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  Entropy, uncertainty, and the depth of implicit knowledge on musical creativity: Computational study of improvisation in melody and rhythm

Daikoku, T. (2018). Entropy, uncertainty, and the depth of implicit knowledge on musical creativity: Computational study of improvisation in melody and rhythm. Frontiers in Computational Neuroscience, 12: 97. doi:10.3389/fncom.2018.00097.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-8D1F-E Version Permalink: http://hdl.handle.net/21.11116/0000-0003-9FF8-3
Genre: Journal Article

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 Creators:
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: creativity; Markov model; N-gram; improvisation; statistical learning; machine learning; uncertainty; entropy
 Abstract: Recent neurophysiological and computational studies have proposed the hypothesis that our brain automatically codes the nth-order transitional probabilities (TPs) embedded in sequential phenomena such as music and language (i.e., local statistics in nth-order level), grasps the entropy of the TP distribution (i.e., global statistics), and predicts the future state based on the internalized nth-order statistical model. This mechanism is called statistical learning (SL). SL is also believed to contribute to the creativity involved in musical improvisation. The present study examines the interactions among local statistics, global statistics, and different levels of orders (mutual information) in musical improvisation interact. Interactions among local statistics, global statistics, and hierarchy were detected in higher-order SL models of pitches, but not lower-order SL models of pitches or SL models of rhythms. These results suggest that the information-theoretical phenomena of local and global statistics in each order may be reflected in improvisational music. The present study proposes novel methodology to evaluate musical creativity associated with SL based on information theory.

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Language(s): eng - English
 Dates: 2018-07-052018-11-232018-12-19
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fncom.2018.00097
PMID: 30618691
PMC: PMC6305898
Other: eCollection 2018
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Funding program : Grant for Groundbreaking Young Researchers
Funding organization : Suntory Foundation
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Funding program : -
Funding organization : Nakayama Foundation for Human Science

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Title: Frontiers in Computational Neuroscience
  Abbreviation : Front Comput Neurosci
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 12 Sequence Number: 97 Start / End Page: - Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188