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  Statistical learning and the uncertainty of melody and bass line in music

Daikoku, T. (2019). Statistical learning and the uncertainty of melody and bass line in music. PLoS One, 14(12): e0226734. doi:10.1371/journal.pone.0226734.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-82AF-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-82B0-F
Genre: Journal Article

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Daikoku, Tatsuya1, 2, Author              
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1Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              
2Centre for Neuroscience in Education, University of Cambridge, United Kingdom, ou_persistent22              

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 Abstract: Statistical learning is the ability to learn based on transitional probability (TP) in sequential information, which has been considered to contribute to creativity in music. The interdisciplinary theory of statistical learning examines statistical learning as a mechanism of human learning. This study investigated how TP distribution and conditional entropy in TP of the melody and bass line in music interact with each other, using the highest and lowest pitches in Beethoven's piano sonatas and Johann Sebastian Bach's Well-Tempered Clavier. Results for the two composers were similar. First, the results detected specific statistical characteristics that are unique to each melody and bass line as well as general statistical characteristics that are shared between the melody and bass line. Additionally, a correlation of the conditional entropies sampled from the TP distribution could be detected between the melody and bass line. This suggests that the variability of entropies interacts between the melody and bass line. In summary, this study suggested that TP distributions and the entropies of the melody and bass line interact with but are partly independent of each other.

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Language(s): eng - English
 Dates: 2019-04-232019-12-032019-12-19
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pone.0226734
Other: eCollection 2019
PMID: 31856208
PMC: PMC6922457
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 14 (12) Sequence Number: e0226734 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850