Humans are extremely proficient at perceiving natural scenes and understanding their contents. However, we know surprisingly little about how or even where in the brain we process the natural scenes. How is it, for instance, that the brain determines whether it is looking at beach or a city skyline? Work on this project is concerned with how we categorize natural scenes; that is, how do we process the kinds of images we encounter in everyday life as opposed to the discreet objects, oriented lines, or other impoverished stimuli typically used in the laboratory.
The representation of natural scenes is likely to simultaneously reside at a fine spatial scale (eg. neurons responsive to beaches versus cities may be interspersed with each other) and be distributed across the cortex (eg. scene categorization is unlikely to involve a single region of cortex), making it difficult to uncover with traditional neurophysiological methods. However, there has been a recent advance in the analysis of functional magnetic resonance imaging data (fMRI) that is particularly well suited to just such a situation: the application of statistical pattern recognition algorithms to fMRI data. Unlike traditional fMRI analysis that treats fMRI data as a collection of small but independent units, pattern recognition algorithms are designed to leverage activity patterns across the brain , making this an ideal method for studying the neural basis of natural scene categorization. We use these algorithms to ask what regions of the brain contain information relevant to scene category, as well as investigate how that information changes as a function of changes made in the natural scene images.