Fundamentals of Music Perception in the Human Brain: Insight from Electrophysiological Recordings
- Author(s): Foo, Francine Min Hui
- Advisor(s): Knight, Robert T
- et al.
Music perception has fascinated neuroscientists and psychologists for over a century. Ever since the advent of neuroimaging techniques in the second half of the 20th century, the scientific community has benefitted from a wealth of research studying the cortical mechanisms that facilitate one of the basic forms of human cognition. While hemodynamic techniques and lesion studies provide insight to the cortical structures implicated in distinct aspects of music perception, non-invasive electrophysiological equipment provides the temporal resolution necessary to track the processing of music. However, observations are usually constrained to either the spatial or temporal domain due to the limitations of each experimental method. As a result, the combined spatial and temporal dynamics of both sensory and cognitive processing of musical stimuli remain largely unknown. Intracranial recordings in neurosurgical patients have high spatial and temporal resolution, thereby providing a means to investigate some of the many unanswered questions in the music neuroscience literature. As few music perception studies to date have tapped on this valuable resource, this dissertation provides one of the first attempts at utilizing both electrocorticography (ECoG) and EEG to understand the fine-grained cortical dynamics of auditory stimulus processing and music perception at the fundamental level. Specifically, the studies reveal that: 1) the superior temporal gyrus exhibits differential processing of consonance and dissonance at a spatial resolution of 1cm; 2) the ventral temporal cortex, conventionally involved in visual recognition and categorization, exhibits neural representations of auditory target identification; 3) selective attention to either pitch direction changes or the consonant/dissonant properties of musical chords implicates a wide range of cortical networks that scale with task complexity.