Department for Perception Cognition and Action, Max Planck Institute for Biological Cybernetics, Germany; Department of Psychology, FOM University, Augsburg, Germany.
Department for Perception Cognition and Action, Max Planck Institute for Biological Cybernetics, Germany.
Acta Psychol (Amst). 2020 Oct;210:103168. doi: 10.1016/j.actpsy.2020.103168. Epub 2020 Sep 9.
The goal of new adaptive technologies is to allow humans to interact with technical devices, such as robots, in natural ways akin to human interaction. Essential for achieving this goal, is the understanding of the factors that support natural interaction. Here, we examined whether human motor control is linked to the visual appearance of the interaction partner. Motor control theories consider kinematic-related information but not visual appearance as important for the control of motor movements (Flash & Hogan, 1985; Harris & Wolpert, 1998; Viviani & Terzuolo, 1982). We investigated the sensitivity of motor control to visual appearance during the execution of a social interaction, i.e. a high-five. In a novel mixed reality setup participants executed a high-five with a three-dimensional life-size human- or a robot-looking avatar. Our results demonstrate that movement trajectories and adjustments to perturbations depended on the visual appearance of the avatar despite both avatars carrying out identical movements. Moreover, two well-known motor theories (minimum jerk, two-thirds power law) better predict robot than human interaction trajectories. The dependence of motor control on the human likeness of the interaction partner suggests that different motor control principles might be at work in object and human directed interactions.
新的适应技术的目标是让人类能够以类似于人类互动的自然方式与技术设备(如机器人)进行交互。实现这一目标的关键是理解支持自然交互的因素。在这里,我们研究了人类运动控制是否与交互伙伴的视觉外观有关。运动控制理论认为运动学相关信息很重要,但视觉外观对于运动控制(Flash & Hogan,1985;Harris & Wolpert,1998;Viviani & Terzuolo,1982)来说并不重要。我们在执行社会互动(即击掌)时研究了运动控制对视觉外观的敏感性,即在混合现实设置中,参与者与三维真人大小的人形或机器人外观的化身进行击掌。我们的结果表明,尽管两个化身执行相同的动作,但运动轨迹和对干扰的调整取决于化身的视觉外观。此外,两个著名的运动理论(最小冲击,三分之二幂律)更好地预测了机器人而不是人类交互轨迹。运动控制对交互伙伴的拟人化程度的依赖表明,在物体和人类指向的交互中可能存在不同的运动控制原则。