Top-down and bottom-up processes of scene categorization

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DESCRIPTION (provided by applicant): One of the great challenges of visual neuroscience is to reveal the neural mechanisms defining our perceptual experience. The human visual system is extremely efficient in categorizing complex scenes, even for novel ones never encountered before and often under poor viewing conditions such as brief exposure duration and dim lighting. What are the underlying neural operations that subserve such proficient scene processing? Our visual experience is not only determined by the sensory input, but also influenced by top- down cognitive mechanisms such as attentional biases and expectations. For example, in the midst of continual overflow of information, prior expectation about the visual world can facilitate recognition by biasing the relevant bottom-up input thereby allowing us to quickly deduce plausible interpretations. When anticipating a particular category of a scene (e.g., a city scene), the prior knowledge about the possible configuration or the objects typically found in that scene may be used to form attentional templates, or the internal representation of scenes, allowing for efficient categorization. However, the neural basis of this interactive relationship between top-down and bottom-up processing in scene perception is largely unknown. The main objective of the proposed research is in bridging the current gap between bottom-up and top-down neural processes of scene categorization. The central hypothesis is that the statistical regularities of scenes--defined in image statistics-- that are consistent acros scenes of a particular category (e.g., different city scenes) serve as effective top- down biasing signals (or category templates) in guiding efficient categorization. We will examine the neural mechanisms of scene perception in normal and object agnostic individuals, those who have severe difficulty recognizing objects due to brain damage in the ventral visual system. By using innovative functional magnetic resonance imaging (fMRI) and behavioral experiments and applying sophisticated fMRI analysis techniques, the proposed research will address the following specific aims: (1) characterize the neural representation of image statistics of scenes within and across categories, (2) investigate the neural basis of scene category template formation and (3) investigate scene processing in patients with visual agnosia. By examining scene categorization in the framework of attentional influences, the proposed research aims to elucidate the complex interaction between bottom-up and top-down processes on scene recognition, an ecologically valid but currently underexplored area of research. The innovative strategy of simultaneously studying normal and lesioned brain functions has the potential to significantly advance our knowledge about normal scene perception as well as potentially reveal alternative mechanisms for scene perception when object information is not available. Overall, a better understanding of the neural mechanisms of scene processing and its relation to attentional processes will provide direct translational implications for clinical population with a wide range of visual and attention disorders.