← 研踪 PaperTrailarXiv:2506.15190 · v4
Learning Reusable Behavioral Motifs for Continuous Animal Behavior Modeling
首次提交 2025-06-18 15:11
版本更新 2026-11-07 14:13
雷达观测 2026-11-10 11:14
摘要
Natural behavior evolves continuously and often reuses movement structure across contexts, but existing behavioral segmentation methods typically assign a single discrete syllable at each time point or impose restrictive generative assumptions. We introduce Motif-based Continuous Dynamics (MCD), a computational framework that models behavior as time-varying combinations of reusable behavioral motifs. MCD learns motifs as latent behavior-level features from transition dynamics, through spectral representation learning within a reinforcement learning framework. MCD then infers policies that combine these motifs across time or context, through offline imitation learning. This formulation avoids hard behavioral boundaries while capturing continuity and compositionality. Across simulated gridworld and continuous-control tasks, mouse maze navigation and pose data, MCD identifies reusable motifs that explain task and temporal variation. MCD therefore provides a flexible and interpretable framework for computational behavior analysis. Code is available in https://github.com/G1NO3/MCD.
作者备注：10 pages and 5 figures for the main text
观测到的版本
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v4 · 2026-11-07 14:13 · 最新观测版本
Learning Reusable Behavioral Motifs for Continuous Animal Behavior Modeling
Natural behavior evolves continuously and often reuses movement structure across contexts, but existing behavioral segmentation methods typically assign a single discrete syllable at each time point or impose restrictive generative assumptions. We introduce Motif-based Continuous Dynamics (MCD), a computational framework that models behavior as time-varying combinations of reusable behavioral motifs. MCD learns motifs as latent behavior-level features from transition dynamics, through spectral representation learning within a reinforcement learning framework. MCD then infers policies that combine these motifs across time or context, through offline imitation learning. This formulation avoids hard behavioral boundaries while capturing continuity and compositionality. Across simulated gridworld and continuous-control tasks, mouse maze navigation and pose data, MCD identifies reusable motifs that explain task and temporal variation. MCD therefore provides a flexible and interpretable framework for computational behavior analysis. Code is available in https://github.com/G1NO3/MCD.
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