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Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence
Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of music...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5078501/ https://www.ncbi.nlm.nih.gov/pubmed/27826221 http://dx.doi.org/10.3389/fnins.2016.00464 |
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author | Liang, Chun Earl, Brian Thompson, Ivy Whitaker, Kayla Cahn, Steven Xiang, Jing Fu, Qian-Jie Zhang, Fawen |
author_facet | Liang, Chun Earl, Brian Thompson, Ivy Whitaker, Kayla Cahn, Steven Xiang, Jing Fu, Qian-Jie Zhang, Fawen |
author_sort | Liang, Chun |
collection | PubMed |
description | Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of musician vs. non-musician difference in frequency change detection abilities. Methods: Twenty-four young normal hearing listeners (12 musicians and 12 non-musicians) participated. All participants underwent psychoacoustic frequency detection tests with three types of stimuli: tones (base frequency at 160 Hz) containing frequency changes (Stim 1), tones containing frequency changes masked by low-level noise (Stim 2), and tones containing frequency changes masked by high-level noise (Stim 3). The EEG data were recorded using tones (base frequency at 160 and 1200 Hz, respectively) containing different magnitudes of frequency changes (0, 5, and 50% changes, respectively). The late-latency evoked potential evoked by the onset of the tones (onset LAEP or N1-P2 complex) and that evoked by the frequency change contained in the tone (the acoustic change complex or ACC or N1′-P2′ complex) were analyzed. Results: Musicians significantly outperformed non-musicians in all stimulus conditions. The ACC and onset LAEP showed similarities and differences. Increasing the magnitude of frequency change resulted in increased ACC amplitudes. ACC measures were found to be significantly different between musicians (larger P2′ amplitude) and non-musicians for the base frequency of 160 Hz but not 1200 Hz. Although the peak amplitude in the onset LAEP appeared to be larger and latency shorter in musicians than in non-musicians, the difference did not reach statistical significance. The amplitude of the onset LAEP is significantly correlated with that of the ACC for the base frequency of 160 Hz. Conclusion: The present study demonstrated that musicians do perform better than non-musicians in detecting frequency changes in quiet and noisy conditions. The ACC and onset LAEP may involve different but overlapping neural mechanisms. Significance: This is the first study using the ACC to examine music-training effects. The ACC measures provide an objective tool for documenting musical training effects on frequency detection. |
format | Online Article Text |
id | pubmed-5078501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50785012016-11-08 Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence Liang, Chun Earl, Brian Thompson, Ivy Whitaker, Kayla Cahn, Steven Xiang, Jing Fu, Qian-Jie Zhang, Fawen Front Neurosci Neuroscience Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of musician vs. non-musician difference in frequency change detection abilities. Methods: Twenty-four young normal hearing listeners (12 musicians and 12 non-musicians) participated. All participants underwent psychoacoustic frequency detection tests with three types of stimuli: tones (base frequency at 160 Hz) containing frequency changes (Stim 1), tones containing frequency changes masked by low-level noise (Stim 2), and tones containing frequency changes masked by high-level noise (Stim 3). The EEG data were recorded using tones (base frequency at 160 and 1200 Hz, respectively) containing different magnitudes of frequency changes (0, 5, and 50% changes, respectively). The late-latency evoked potential evoked by the onset of the tones (onset LAEP or N1-P2 complex) and that evoked by the frequency change contained in the tone (the acoustic change complex or ACC or N1′-P2′ complex) were analyzed. Results: Musicians significantly outperformed non-musicians in all stimulus conditions. The ACC and onset LAEP showed similarities and differences. Increasing the magnitude of frequency change resulted in increased ACC amplitudes. ACC measures were found to be significantly different between musicians (larger P2′ amplitude) and non-musicians for the base frequency of 160 Hz but not 1200 Hz. Although the peak amplitude in the onset LAEP appeared to be larger and latency shorter in musicians than in non-musicians, the difference did not reach statistical significance. The amplitude of the onset LAEP is significantly correlated with that of the ACC for the base frequency of 160 Hz. Conclusion: The present study demonstrated that musicians do perform better than non-musicians in detecting frequency changes in quiet and noisy conditions. The ACC and onset LAEP may involve different but overlapping neural mechanisms. Significance: This is the first study using the ACC to examine music-training effects. The ACC measures provide an objective tool for documenting musical training effects on frequency detection. Frontiers Media S.A. 2016-10-25 /pmc/articles/PMC5078501/ /pubmed/27826221 http://dx.doi.org/10.3389/fnins.2016.00464 Text en Copyright © 2016 Liang, Earl, Thompson, Whitaker, Cahn, Xiang, Fu and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Liang, Chun Earl, Brian Thompson, Ivy Whitaker, Kayla Cahn, Steven Xiang, Jing Fu, Qian-Jie Zhang, Fawen Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence |
title | Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence |
title_full | Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence |
title_fullStr | Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence |
title_full_unstemmed | Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence |
title_short | Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence |
title_sort | musicians are better than non-musicians in frequency change detection: behavioral and electrophysiological evidence |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5078501/ https://www.ncbi.nlm.nih.gov/pubmed/27826221 http://dx.doi.org/10.3389/fnins.2016.00464 |
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