Dynamic task dependent decoding of visual motion information during continuous behavior

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In order to act and react in our daily lives, we have to continuously integrate information from our sensory systems to solve a broad range of constantly changing tasks. Despite tremendous insights into the paths in which neural information is integrated, little is known how the required flexibility in the decoding of the visual information to solve complex tasks in natural scenarios is achieved. In the laboratory, such attentive properties are typically investigated in isolation with discrete decisions being made. I aim to overcome these limitations using a set of innovative experiments and analyses. I will record data during continuous and naturalistic behavior and stimulation at three complementary scales of neural processing: simultaneous single cell responses within areas MT and MST of the macaque brain, large scale cortical circuits (human fMRI) and the resultant behavior. I aim to apply a common statistical model to this data that will provide rigorous insights into the neural processes. The project will lay the groundwork for future research in which neuroscientific data can be collected and analyzed in complex, natural environments. This will allow understanding neural computations in continuous natural behavior and reduce current constraints imposed on the subjects and especially animals under research.