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  Neural processing of poems and songs is based on melodic properties

Scharinger, M., Knoop, C. A., Wagner, V., & Menninghaus, W. (2022). Neural processing of poems and songs is based on melodic properties. NeuroImage, 257: 119310. doi:10.1016/j.neuroimage.2022.119310.

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Neural processing of poems and songs is based on melodic properties.pdf (Publisher version), 3MB
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Neural processing of poems and songs is based on melodic properties.pdf
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© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license

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 Creators:
Scharinger, Mathias1, 2, 3, Author           
Knoop, Christine A.1, 4, Author           
Wagner, Valentin1, 5, Author           
Menninghaus, Winfried1, Author           
Affiliations:
1Department of Language and Literature, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421695              
2Research Group Phonetics, Institute of German Linguistics, Philipps-University Marburg, Pilgrimstein 16, Marburg 35032, Germany, ou_persistent22              
3Center for Mind, Brain and Behavior, Universities of Marburg and Gießen, Germany, ou_persistent22              
4Department of Music, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421696              
5Experimental Psychology Unit, Helmut Schmidt University / University of the Federal Armed Forces , Hamburg, Germany, ou_persistent22              

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Free keywords: Speech, Music, Melody, fMRI, Processing, Temporal cortex, Default-mode network, Aesthetics
 Abstract: The neural processing of speech and music is still a matter of debate. A long tradition that assumes shared processing capacities for the two domains contrasts with views that assume domain-specific processing. We here contribute to this topic by investigating, in a functional magnetic imaging (fMRI) study, ecologically valid stimuli that are identical in wording and differ only in that one group is typically spoken (or silently read), whereas the other is sung: poems and their respective musical settings. We focus on the melodic properties of spoken poems and their sung musical counterparts by looking at proportions of significant autocorrelations (PSA) based on pitch values extracted from their recordings. Following earlier studies, we assumed a bias of poem-processing towards the left and a bias for song-processing on the right hemisphere. Furthermore, PSA values of poems and songs were expected to explain variance in left- vs. right-temporal brain areas, while continuous liking ratings obtained in the scanner should modulate activity in the reward network. Overall, poem processing compared to song processing relied on left temporal regions, including the superior temporal gyrus, whereas song processing compared to poem processing recruited more right temporal areas, including Heschl's gyrus and the superior temporal gyrus. PSA values co-varied with activation in bilateral temporal regions for poems, and in right-dominant fronto-temporal regions for songs. Continuous liking ratings were correlated with activity in the default mode network for both poems and songs. The pattern of results suggests that the neural processing of poems and their musical settings is based on their melodic properties, supported by bilateral temporal auditory areas and an additional right fronto-temporal network known to be implicated in the processing of melodies in songs. These findings take a middle ground in providing evidence for specific processing circuits for speech and music in the left and right hemisphere, but simultaneously for shared processing of melodic aspects of both poems and their musical settings in the right temporal cortex. Thus, we demonstrate the neurobiological plausibility of assuming the importance of melodic properties in spoken and sung aesthetic language alike, along with the involvement of the default mode network in the aesthetic appreciation of these properties.

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Language(s): eng - English
 Dates: 2021-12-232022-05-112022-05-13
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2022.119310
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Title: NeuroImage
Source Genre: Journal
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Pages: - Volume / Issue: 257 Sequence Number: 119310 Start / End Page: - Identifier: ISBN: 1053-8119