Dr Tatsuya Daikoku

University position

Assistant professor (U Tokyo) / Researcher (U Cam)


Department of Psychology


Centre for Neuroscience in Education



Home page

https://www.daikoku.xyz (personal home page)

Research Themes

Systems and Computational Neuroscience

Cognitive and Behavioural Neuroscience


I'm an assistant professor in The University of Tokyo, and a researcher in University of Cambridge. I'm also a composer who play the piano. I'm interested in interdisciplinary understanding of human and artificial intelligence. Particularly, my topic is to investigate universality and specialty in music and language. I also try to develop new music theory to compose contemporary music.

Research Focus


Statistical Learning

Machine Learning




Clinical conditions

No direct clinical relevance


Computational modelling

Electroencephalography (EEG)

Magnetic resonance imaging (MRI)

Magnetoencephalography (MEG)


No collaborators listed

Associated News Items

    Key publications

    Tatsuya Daikoku (2020), “Where does the creativity come from?(in Japanese), Kobunsha publishing” Book ISBN-13: 978-4334044664



    Daikoku T (2019), “Statistical learning and the uncertainty of melody and bass line in music.” PLoS ONE 14(12): e0226734

    Daikoku T (2019), “Computational models and neural bases of statistical learning in music and language.” Physics of Life Reviews pii: S1571-0645(19)30141-1

    Daikoku T (2019), “Implicit Learning in the Developing Brain: An Exploration of ERP Indices for Developmental Disorders.” Clinical Neurophysiology 130(11):2166-2168

    Daikoku T (2019), “Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning.” Frontiers in Human Neuroscience 13:70

    Daikoku T (2019), “Depth and the Uncertainty of Statistical Knowledge on Musical Creativity Fluctuate Over a Composer’s Lifetime.” Frontiers in Computational Neuroscience 13:27

    Daikoku T (2019), “Method and apparatus for analyzing characteristics of music information” US Patent US20190189100

    Daikoku T, Yumoto M (2019), “Concurrent statistical learning of ignored and attended sound sequences: An MEG study.” Frontiers in Human Neuroscience 13, 102

    Tsogli V, Jentschke S, Daikoku T, Koelsch S (2019), “When the statistical MMN meets the physical MMN.” Scientific Reports 9, 5563


    Daikoku T (2018), “Entropy, uncertainty, and the depth of implicit knowledge on musical creativity: Computational study of improvisation in melody and rhythm.” Frontiers in Computational Neuroscience 12, 97

    Daikoku T (2018), “Musical Creativity and Depth of Implicit Knowledge: Spectral and Temporal Individualities in Improvisation.” Frontiers in Computational Neuroscience 12, 89

    Daikoku T (2018), “Neurophysiological marker of statistical learning in music and language: hierarchy, entropy, and uncertainty.” Brain Sciences 8, 114

    Daikoku T (2018), “Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge.” PLoS ONE 13(5), e0196493

    Daikoku T, Takahashi Y, Tarumoto M,Yasuda H (2018), “Motor reproduction of time interval depends on internal temporal cues in the brain: Sensorimotor imagery in rhythm.” Frontiers in Psychology 9, 1873

    Daikoku T, Takahashi Y, Tarumoto M,Yasuda H (2018), “Auditory Statistical Learning During Concurrent Physical Exercise and the Tolerance for Pitch, Tempo, and Rhythm Changes.” Motor Control 22(3): 233-244

    Yumoto M, Daikoku T (2018), “Neurophysiological Studies on Auditory Statistical Learning (in Japanese).” Japanese journal of cognitive neuroscience 20(1), 38-43


    Daikoku T, Takahashi Y, Futagami H,Tarumoto M,Yasuda H (2017), “Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.” Neurological Research 30, 107-116

    Daikoku T, Yumoto M (2017), “Single, but not dual, attention facilitates statistical learning of two concurrent auditory sequences.” Scientific Reports 7, 10108


    Daikoku T, Yatomi Y, Yumoto M (2016), “Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering.” Neuropsychologia 95 1-10

    Daikoku T, Yatomi Y, Yumoto M (2016), “Pitch-class distribution modulates statistical learning of atonal chord sequences.” Brain and Cognition 108, 1-10


    Daikoku T, Yatomi Y, Yumoto M (2015), “Statistical learning of music- and language-like sequences and tolerance for spectral shifts.” Neurobiology of Learning and Memory 118, 8-19


    Daikoku T, Yatomi Y, Yumoto M (2014), “Implicit and explicit statistical learning of tone sequences across spectral shifts.” Neuropsychologia 63, 194-204


    Daikoku T, Ogura H, Watanabe M (2012), “The variation of hemodynamics relative to listening to consonance or dissonance during chord progression.” Neurological research 34, 557-563