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Segmenting and Predicting Musical Phrase Structure Exploits Neural Gain Modulation and Phase Precession.
- Source :
-
Journal of Neuroscience . 7/24/2024, Vol. 44 Issue 30, p1-17. 17p. - Publication Year :
- 2024
-
Abstract
- Music, like spoken language, is often characterized by hierarchically organized structure. Previous experiments have shown neural tracking of notes and beats, but little work touches on the more abstract question: how does the brain establish high-level musical structures in real time? We presented Bach chorales to participants (20 females and 9 males) undergoing electroencephalogram (EEG) recording to investigate how the brain tracks musical phrases. We removed the main temporal cues to phrasal structures, so that listeners could only rely on harmonic information to parse a continuous musical stream. Phrasal structures were disrupted by locally or globally reversing the harmonic progression, so that our observations on the original music could be controlled and compared. We first replicated the findings on neural tracking of musical notes and beats, substantiating the positive correlation between musical training and neural tracking. Critically, we discovered a neural signature in the frequency range ~0.1 Hz (modulations of EEG power) that reliably tracks musical phrasal structure. Next, we developed an approach to quantify the phrasal phase precession of the EEG power, revealing that phrase tracking is indeed an operation of active segmentation involving predictive processes. We demonstrate that the brain establishes complex musical structures online over long timescales (>5 s) and actively segments continuous music streams in a manner comparable to language processing. These two neural signatures, phrase tracking and phrasal phase precession, provide new conceptual and technical tools to study the processes underpinning high-level structure building using noninvasive recording techniques. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02706474
- Volume :
- 44
- Issue :
- 30
- Database :
- Academic Search Index
- Journal :
- Journal of Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 178706060
- Full Text :
- https://doi.org/10.1523/JNEUROSCI.1331-23.2024