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      Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

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          Abstract

          Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called “eureka” moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

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          The free-energy principle: a unified brain theory?

          A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories - optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.
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            Alpha-band oscillations, attention, and controlled access to stored information

            Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
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              Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

              We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                20 April 2021
                2021
                : 15
                : 640412
                Affiliations
                [1] 1International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo , Tokyo, Japan
                [2] 2AI Lab, Vrije Universiteit Brussel , Brussels, Belgium
                [3] 3School of Electronic Engineering and Computer Science, Queen Mary University of London , London, United Kingdom
                [4] 4Institute for AI and Beyond, The University of Tokyo , Tokyo, Japan
                Author notes

                Edited by: Andrea Schiavio, University of Graz, Austria

                Reviewed by: Oded M. Kleinmintz, University of Haifa, Israel; Peter Schneider, Heidelberg University, Germany

                *Correspondence: Tatsuya Daikoku, daikoku.tatsuya@ 123456mail.u-tokyo.ac.jp

                This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2021.640412
                8093513
                33958983
                e212f832-5dab-4e1c-b6ec-bbc10b8d179f
                Copyright © 2021 Daikoku, Wiggins and Nagai.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 December 2020
                : 10 March 2021
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 156, Pages: 14, Words: 0
                Funding
                Funded by: Core Research for Evolutional Science and Technology 10.13039/501100003382
                Award ID: JPMJCR16E2
                Funded by: Japan Society for the Promotion of Science 10.13039/501100001691
                Award ID: 20K22676
                Categories
                Neuroscience
                Review

                Neurosciences
                statistical learning,prediction,creativity,development,hierarchy,abstraction,integration,autism spectrum disorder

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