13U. Güçlü and M. A. J. van Gerven, “Increasingly complex representa-tions of natural movies across the dorsal stream are shared between subjects,” Neuroimage, vol.145, pp.329–336, Jan. 2017.14S. Nishida and S. Nishimoto, “Decoding naturalistic experiences from human brain activity via distributed representations of words,” Neuroim-age, vol.180, no.A, pp.232–242, 2018.15S. Nishida, A. Blanc, N. Maeda, M. Kado, and S. Nishimoto, “Behav-ioral correlates of cortical semantic representations modeled by word vectors,” PLoS Comput. Biol., vol.17, no.6, pp.e1009138–e1009138, 2021.16S. Nishida, Y. Nakano, A. Blanc, N. Maeda, M. Kado, and S. Nishimoto, “Brain-Mediated Transfer Learning of Convolutional Neural Networks,” AAAI, vol.34, no.04, pp.5281–5288, April 2020.17S. Jain and A. Huth, “Incorporating context into language encoding models for fMRI,” Adv. Neural Inf. Process. Syst., vol.31, 2018.18M. D. Lescroart and J. L. Gallant, “Human Scene-Selective Areas Rep-resent 3D Configurations of Surfaces,” Neuron, vol.101, no.1, pp.178–192.e7, Jan. 2019.19S. F. Popham, A. G. Huth, N. Y. Bilenko, F. Deniz, J. S. Gao, A. O. Nunez-Elizalde, and J. L. Gallant, “Visual and linguistic semantic representations are aligned at the border of human visual cortex,” Nat. Neurosci., vol.24, no.11, pp.1628–1636, Nov. 2021.20N. Koide-Majima, T. Nakai, and S. Nishimoto, “Distinct dimensions of emotion in the human brain and their representation on the cortical surface,” Neuroimage, vol.222, pp.117258–117258, 2020.21S. Nishida, A. G. Huth, J. L. Gallant, and S. Nishimoto, “Word statistics in large-scale texts explain the human cortical semantic representation of objects, actions, and impressions,” Society Neuroscience Abstract, vol.45, p.333.13, 2015.22西田 知史 and 西本 伸志, “意味認知と脳内情報表現,” 人工知能, vol.32, no.6, pp.857–862, 2017.23C. Kawase, I. Kobayashi, S. Nishimoto, S. Nishida, and H. Asoh, “Se-mantic representation in the cerebral cortex with sparse coding,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.606–611, Oct. 2017.24T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Distributed representa-tions of words and phrases and their compositionality,” Adv. Neural Inf. Process. Syst., vol.26, pp.3111–3119, 2013.25T. Mikolov, W.-T. Yih, and G. Zweig, “Linguistic Regularities in Continuous Space Word Representations,” Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Lin-guistics: Human Language Technologies, pp.746–751, June 2013.26F. Pereira, S. Gershman, S. Ritter, and M. Botvinick, “A comparative evaluation of off-the-shelf distributed semantic representations for mod-elling behavioural data,” Cogn. Neuropsychol., vol.33, no.3–4, pp.175–190, 2016.27A. G. Huth, T. Lee, S. Nishimoto, N. Y. Bilenko, A. T. Vu, and J. L. Gallant, “Decoding the semantic content of natural movies from human brain activity,” Front. Syst. Neurosci., vol.10, no.October, pp.1–16, 2016.28E. Matsuo, I. Kobayashi, S. Nishimoto, S. Nishida, and H. Asoh, “Gen-erating Natural Language Descriptions for Semantic Representations of Human Brain Activity,” Proceedings of the ACL 2016 Student Research Workshop, pp.22–29, Aug. 2016.29E. Matsuo, I. Kobayashi, S. Nishimoto, S. Nishida, and H. Asoh, “De-scribing Semantic Representations of Brain Activity Evoked by Visual Stimuli,” 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.576–583, Oct. 2018.30F. Pereira, B. Lou, B. Pritchett, S. Ritter, S. J. Gershman, N. Kanwisher, M. Botvinick, and E. Fedorenko, “Toward a universal decoder of lin-guistic meaning from brain activation,” Nat. Commun., vol.9, no.1, p.963, 2018.31川畑 輝一, A. Blanc, 前田 直哉, 西本 伸志, and 西田 知史, “畳み込みニューラルネットワークによる脳活動予測を介して脳内知覚情報の個人差を推定するシステム,” 電子情報通信学会技術研究報告; 信学技報, vol.121, no.338, pp.1–6, Jan. 2022.32S. Nishida, S. Toyoda, C. Honda, M. Watanabe, M. Ollikainen, E. Vuoksimaa, J. Kaprio, and S. Nishimoto, “Genetic influences on brain representations of natural audiovisual experiences,” Research Square, 27-Sep-2021.33R. Shinkuma, S. Nishida, M. Kado, N. Maeda, and S. Nishimoto, “Re-lational network of people constructed on the basis of similarity of brain activities,” IEEE Access, vol.7, no.1, pp.110258–110266, 2019.34R. Shinkuma, S. Nishida, N. Maeda, M. Kado, and S. Nishimoto, “Re-duction of Information Collection Cost for Inferring Brain Model Rela-tions From Profile Information Using Machine Learning,” IEEE Trans. Syst. Man Cybern., 2021.35K. Miyahara, T. Niikawa, H. Taiyo, and S. Nishida, “Developing a short-term phenomenological training program : A report of methodological lessons,” New Ideas Psychol., vol.58, p.100780, 2020.36T. Niikawa, K. Miyahara, H. T. Hamada, S. Nishida, and Nicod, Institut Jean, “A new experimental phenomenological method to explore the subjective features of psychological phenomena : its application to binocular rivalry,” Neuroscience of Consciousness, vol.2020, no.1, p.iaa018, 2020.西田 知史 (にしだ さとし)未来ICT研究所脳情報通信融合研究センター脳情報工学研究室主任研究員博士(医学)認知・計算神経科学、ニューロイメージング、人工知能【受賞歴】2020年 人工知能学会2020年度全国大会 優秀賞2011年 IEEE Computational Intelligence Society Japan Chapter, Young Researcher Award2011年 日本神経回路学会 最優秀研究賞193-1 日常的な認知に関わる脳情報処理のモデル化と人工脳への応用
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