Horn Lisa, Karsai Márton, Markova Gabriela
Department of Behavioral and Cognitive Biology University of Vienna Vienna Austria.
Department of Network and Data Science Central European University Vienna Austria.
Child Dev Perspect. 2024 Mar;18(1):36-43. doi: 10.1111/cdep.12495. Epub 2023 Dec 8.
Most children first enter social groups of peers in preschool. In this context, children use movement as a social tool, resulting in distinctive proximity patterns in space and synchrony with others over time. However, the social implications of children's movements with peers in space and time are difficult to determine due to the difficulty of acquiring reliable data during natural interactions. In this article, we review research demonstrating that proximity and synchrony are important indicators of affiliation among preschoolers and highlight challenges in this line of research. We then argue for the advantages of using wearable sensor technology and machine learning analytics to quantify social movement. This technological and analytical advancement provides an unprecedented view of complex social interactions among preschoolers in natural settings, and can help integrate young children's movements with others in space and time into a coherent interaction framework.
大多数儿童首次进入同龄人的社交群体是在幼儿园阶段。在这种背景下，儿童将动作作为一种社交工具，从而在空间上形成独特的接近模式，并随着时间推移与他人保持同步。然而，由于在自然互动过程中获取可靠数据存在困难，儿童与同龄人在空间和时间上的动作所具有的社会意义难以确定。在本文中，我们回顾了一些研究，这些研究表明接近度和同步性是学龄前儿童之间归属感的重要指标，并强调了这一研究领域中的挑战。然后，我们论证了使用可穿戴传感器技术和机器学习分析来量化社交动作的优势。这一技术和分析上的进步为自然环境中学龄前儿童之间复杂的社交互动提供了前所未有的视角，并且能够帮助将幼儿在空间和时间上与他人的动作整合到一个连贯的互动框架中。
