Proceedings of the Annual Meeting of the Cognitive Science Society

People are adept at perceiving interactions from movementsof simple shapes but the underlying mechanism remains un-known. Previous studies have often used object movementsdefined by experimenters. The present study used aerial videosrecorded by drones in a real-life environment to generate de-contextualized motion stimuli. Motion trajectories of dis-played elements were the only visual input. We measuredhuman judgments of interactiveness between two moving el-ements, and the dynamic change of such judgments over time.A hierarchical model was developed to account for human per-formance in this task, which represents interactivity using la-tent variables, and learns the distribution of critical movementfeatures that signal potential interactivity. The model providesa good fit to human judgments and can also be generalized tothe original Heider-Simmel animations (1944). The model canalso synthesize decontextualized animations with controlleddegree of interactiveness, providing a viable tool for studyinganimacy and social perception.