发表机构
Department of Collective Behaviour, Max Planck Institute of Animal Behavior（动物行为学集体行为系，马克斯·普朗克动物行为研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于鱼群集体感知模型，提出集体感知可视为涌现的贝叶斯推断，通过局部规则实现群体层面的近似推断，为集体行为与贝叶斯力学建立定量桥梁。
AI 中文摘要
动物群体的集体行为通常用认知语言来描述：鱼群感知梯度，群体计算，群体决策。这些表述往往未阐明群体在推断什么、所表示的计算对象是什么，或者这种表示如何从微观交互中产生。在此，我们基于鱼群集体感知的经典模型（Berdahl等人，2013）在模拟中定量地发展了这一描述，在该模型中，集体梯度感知从本身不估计梯度的个体中涌现：个体仅根据局部光照强度调节其速度，并对邻居的简单社会力做出响应。我们表明，这些局部规则允许在群体层面进行介观贝叶斯重新解释，并假设集体动力学在黑暗梯度如何驱动个体间速度差异的生成模型上实现近似推断。由此产生的群体层面推断过程产生关于局部黑暗梯度的后验分布，其统计量随环境结构变化，并与集体运动和感知性能系统相关。因此，在贝叶斯力学意义上，群体可以被描述为分布式推断系统，而无需个体代理自身执行概率信念更新。这为集体感知作为涌现推断提供了定量、可证伪的解释，并为从集体行为到贝叶斯力学架起了一座具体的桥梁。
英文摘要
Collective behavior in animal groups is routinely described in cognitive language: fish schools sense gradients, swarms compute, colonies decide. These formulations often leave open what the collective is inferring, what computational object is represented, or how that representation arises from microscopic interactions. Here we develop that description quantitatively in a simulation based on the canonical model of collective sensing in fish schools (Berdahl et al., 2013), in which collective gradient sensing emerges from individuals that do not estimate gradients themselves: agents only modulate their speed in response to local light intensity and respond to simple social forces from neighbors. We show that these local rules admit a mesoscopic Bayesian reinterpretation at the level of the school, and hypothesize that the collective dynamics realize approximate inference on a generative model of how darkness gradients drive inter-individual speed differences. The resulting school-level inference process yields a posterior distribution over the local darkness gradient, whose statistics vary with environmental structure and relate systematically to collective motion and sensing performance. The school can therefore be described, in a Bayesian-mechanical sense, as a distributed inferential system without requiring individual agents to perform probabilistic belief updates themselves. This yields a quantitative, falsifiable account of collective sensing as emergent inference, and a concrete bridge from collective behavior to Bayesian mechanics.
CommentsAccepted for publication in the proceedings of the 7th International Workshop on Active Inference (IWAI 2026, Madrid). Author's accepted manuscript. 26 pages, 12 figures, 1 table; includes appendices
