515, USA e-mail: burgencogsci.ucsd.edu; saygin@ 123456cogsci.ucsd.edu

                This article was submitted to the journal Frontiers in Neurorobotics.

                Article
                10.3389/fnbot.2013.00019
                3826547
                09394107-6a99-4c27-aeb9-0a132df63433
                Copyright © 2013 Urgen, Plank, Ishiguro, Poizner and Saygin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 May 2013
                : 09 October 2013
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 91, Pages: 13, Words: 0
                Categories
                Neuroscience
                Original Research Article

                Robotics
                eeg,mirror neuron system,social robotics,theta rhythm,mu rhythm,action perception
                Robotics
                eeg, mirror neuron system, social robotics, theta rhythm, mu rhythm, action perception

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