Learning Reusable Motor Motifs for Continuous Animal Behavior Modeling

Animals generate complex behaviors by flexibly recombining a finite set of motor primitives, but existing behavior segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. Here, we introduce Motif-based Continuous Dynamics (MCD), a framework that models behavior as a continuous, compositional process driven by reusable motor motifs. MCD leverages reinforcement learning (RL) to (1) discover interpretable motif representations via transition-based representation learning, and (2) model behavior as a time-varying mixture of these motifs through motif-based policies. This formulation avoids restrictive dynamics assumptions while capturing continuity, compositionality, and long-term dependencies in behavior. Across simulated gridworld tasks, maze navigation, and animal behavior datasets, MCD identifies reusable motifs and interprets the trajectories with them. These results provide a generative account of behavior as combinations of fundamental motifs, offering a flexible and interpretable framework for studying natural behavior.