ArticlePloS one2018
Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Order of statistical learning depends on perceptive uncertainty.Current research in neurobiology · 2023Article
- Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning.Frontiers in neuroscience · 2021Review
- Depth and the Uncertainty of Statistical Knowledge on Musical Creativity Fluctuate Over a Composer's Lifetime.Frontiers in computational neuroscience · 2019Article
- Concurrent Statistical Learning of Ignored and Attended Sound Sequences: An MEG Study.Frontiers in human neuroscience · 2019Article
- Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning.Frontiers in computational neuroscience · 2019Article
- Article
- Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.Brain sciences · 2018Review
- Musical Creativity and Depth of Implicit Knowledge: Spectral and Temporal Individualities in Improvisation.Frontiers in computational neuroscience · 2018Article
- Entropy, Uncertainty, and the Depth of Implicit Knowledge on Musical Creativity: Computational Study of Improvisation in Melody and Rhythm.Frontiers in computational neuroscience · 2018Article
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Abstract
Learning and knowledge of transitional probability in sequences like music, called statistical learning and knowledge, are considered implicit processes that occur without intention to learn and awareness of what one knows. This implicit statistical knowledge can be alternatively expressed via abstract medium such as musical melody, which suggests this knowledge is reflected in melodies written by a composer. This study investigates how statistics in music vary over a composer's lifetime. Transitional probabilities of highest-pitch sequences in Ludwig van Beethoven's Piano Sonata were calculated based on different hierarchical Markov models. Each interval pattern was ordered based on the sonata opus number. The transitional probabilities of sequential patterns that are musical universal in music gradually decreased, suggesting that time-course variations of statistics in music reflect time-course variations of a composer's statistical knowledge. This study sheds new light on novel methodologies that may be able to evaluate the time-course variation of composer's implicit knowledge using musical scores.
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