用连续混合方式发现动物行为的通用运动模式
Learning Task-Agnostic Motifs to Capture the Continuous Nature of Animal Behavior
- 基于行为转换结构发现可解释的运动基元
- 模型能捕捉行为随时间连续演变的动态组合
- 适用于多任务、自由运动等复杂场景
动物通过灵活重组有限的核心运动模式来应对多样任务需求，但现有行为分割方法在严格生成假设下将行为划分为离散单元，简化了这一过程。为更好捕捉行为生成的连续性，我们提出基于运动基元的连续动力学（MCD）发现框架：（1）利用行为转换结构表示，识别可解释的运动基元作为行为的潜在基函数；（2）将行为动态建模为这些基元的连续混合。我们在多任务网格世界、迷宫导航任务和自由运动动物行为数据上验证了MCD。结果表明，该方法能识别可复用的基元组件，捕捉连续组合动态，并生成传统离散分割模型无法实现的逼真轨迹。本方法为复杂动物行为如何由基本运动模式动态组合产生提供了生成性解释，推动了自然行为的量化研究。
原文摘要 · Abstract (English)
Animals flexibly recombine a finite set of core motor motifs to meet diverse task demands, but existing behavior segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. To better capture the continuous structure of behavior generation, we introduce motif-based continuous dynamics (MCD) discovery, a framework that (1) uncovers interpretable motif sets as latent basis functions of behavior by leveraging representations of behavioral transition structure, and (2) models behavioral dynamics as continuously evolving mixtures of these motifs. We validate MCD on a multi-task gridworld, a labyrinth navigation task, and freely moving animal behavior. Across settings, it identifies reusable motif components, captures continuous compositional dynamics, and generates realistic trajectories beyond the capabilities of traditional discrete segmentation models. By providing a generative account of how complex animal behaviors emerge from dynamic combinations of fundamental motor motifs, our approach advances the quantitative study of natural behavior.
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