Mouse- and hand-tracking has become a popular method for studying the cognitive processes involved in a variety of domains, including language processing, memory functions, social cognition, and preferential and moral decision making, to name just a few. The popularity of mouse- and hand-tracking derives from its promise to provide a window into the evolution of cognitive processes with an unrivaled temporal resolution. This window is opened by linking characteristics of the movement, such as the shape of its trajectory, to characteristics of the underlying cognitive process. In current research, this means, more often than not, inferring the response competition created by different conditions or stimuli based on the curvature of aggregate movement trajectories. However, we will argue that this aggregate-level approach risks obscuring important trial-level variability in movement trajectories that can paint a different picture of the underlying cognitive process than the aggregate-level results do. Fortunately, mouse-tracking can do much more. In this chapter, we present a new approach to analyzing mouse-trajectories based on trajectory clustering that overcomes the limitations of aggregation-based analyses of movement trajectories.