Proceedings of the Annual Meeting of the Cognitive Science Society

Human scene understanding involves not just localizing objects, but also inferring the latent causal properties that giverise to the scene for instance, how heavy those objects are. These properties can be guessed based on visual features(e.g., material texture), but we can also infer them from how they impact the dynamics of the scene. Furthermore, theseinferences are performed rapidly in response to dynamic, ongoing information. Here we propose a computational frame-work for understanding these inferences, and three models that instantiate this framework. We compare these models tothe evolution of human beliefs about object masses. We find that while peoples judgments are generally consistent withBayesian inference over these latent parameters, the models that best explain human judgments are approximations to thisinference that hold and dynamically update beliefs. An earlier version of this work was published in the proceedings ofCCN 2018 at https://ccneuro.org/2018/proceedings/1091.pdf.