Understanding animal motion behavior is important for understanding how the brain organizes memory, decision-making, and goal-directed action. In our research, we study mouse navigation behavior using the Morris Water Maze (MWM), a widely used behavioral paradigm for investigating spatial learning and memory in rodents. While conventional measures such as escape latency and path length provide useful summaries of performance, they often do not fully capture the rich and dynamic nature of movement during navigation.
Our research focuses on understanding mouse motion behavior at a finer temporal scale. Instead of treating each trial as a single behavioral unit, we examine how navigation strategies evolve within a trial, since mice may shift among exploration, wall-following, scanning, circling, and more direct platform-oriented movement over time. These within-trial changes can offer deeper insight into learning processes, behavioral flexibility, and group differences that may be overlooked by aggregate metrics alone.
To support this goal, we develop an interpretable and lightweight computational framework for analyzing tracked trajectories from behavioral videos. Our approach analyzes motion continuously over time and identifies sub-trajectory-level navigational states. The pipeline first corrects for tracking irregularities through uniform resampling and smoothing, then derives geometry- and kinematics-based descriptors such as curvature, displacement, turning behavior, and target alignment. These features are mapped to human-readable behavioral categories through a hierarchical rule-based inference process, followed by temporal refinement to reduce fragmented or implausible label switching.
This work emphasizes interpretability and practical usability in neuroscience research. By providing point-wise annotations and visually verifiable outputs, the framework enables behavioral phenotyping and hypothesis-driven analysis of strategy transitions and learning dynamics.
