| Discovering the Neuroanatomical Correlates of Music with Machine Learning T Daikoku Handbook of Artificial Intelligence for Music, 117-161, 2021 | 74* | 2021 |
| Neurophysiological markers of statistical learning in music and language: hierarchy, entropy and uncertainty T Daikoku Brain sciences 8 (6), 114, 2018 | 73 | 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 | 56 | 2022 |
| Implicit and explicit statistical learning of tone sequences across spectral shifts T Daikoku, Y Yatomi, M Yumoto Neuropsychologia 63, 194-204, 2014 | 54 | 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 |
| 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 |
| 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 | 35 | 2017 |
| 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 | 28 | 2019 |
| Musical expertise facilitates statistical learning of rhythm and the perceptive uncertainty: A cross-cultural study T Daikoku, M Yumoto Neuropsychologia 146, 107553, 2020 | 27 | 2020 |
| 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 | 23 | 2017 |
| 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 | 20 | 2021 |
| Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge T Daikoku PloS one 13 (5), e0196493, 2018 | 19 | 2018 |
| 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 |
| 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 | 18 | 2021 |
| 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 |
| Motor reproduction of time interval depends on internal temporal cues in the brain: Sensorimotor imagery in rhythm T Daikoku, Y Takahashi, N Tarumoto, H Yasuda Frontiers in psychology 9, 1873, 2018 | 14 | 2018 |
Yukie NagaiThe University of Tokyo確認したメール アドレス: mail.u-tokyo.ac.jp
