Proceedings of the Annual Meeting of the Cognitive Science Society

Humans possess rich knowledge of the structure of the world, including co-occurrences among entities, and co-variation among their discrete and continuous features. But how people learn, infer and predict this structure is not wellunderstood. Here we explore everyday scene understanding as a case study of people’s structural knowledge and reasoning.We introduce a probabilistic model over scene graphs that can learn the relational structure of objects and their arrangementsand support inference and generation. Our model was able to learn the underlying structure of real-world scenes, and use it forinference and compression. In two human psychophysical experiments we found that a corresponding computational cognitivemodel was able to explain how people learn novel scene distributions and use it for classification and construction. Our workrepresents the first computational theory of human scene understanding that can account for people’s rich capacity for learningand reasoning about structure.