In this paper, we bring together research on active learning and intuitive physics to explore how people learn about "microworlds" with continuous spatiotemporal dynamics. Participants interacted with objects in simple two-dimensional worlds governed by a physics simulator, with the goal of identifying latent physical properties such as mass, and forces of attraction or repulsion. We find an advantage for active learners over passive and yoked controls. Active participants spontaneously performed several kinds of "natural experiments" which reveal the objects' properties with varying success. While yoked participants' judgments were affected by the quality of the active participant they observed, they did not share the learning advantage, performing no better than passive controls overall. We discuss possible explanations for the divergence between active and yoked learners, and outline further steps to categorize and explore active learning in the wild.