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Mapping Brain Architecture and Processes
Supporting Experience Based Cognition
PI: Walter Schneider
Universityof Pittsburgh
Co-PIs: Greg Siegle, MarkWheeler and Kwan-Jin Jung
University
of Pittsburgh
Rainer Goebel and Elia Formisano
Maastricht University Netherlands
Tom Landauer and Peter Foltz
Pearson Knowledge Technologies
Daniel Levin
Vanderbilt University
This project will use modern brain imaging methods including functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging to map the structural and temporal aspects of human Experience Based Cognition (EBC) with sufficient detail to provide a foundation for guiding the development of computer EBC that can match human ability. Our research involves the following innovative claims; the imaging of human EBC will:
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Detail the brain mechanisms associated with representing multiple commonly experienced classes of information (i.e., “representation spaces”), the specialization of these representation spaces, the number of levels within each, and the interconnectivity of representation spaces subserving EBC.
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Determine the executive control networks and emotional regulation systems that moderate activity in representation spaces and determine the operations across a range of actions associated with EBC including input, recall, query, planning, and action processing.
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Develop technology to map the activity vector spaces to track human cognition in EBC. A goal will be to read brain activation patterns sufficiently to discriminate what a human is experiencing and the specialization and layers of representation supporting that coding. This will be done with sufficient resolution to predict which movie scene a human is experiencing.
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Develop data analysis (brain activity analysis, data mining) and representation techniques to code EBC and provide a database of activation patterns during EBC that can enable standardized high profile competitive challenge testing of discrimination methods of processing brain activation of human EBC.
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Model EBC in a hybrid architecture predicting activation patterns and the nature of control processing during video-based EBC, accounting for representation, executive control, and emotional coding of the information.
Note: More detailed descriptions available on request.
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