Tatsuya Daikoku

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Yukie NagaiThe University of TokyoEmail verificata su mail.u-tokyo.ac.jp
Riikka MöttönenUniversity of NottinghamEmail verificata su nottingham.ac.uk
Stefan KoelschInstitute for Biological and Medical Psychology, University of BergenEmail verificata su uib.no
Yasuo KuniyoshiSchool of Information Science and Technology, The University of TokyoEmail verificata su isi.imi.i.u-tokyo.ac.jp
Eleonore SmalleTilburg UniversityEmail verificata su uclouvain.be
Arnaud Szmalec, PhDUniversité catholique de Louvain, Universiteit GentEmail verificata su uclouvain.be
Wouter DuyckGhent UniversityEmail verificata su ugent.be
Usha GoswamiProfessor of Cognitive Developmental Neuroscience, University of CambridgeEmail verificata su cam.ac.uk
Sebastian JentschkeAssociate professor of Developmental Psychology, Dept. of Psychosocial Science, University of BergenEmail verificata su uib.no
Naoto YoshidaKyoto UniversityEmail verificata su kyoto-u.ac.jp
Maiko MinatoyaUniversity of TokyoEmail verificata su g.ecc.u-tokyo.ac.jp
Geraint WigginsProfessor of Computational Creativity, Vrije Universiteit Brussel / Queen Mary University of LondonEmail verificata su vub.ac.be
Merav AhissarProfessor of Psychology and Cognitive Neuroscience, Hebrew UniversityEmail verificata su mscc.huji.ac.il
Thomas LachmannProfessor of Psychology, Center for Cognitive Science, University of Kaiserslautern-LandauEmail verificata su sowi.uni-kl.de
Takato HoriiOsaka University, JapanEmail verificata su sys.es.osaka-u.ac.jp
Tomohiro IshizuKansai UniversityEmail verificata su kansai-u.ac.jp
Tara SrirangarajanStanford UniversityEmail verificata su stanford.edu

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Tatsuya Daikoku

Associate Professor(UniversityTokyo), Researcher(UniversityCambridge)

Email verificata su mail.u-tokyo.ac.jp - Home page

Brain Statistical Learning Music Creativity


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Discovering the Neuroanatomical Correlates of Music with Machine Learning T Daikoku Handbook of Artificial Intelligence for Music, 117-161, 2021	95*	2021
Neurophysiological markers of statistical learning in music and language: hierarchy, entropy and uncertainty T Daikoku Brain sciences 8 (6), 114, 2018	74	2018
Unlocking adults’ implicit statistical learning by cognitive depletion EHM Smalle, T Daikoku, A Szmalec, W Duyck, R Möttönen Proceedings of the National Academy of Sciences 119 (2), e2026011119, 2022	68	2022
Implicit and explicit statistical learning of tone sequences across spectral shifts T Daikoku, Y Yatomi, M Yumoto Neuropsychologia 63, 194-204, 2014	55	2014
Statistical learning of music-and language-like sequences and tolerance for spectral shifts T Daikoku, Y Yatomi, M Yumoto Neurobiology of learning and memory 118, 8-19, 2015	47	2015
When the statistical MMN meets the physical MMN V Tsogli, S Jentschke, T Daikoku, S Koelsch Scientific Reports 9 (1), 5563, 2019	39	2019
Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering T Daikoku, Y Yatomi, M Yumoto Neuropsychologia 95, 1-10, 2017	37	2017
Pitch-class distribution modulates the statistical learning of atonal chord sequences T Daikoku, Y Yatomi, M Yumoto Brain and cognition 108, 1-10, 2016	37	2016
Musical expertise facilitates statistical learning of rhythm and the perceptive uncertainty: A cross-cultural study T Daikoku, M Yumoto Neuropsychologia 146, 107553, 2020	33	2020
Musical creativity and depth of implicit knowledge: spectral and temporal individualities in improvisation T Daikoku Frontiers in computational neuroscience 12, 89, 2018	31	2018
Depth and the uncertainty of statistical knowledge on musical creativity fluctuate over a composer's lifetime T Daikoku Frontiers in computational neuroscience 13, 27, 2019	29	2019
Importance of environmental settings for the temporal dynamics of creativity T Daikoku, Q Fang, T Hamada, Y Handa, Y Nagai Thinking Skills and Creativity 41, 100911, 2021	25	2021
Entropy, uncertainty, and the depth of implicit knowledge on musical creativity: computational study of improvisation in melody and rhythm T Daikoku Frontiers in Computational Neuroscience 12, 97, 2018	25	2018
Single, but not dual, attention facilitates statistical learning of two concurrent auditory sequences T Daikoku, M Yumoto Scientific reports 7 (1), 10108, 2017	24	2017
Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge T Daikoku PloS one 13 (5), e0196493, 2018	20	2018
Statistical properties of musical creativity: Roles of hierarchy and uncertainty in statistical learning T Daikoku, GA Wiggins, Y Nagai Frontiers in Neuroscience 15, 640412, 2021	19	2021
The variation of hemodynamics relative to listening to consonance or dissonance during chord progression T Daikoku, H Ogura, M Watanabe Neurological research 34 (6), 557-563, 2012	19	2012
Auditory statistical learning during concurrent physical exercise and the tolerance for pitch, tempo, and rhythm changes T Daikoku, Y Takahashi, N Tarumoto, H Yasuda Motor Control 22 (3), 233-244, 2018	16	2018
Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise T Daikoku, Y Takahashi, H Futagami, N Tarumoto, H Yasuda Neurological research 39 (2), 107-116, 2017	16	2017
Embodiment perspective of reward definition for behavioural homeostasis N Yoshida, T Daikoku, Y Nagai, Y Kuniyoshi Deep RL Workshop NeurIPS 2021, 2021	15	2021

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