University of Shanghai for Science and Technology, Shanghai, China.
Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse-Paul Sabatier (UPS), Toulouse, France.
PLoS Comput Biol. 2020 Mar 16;16(3):e1007194. doi: 10.1371/journal.pcbi.1007194. eCollection 2020 Mar.
Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their motion. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based agent model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably, and even without confinement, we find that groups remain cohesive and polarized when each agent interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent, dubbed as the "most influential neighbor". However, group cohesion is lost when each agent only interacts with its nearest neighbor. We then investigate by means of a robotic platform the collective motion in groups of five robots. Our platform combines the implementation of the fish behavioral model and a control system to deal with real-world physical constraints. A better agreement with experimental results for fish is obtained for groups of robots only interacting with their most influential neighbor, than for robots interacting with one or even two nearest neighbors. Finally, we discuss the biological and cognitive relevance of the notion of "most influential neighbors". Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups.
鱼群的协调运动和集体决策是个体整合其邻居行为信息的复杂相互作用的结果。然而，个体如何整合这些信息做出决策并控制自己的运动仍然知之甚少。在这里，我们结合实验、计算和机器人方法，研究了鱼与邻居相互作用的不同策略对群体中霓虹脂鲤（Hemigrammus rhodostomus）集体游动的影响。通过描述 H. rhodostomus 个体之间相互作用的基于数据的代理模型（Calovi 等人，2018 年），我们表明，仅添加与两个邻居的成对相互作用，就可以定量再现五鱼群体中观察到的集体行为。增加相互作用的邻居数量不会显著改善模拟结果。值得注意的是，即使没有约束，当每个个体仅与一个邻居相互作用时，即与对焦点个体的游动方向变化贡献最大的那个邻居相互作用时，群体仍然保持凝聚力和极化，我们将这个邻居称为“最有影响力的邻居”。然而，当每个个体仅与最近的邻居相互作用时，群体的凝聚力就会丧失。然后，我们通过一个五机器人的机器人平台来研究群体的集体运动。我们的平台结合了鱼类行为模型的实现和控制系统，以应对现实世界的物理约束。与仅与最有影响力的邻居相互作用的机器人群体相比，与一个或甚至两个最近邻居相互作用的机器人群体，与实验结果的吻合度更好。最后，我们讨论了“最有影响力的邻居”这一概念的生物学和认知相关性。总体而言，我们的结果表明，当鱼群游动时，它们只需要获取关于环境的最小信息量即可协调它们的运动。
