A Mechanistic Model for Collective Motion from Sensorimotor Regularities

2026-05-15

AI Summary

Traditional self-propelled particle models struggle to elucidate the generative mechanisms of collective behavior due to their neglect of individual perceptual and cognitive processes. This work proposes a robotics-inspired embodied agent model in which individuals estimate neighbors’ states using only angular bearing and visual size cues within a limited field of view, and select actions by optimizing a desired social distance via gradient descent—without predefined interaction forces. For the first time, this approach grounds collective behavior modeling in empirically measurable biological sensing and motor constraints, framing emergent group dynamics as the outcome of coupled perception, action, and environmental interaction. The model successfully reproduces polarized motion, ring rotation, and group splitting, with sensitivity analyses revealing that transitions between these states are governed by biologically quantifiable parameters such as visual field geometry, sensory noise, turning agility, and memory, thereby offering a mechanistic explanation for interspecies behavioral differences.

Abstract

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.

Problem

• collective motion
• mechanistic model
• sensorimotor
• self-propelled particles
• emergent behavior

Innovation

• sensorimotor modeling
• collective motion
• mechanistic model
• emergent behavior
• embodiment