Scene statistics: neural representation of real-world structure in rapid visual perception

How does the brain represent our visual environment? Research has revealed brain areas that respond to specific information such as faces and objects, but how a representation of an entire visual scene is formed is still unclear. This thesis explores the idea that scene statistics play an important role in the formation of neural representations of real-world scenes. Scene statistics are statistical regularities resulting from the physical laws that govern our environment. The main hypothesis is that the brain uses these statistical regularities to rapidly categorize scenes. To test this hypothesis, we used an interdisciplinary approach, integrating methods from psychology, neuroscience and informatics (computer vision). In five chapters, we describe a total of 10 experiments in which we tested computational models of scene statistics against human behavioral and brain data obtained with electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI). Our results show that the visual system exhibits strong sensitivity to scene statistics. Focusing in particular on the temporal dynamics of this neural sensitivity, we examined in depth how scene statistics shape neural representations. We demonstrate a potential role for scene statistics in perceived similarity of naturalistic textures, scene gist perception and object recognition. Overall, the results suggest that visual cortex might compute scene statistics in order to estimate the degree of coherence, i.e. the amount of organized structure vs. chaos present in the visual input, and use this information for rapid scene categorization, or to dynamically adjust its processing of other visual information, such as objects.