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  Musical creativity and depth of implicit knowledge: Spectral and temporal individualities in improvisation

Daikoku, T. (2018). Musical creativity and depth of implicit knowledge: Spectral and temporal individualities in improvisation. Frontiers in Computational Neuroscience, 12: 89. doi:10.3389/fncom.2018.00089.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-6351-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-74BC-7
Genre: Journal Article

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Daioku_Frontiers_2018.pdf (Publisher version), 4MB
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Daikoku, Tatsuya1, Author              
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1Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              

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Free keywords: Implicit learning; Statistical learning; Markov model; Ngram; Music; Creativitity; Improvisation (music)
 Abstract: It has been suggests musical creativity is mainly formed by implicit knowledge. However, the types of spectro-temporal features and depth of the implicit knowledge forming individualities of improvisation are unknown. This study, using various-order Markov models on implicit statistical learning, investigated spectro-temporal statistics among musicians. The results suggested that lower-order models on implicit knowledge represented general characteristics shared among musicians, whereas higher-order models detected specific characteristics unique to each musician. Second, individuality may essentially be formed by pitch but not rhythm, whereas the rhythms may allow the individuality of pitches to strengthen. Third, time-course variation of musical creativity formed by implicit knowledge and uncertainty (i.e., entropy) may occur in a musician’s lifetime. Individuality of improvisational creativity may be formed by deeper but not superficial implicit knowledge of pitches, and that the rhythms may allow the individuality of pitches to strengthen. Individualities of the creativity may shift over a musician’s lifetime via experience and training.

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Language(s): eng - English
 Dates: 2018-08-132018-10-182018-11-13
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fncom.2018.00089
PMID: 30483087
PMC: PMC6243102
Other: eCollection 2018
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Title: Frontiers in Computational Neuroscience
  Abbreviation : Front Comput Neurosci
Source Genre: Journal
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Pages: - Volume / Issue: 12 Sequence Number: 89 Start / End Page: - Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188