Modeling Fly Social Behavior with Agent-Centric Pose Forecasting

Understanding animal behavior at an algorithmic level – what animals attend to, how they form internal representations, and how this maps to action – remains a central challenge in neuroethology. We explore how well large generative models capture properties of fly social behavior. We combine transformers with agent-centric representations. Models input egocentric sensory observations and output egocentric movements, mirroring biological constraints. Social behavior emerges from agents independently sensing and responding to one another. We demonstrate that this simple model captures many properties of fly behavior, outperforming the world-centric representation and achieving SOTA on MABe 2022-fly. We systematically evaluate properties of the representation important for capturing the behavior distribution through a series of lesioning experiments. These comparisons were enabled by a new, general-purpose library for animal-centric pose forecasting.