Attentional selection from natural scenes

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One of the great challenges of cognitive neuroscience is to reveal the neural mechanisms underlying perceptual and cognitive processes that are utilized under naturalistic conditions. Selecting an object from a cluttered environment such as a natural scene presents a particularly complicated problem, since the exact location of the object is often unknown, and an object has an almost infinite number of visual appearances. Despite these challenges, the visual system has an extraordinary capability to extract categorical information quickly and efficiently from natural scenes (e.g. detecting cars when crossing a street). However, little is known about the neural mechanisms related to such real-world search. Dr. Sabine Kastner, Princeton University, will use complementary methodological approaches, including psychophysics, functional magnetic resonance imaging (fMRI), and electrocorticography (ECoG) to address this gap by performing a series of three studies. Based on her recent findings, she hypothesizes that categorical selection from natural scenes is mediated through the formation of category-specific search templates, which are instantiated in visual cortex to facilitate processing of matching objects, but are generated through interactions with higher-order cortex. The goal of the project is to characterize such attentional search templates with respect to (i) their source, (ii) their temporal dynamics, and (iii) their featural or semantic content.The selection of relevant information from cluttered natural environments for further processing is one of the most fundamental cognitive abilities for guiding behavior. This becomes strikingly clear when the attentional selection mechanisms fail, such as in individuals afflicted with attention-deficit disorder (ADHD), visuo-spatial hemineglect, which is often observed following stroke, and schizophrenia. Therefore, progress in understanding the neural mechanisms underlying attentional selection is essential. The project will shed light specifically on how we select visual information under the endlessly variable conditions of our natural environments, thereby transforming this area of research from laboratory conditions to the real world. Dr. Kastner's research program offers training opportunities for students at all levels of education including high school students. The project will also serve Dr. Kastner's continued outreach to K-12 science education through lectures and teacher collaboration as well her efforts to foster the early careers of female scientists. Finally, Dr. Kastner will participate in the big data sharing effort by making the data available to support efforts to make use of real data in the teaching of STEM-related courses and to enable participation in discovery science by those who would otherwise have no access to such data.