Literature DB >> 27939187

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering.

Tatsuya Daikoku1, Yutaka Yatomi1, Masato Yumoto2.   

Abstract

Previous neural studies have supported the hypothesis that statistical learning mechanisms are used broadly across different domains such as language and music. However, these studies have only investigated a single aspect of statistical learning at a time, such as recognizing word boundaries or learning word order patterns. In this study, we neutrally investigated how the two levels of statistical learning for recognizing word boundaries and word ordering could be reflected in neuromagnetic responses and how acquired statistical knowledge is reorganised when the syntactic rules are revised. Neuromagnetic responses to the Japanese-vowel sequence (a, e, i, o, and u), presented every .45s, were recorded from 14 right-handed Japanese participants. The vowel order was constrained by a Markov stochastic model such that five nonsense words (aue, eao, iea, oiu, and uoi) were chained with an either-or rule: the probability of the forthcoming word was statistically defined (80% for one word; 20% for the other word) by the most recent two words. All of the word transition probabilities (80% and 20%) were switched in the middle of the sequence. In the first and second quarters of the sequence, the neuromagnetic responses to the words that appeared with higher transitional probability were significantly reduced compared with those that appeared with a lower transitional probability. After switching the word transition probabilities, the response reduction was replicated in the last quarter of the sequence. The responses to the final vowels in the words were significantly reduced compared with those to the initial vowels in the last quarter of the sequence. The results suggest that both within-word and between-word statistical learning are reflected in neural responses. The present study supports the hypothesis that listeners learn larger structures such as phrases first, and they subsequently extract smaller structures, such as words, from the learned phrases. The present study provides the first neurophysiological evidence that the correction of statistical knowledge requires more time than the acquisition of new statistical knowledge.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Magnetoencephalography; Markov process; Statistical learning; Word ordering; Word segmentation

Mesh:

Year:  2016        PMID: 27939187     DOI: 10.1016/j.neuropsychologia.2016.12.006

Source DB:  PubMed          Journal:  Neuropsychologia        ISSN: 0028-3932            Impact factor:   3.139


  8 in total

Review 1.  Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

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

2.  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

3.  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

4.  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

5.  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

6.  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

7.  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

8.  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

  8 in total

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