Learning Task-Agnostic Motifs to Capture the Continuous Nature of Animal Behavior
This addresses the need for better quantitative study of natural animal behavior by researchers in neuroscience and robotics, though it is incremental as it builds on existing motif-based approaches with a continuous modeling twist.
The paper tackled the problem of oversimplifying animal behavior segmentation by existing methods that impose discrete syllables, introducing motif-based continuous dynamics (MCD) discovery to capture continuous structure; it validated MCD on tasks like gridworld and animal behavior, showing it identifies reusable motifs and generates realistic trajectories beyond traditional models.
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.