Probing neural mechanisms of music perception, cognition, and performance using multivariate decoding

Recent neuroscience research has shown increasing use of multivariate decoding methods and machine learning. These methods, by uncovering the source and nature of informative variance in large data sets, invert the classical direction of inference that attempts to explain brain activity from mental state variables or stimulus features. However, these techniques are not yet commonly used among music researchers. In this position article, we introduce some key features of machine learning methods and review their use in the field of cognitive and behavioral neuroscience of music. We argue for the great potential of these methods in decoding multiple data types, specifically audio waveforms, electroen- cephalography, functional MRI, and motion capture data. By finding the most informative aspects of stimulus and performance data, hypotheses can be generated pertaining to how the brain processes incoming musical information and generates behavioral output, respectively. Importantly, these methods are also applicable to different neural and physiological data types such as magnetoencephalography, near-infrared spectroscopy, positron emission tomography, and electromyography.

Rebecca S. Schaefer, Shinichi Furuya, Leigh M. Smith, Blair Bohannan Kaneshiro and Petri Toiviainen

Psychomusicology: Music, Mind and Brain, 22(2):168–174, 2012

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