Literature DB >> 27429093

Pitch-class distribution modulates the statistical learning of atonal chord sequences.

Tatsuya Daikoku1, Yutaka Yatomi1, Masato Yumoto2.   

Abstract

The present study investigated whether neural responses could demonstrate the statistical learning of chord sequences and how the perception underlying a pitch class can affect the statistical learning of chord sequences. Neuromagnetic responses to two chord sequences of augmented triads that were presented every 0.5s were recorded from fourteen right-handed participants. One sequence was a series of 360 chord triplets, each of which consisted of three chords in the same pitch class (clustered pitch-classes sequences). The other sequence was a series of 360 chord triplets, each of which consisted of three chords in different pitch classes (dispersed pitch-classes sequences). The order of the triplets was constrained by a first-order Markov stochastic model such that a forthcoming triplet was statistically defined by the most recent triplet (80% for one; 20% for the other two). We performed a repeated-measures ANOVA with the peak amplitude and latency of the P1m, N1m and P2m. In the clustered pitch-classes sequences, the P1m responses to the triplets that appeared with higher transitional probability were significantly reduced compared with those with lower transitional probability, whereas no significant result was detected in the dispersed pitch-classes sequences. Neuromagnetic significance was concordant with the results of familiarity interviews conducted after each learning session. The P1m response is a useful index for the statistical learning of chord sequences. Domain-specific perception based on the pitch class may facilitate the domain-general statistical learning of chord sequences.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Magnetoencephalography; Markov process; Musical chord sequence; Pitch class; Statistical learning

Mesh:

Year:  2016        PMID: 27429093     DOI: 10.1016/j.bandc.2016.06.008

Source DB:  PubMed          Journal:  Brain Cogn        ISSN: 0278-2626            Impact factor:   2.310


  11 in total

1.  Functional connectivity of the cortical network supporting statistical learning in musicians and non-musicians: an MEG study.

Authors:  Evangelos Paraskevopoulos; Nikolas Chalas; Panagiotis Bamidis
Journal:  Sci Rep       Date:  2017-11-24       Impact factor: 4.379

Review 2.  Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

Authors:  Tatsuya Daikoku
Journal:  Brain Sci       Date:  2018-06-19

3.  When the statistical MMN meets the physical MMN.

Authors:  Vera Tsogli; Sebastian Jentschke; Tatsuya Daikoku; Stefan Koelsch
Journal:  Sci Rep       Date:  2019-04-03       Impact factor: 4.379

4.  Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning.

Authors:  Tatsuya Daikoku
Journal:  Front Comput Neurosci       Date:  2019-10-02       Impact factor: 2.380

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

Authors:  Tatsuya Daikoku
Journal:  PLoS One       Date:  2019-12-19       Impact factor: 3.240

6.  Single, but not dual, attention facilitates statistical learning of two concurrent auditory sequences.

Authors:  Tatsuya Daikoku; Masato Yumoto
Journal:  Sci Rep       Date:  2017-08-31       Impact factor: 4.379

7.  Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge.

Authors:  Tatsuya Daikoku
Journal:  PLoS One       Date:  2018-05-09       Impact factor: 3.240

8.  Motor Reproduction of Time Interval Depends on Internal Temporal Cues in the Brain: Sensorimotor Imagery in Rhythm.

Authors:  Tatsuya Daikoku; Yuji Takahashi; Nagayoshi Tarumoto; Hideki Yasuda
Journal:  Front Psychol       Date:  2018-10-02

9.  Musical Creativity and Depth of Implicit Knowledge: Spectral and Temporal Individualities in Improvisation.

Authors:  Tatsuya Daikoku
Journal:  Front Comput Neurosci       Date:  2018-11-13       Impact factor: 2.380

10.  Concurrent Statistical Learning of Ignored and Attended Sound Sequences: An MEG Study.

Authors:  Tatsuya Daikoku; Masato Yumoto
Journal:  Front Hum Neurosci       Date:  2019-04-17       Impact factor: 3.169

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