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Observation and imitation of actions performed by humans androids and robots: an EMG study

机译:观察和模仿人类机器人和机器人执行的动作:一项EMG研究

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摘要

Understanding others’ actions is essential for functioning in the physical and social world. In the past two decades research has shown that action perception involves the motor system, supporting theories that we understand others’ behavior via embodied motor simulation. Recently, empirical approach to action perception has been facilitated by using well-controlled artificial stimuli, such as robots. One broad question this approach can address is what aspects of similarity between the observer and the observed agent facilitate motor simulation. Since humans have evolved among other humans and animals, using artificial stimuli such as robots allows us to probe whether our social perceptual systems are specifically tuned to process other biological entities. In this study, we used humanoid robots with different degrees of human-likeness in appearance and motion along with electromyography (EMG) to measure muscle activity in participants’ arms while they either observed or imitated videos of three agents produce actions with their right arm. The agents were a Human (biological appearance and motion), a Robot (mechanical appearance and motion), and an Android (biological appearance and mechanical motion). Right arm muscle activity increased when participants imitated all agents. Increased muscle activation was found also in the stationary arm both during imitation and observation. Furthermore, muscle activity was sensitive to motion dynamics: activity was significantly stronger for imitation of the human than both mechanical agents. There was also a relationship between the dynamics of the muscle activity and motion dynamics in stimuli. Overall our data indicate that motor simulation is not limited to observation and imitation of agents with a biological appearance, but is also found for robots. However we also found sensitivity to human motion in the EMG responses. Combining data from multiple methods allows us to obtain a more complete picture of action understanding and the underlying neural computations.
机译:了解他人的行为对于在现实世界和社会世界中发挥作用至关重要。在过去的二十年中,研究表明动作感知涉及电机系统,支持了我们通过具体的电机仿真来了解他人行为的理论。近来,通过使用诸如机器人之类的受控良好的人工刺激,促进了对动作感知的经验方法。这种方法可以解决的一个广泛的问题是,观察者和被观察者之间相似性的哪些方面有助于运动仿真。由于人类已经在其他人类和动物之间进化,因此使用诸如机器人之类的人工刺激可以使我们探究我们的社会感知系统是否经过专门调整以处理其他生物实体。在这项研究中,我们使用了外观和动作与人体相似程度不同的类人机器人,以及肌电图(EMG)来测量参与者手臂中的肌肉活动,而他们要么观察到或模仿了三个特工的视频对他们右臂的动作。这些代理是人类(生物的外观和动作),机器人(机械的外观和动作)和机器人(生物的外观和动作)。当参与者模仿所有因素时,右臂肌肉活动增加。在模仿和观察期间,还发现固定臂的肌肉激活增加。此外,肌肉活动对运动动力学很敏感:模仿人类的活动明显强于两种机械作用。肌肉活动的动力学与刺激中的运动动力学之间也存在关系。总体而言,我们的数据表明,电机模拟不仅限于观察和模仿具有生物学外观的试剂,而且还可以用于机器人。但是,我们还在EMG反应中发现了对人体运动的敏感性。结合多种方法的数据,我们可以更全面地了解动作理解和潜在的神经计算。

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