A global framework for scene gist

Abstract

Human observers are able to rapidly and accurately categorize natural scenes, but the representation mediating this feat is still unknown. Here we propose a framework of rapid scene categorization that does not segment a scene into objects and instead uses a vocabulary of global, ecological properties that describe spatial and functional aspects of scene space (such as navigability or mean depth). In Chapter 1, four experiments explore the human sensitivity to global properties for rapid scene categorization, as well as the computational sufficiency of these properties for predicting scene categories. Chapter 2 explores the time course of scene understanding, finding that global properties can be perceived with less image exposure than the computation of a scene's basic-level category. Finally, in Chapter 3, I explore aftereffects to adaptation to global properties, showing that repeated exposure to many global properties produces robust high-level aftereffects, thus providing evidence for the neural coding of these properties. Altogether, these results provide support for the hypothesis that rapid categorization of natural scenes may not be mediated primarily though objects and parts, but also through global properties of structure and affordance.