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Brain Sci. 2018, 8(6), 114; https://doi.org/10.3390/brainsci8060114

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

Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
Received: 10 May 2018 / Revised: 14 June 2018 / Accepted: 18 June 2018 / Published: 19 June 2018
(This article belongs to the Special Issue Advances in the Neurocognition of Music and Language)
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Abstract

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human’s brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics. View Full-Text
Keywords: statistical learning; implicit learning; domain generality; information theory; entropy; uncertainty; order; n-gram; Markov model; word segmentation statistical learning; implicit learning; domain generality; information theory; entropy; uncertainty; order; n-gram; Markov model; word segmentation
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Daikoku, T. Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy and Uncertainty. Brain Sci. 2018, 8, 114.

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