Melodies in space: neural processing of musical features
2013-03
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Melodies in space: neural processing of musical features
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2013-03
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With modern digital technology, it is now possible to capture, store and describe the brain's response to musical stimuli with some degree of confidence. Increasing financial and materiel resources are being made available to music-brain researchers and, as a result, the number of music perception and cognition publications is expanding exponentially (Levitin & Tirovolas, 2011). Many neuro-musicologists have access to at least one of the two most popular brain-imaging technologies, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG measures the electric brain signal passing through soft tissues, which becomes 'smeared' and difficult to separate from signals measured elsewhere on the scalp. fMRI measure the blood-oxygen level dependent (BOLD) signal, however this signal develops too slowly (2-5 seconds) to accurately capture the brain's swift processing of individual melodic notes. In contrast, magneto-encephalography (MEG) affords high temporal resolution (1 ms) and high fidelity (i.e. the clean, direct measurement of undistorted electromagnetic fluctuations in neural populations) and is therefore the most suitable method for matching the brain's dynamic, interacting sub-networks to the processing of melodies played at normal tempos. To explore my idea that this evolving process is both observable and quantifiable, I have performed a series of MEG experiments involving human subjects listening to melodic stimuli. This dissertation details my examination of the brain's response to melodic pitch, contour, interval distance and next-note probability.
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University of Minnesota Ph.D. dissertation. March 2013. Major:Neuroscience. Advisors: Dr. Apostolos P. Georgopoulos, Dr. Scott D. Lipscomb. 1 computer file (PDF); xiii, 90 pages, appendices A-B.
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Dumas, Roger Edward. (2013). Melodies in space: neural processing of musical features. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/155664.
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