Abstract
Innovations in neuro-technology have created a potential gap in our ability to measure human performance and decision making in dynamic environments. Therefore, a need exists to create more reliable testing methodologies and data analytic solutions. The primary aim of this paper is to describe work to integrate subject matter expertise with algorithms designed to measure human brain activity in real time. Specifically, Guided Learning using constrained spectral partitioning to increase the reliability and interpretability of fMRI data is explicated and applied as a test case to the Default Mode Network in the elderly population. How Guided Learning can be further applied to other neuro-imaging technologies that may be more conducing to furthering the field of augmented cognition is discussed.
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Phillips, H.L., Walker, P.B., Kennedy, C.H., Carmichael, O., Davidson, I.N. (2013). Guided Learning Algorithms: An Application of Constrained Spectral Partitioning to Functional Magnetic Resonance Imaging (fMRI). In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. AC 2013. Lecture Notes in Computer Science(), vol 8027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39454-6_76
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DOI: https://doi.org/10.1007/978-3-642-39454-6_76
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