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Kuai H, Chen J, Tao X, Cai L, Imamura K, Matsumoto H, Liang P, Zhong N. Never-Ending Learning for Explainable Brain Computing. Adv Sci (Weinh) 2024:e2307647. [PMID: 38602432 DOI: 10.1002/advs.202307647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/24/2024] [Indexed: 04/12/2024]
Abstract
Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.
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Affiliation(s)
- Hongzhi Kuai
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, 4350, Australia
| | - Lingyun Cai
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
| | - Kazuyuki Imamura
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
| | - Hiroki Matsumoto
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
| | - Ning Zhong
- Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, 100048, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China
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