Summary data from Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish

Understanding the emergence of collective behaviour has long been a key research focus in the natural sciences. Besides the fundamental role of social interaction rules, a combination of theoretical and empirical work indicates individual speed may be a key process that drives the collective behaviour of animal groups. Socially induced changes in speed by interacting animals make it difficult to isolate the effects of individual speed on group-level behaviours. Here, we tackled this issue by pairing guppies with a biomimetic robot. We used a closed-loop tracking and feedback system to let a robotic fish naturally interact with a live partner in real time, and programmed it to strongly copy and follow its partner's movements while lacking any preferred movement speed or directionality of its own. We show that individual differences in guppies' movement speed were highly repeatable and shaped key collective patterns: higher individual speeds resulted in stronger leadership, lower cohesion, higher alignment and better temporal coordination in the pairs. By combining the strengths of individual-based models and observational work with state-of-the-art robotics, we provide novel evidence that individual speed is a key, fundamental process in the emergence of collective behaviour.

长期以来，解析集体行为（collective behaviour）的涌现机制一直是自然科学领域的核心研究议题。除社会互动规则所发挥的基础作用外，多项理论与实证研究均表明，个体运动速度或许是驱动动物群体集体行为的关键过程。互动个体因社交产生的速度变化，会令研究者难以分离个体速度对群体层面行为的影响效应。为此，本研究通过将孔雀鱼与仿生机器人（biomimetic robot）配对以解决这一难题。我们搭建了闭环追踪与反馈系统，使机器鱼（robotic fish）能够与活的同伴实现实时自然交互，并对其进行编程，使其精准复刻并跟随同伴的运动轨迹，自身则无预设的运动速度偏好与运动方向性。研究结果显示，孔雀鱼个体的运动速度差异具有高度可重复性，并塑造了关键的集体行为模式：在该配对组中，个体速度越高，领头性越强、群体内聚性越低、方向一致性越高，且时间协调性越好。本研究结合基于个体的模型（individual-based models）、观测研究与顶尖机器人技术的优势，为个体速度是集体行为涌现过程中的核心基础过程提供了全新实证证据。