Collective behavior in animals has long been modeled through self-propelled particle models, which reproduce striking group-level phenomena through abstract interaction forces. Yet these models are fundamentally descriptive: they leave open the question of how collective behavior is actually produced. Recent empirical work makes this gap concrete: locusts do not align with neighbors, sensory and cognitive mechanisms mediate interaction instead. A mechanistic model must therefore operate at the sensorimotor level, grounded in what individual organisms can actually perceive, estimate, and physically execute. We present such a model based on a modeling framework from robotics, extended here to collective motion. Each agent perceives neighbors through bearing and apparent-size cues within a limited field of view, maintains uncertain internal state estimates, and selects actions through gradient descent on a desired social distance -- without any prescribed interaction forces. This simple model produces diverse collective behaviors including polarized motion, milling, ring formations, and subgroup fragmentation. A global sensitivity analysis shows that behavioral transitions are governed by sensorimotor parameters corresponding to measurable biological quantities: field of view geometry, sensory noise, turning agility, and memory. Collective behavior can therefore be understood as the emergent outcome of interacting sensorimotor regularities, and differences across species as the emergent outcome of differences in embodiment and environment.

翻译：动物集体行为长期通过自推进粒子模型进行建模，这些模型通过抽象相互作用力重现显著的群体层面现象。然而，这些模型本质上是描述性的：它们未能回答集体行为如何实际产生的问题。最近的实验研究使这一差距具体化：蝗虫并不与相邻个体对齐，感官与认知机制反而起到中介交互作用。因此，机制模型必须基于感知运动层面运作，植根于个体生物实际能感知、估计和物理执行的内容。我们提出了这样一个模型，该模型基于机器人学的建模框架并扩展至集体运动场景。每个智能体通过有限视场内的方位角和表观尺寸线索感知相邻个体，维持不确定的内部状态估计，并通过期望社交距离的梯度下降法选择行动——无需预设任何相互作用力。这一简单模型能产生多样化的集体行为，包括偏振运动、绕圈运动、环状结构及子群分裂。全局敏感性分析表明，行为转变由可量化的生物参数对应的感知运动参数所支配：视场几何结构、感官噪声、转向敏捷性和记忆。因此，集体行为可被理解为相互作用感知运动规律涌现的结果，而物种间的行为差异则可视为身体结构与生态环境差异的涌现结果。