Proceedings of the Annual Meeting of the Cognitive Science Society

A detailed model of the outside world is an essential ingredient of human cognition, enabling us to navigate, form goals, execute plans, and avoid danger. Critically, these world models are flexible—they can arbitrarily expand to introduce previously-undetected objects when new information suggests their presence. Although the number of possible undetected objects is theoretically infinite, people rapidly and accurately infer unseen objects in everyday situations. How? Here we investigate one approach to characterizing this behavior—as nonparametric clustering over low-level cues—and report preliminary results comparing a computational model to human physical inferences from real-world video.