Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands; Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.
Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.
Neuropsychologia. 2021 Jul 16;157:107853. doi: 10.1016/j.neuropsychologia.2021.107853. Epub 2021 Apr 21.
Despite the increase in interactions between children and robots, our understanding of children's neural processing of robotic movements is limited. The current study theorized that motor resonance hinges on the agency of an actor: its ability to perform actions volitionally. As one of the first studies with a cross-sectional sample of preschoolers and older children and with a specific focus on robotic action (rather than abstract non-human action), the current study investigated whether the perceived agency of a robot moderated children's motor resonance for robotic movements, and whether this changed with age. Motor resonance was measured using electroencephalography (EEG) by assessing mu power while 4 and 8-year-olds observed actions performed by agentic versus non-agentic robots and humans. Results show that older children resonated more strongly with non-agentic than agentic robotic or human movement, while no such differences were found for preschoolers. This outcome is discussed in terms of a predictive coding account of motor resonance. Importantly, these findings contribute to the existing set of studies on this topic by showing that, while keeping all kinematic information constant, there is a clear developmental difference in how children process robotic movement depending on the level of agency of a robot.
尽管儿童与机器人之间的互动有所增加，但我们对儿童对机器人运动的神经处理的理解是有限的。本研究理论认为，运动共鸣取决于行为者的代理权：其自愿执行动作的能力。作为第一项具有学龄前儿童和年龄较大儿童的横截面样本的研究之一，并且特别关注机器人动作（而不是抽象的非人类动作），本研究调查了机器人的感知代理权是否调节了儿童对机器人动作的运动共鸣，以及这种变化是否随年龄而变化。通过评估运动时的 mu 功率，使用脑电图（EEG）来测量运动共鸣，而 4 岁和 8 岁的儿童观察由代理机器人和人类与非代理机器人和人类执行的动作。结果表明，年龄较大的儿童对非代理机器人或人类运动的共鸣更强，而学龄前儿童则没有发现这种差异。根据运动共鸣的预测编码理论，对这一结果进行了讨论。重要的是，这些发现通过表明，在保持所有运动信息不变的情况下，根据机器人的代理级别，儿童处理机器人运动的方式存在明显的发展差异，从而为该主题的现有研究做出了贡献。
