| 日 時 | 2026年11月24日(火) 13:00 より 13:50 まで |
|---|---|
| 講演者 | Yu-Shiang Su, Ph.D. |
| 講演者所属 | Project Researcher, International Research Center for Neurointelligence (WPI-ICRN), The University of Tokyo (東京大学 ニューロインテリジェンス国際研究機構) |
| 場 所 | 生理学研究所 １Fセミナー室A/B |
| お問い合わせ先 | 福永雅喜（fuku@nips.ac.jp） |
| 要旨 |
Interpreting the world requires assigning significance to available information and inferring what cannot be directly observed. In this talk, I explore how the brain constructs interpretations from information that varies in form and complexity. Beginning with explicitly stated probabilities and rewards, I present fMRI findings on age differences in neural probability processing during value-based decisions. When critical information is hidden, participants must infer latent causes from partial observations. Computational modeling and fMRI reveal age differences in belief stability and neural representations of these causes. Creativity introduces a more open-ended challenge, requiring participants to draw on learned internal representations to form new conceptual connections and generate novel, useful responses. Here, I examine prestimulus brain-network states associated with subsequent creative performance. These studies motivate my proposed research on how abstraction supports conceptual learning and creativity. This work will investigate how the brain organizes high-dimensional information and flexibly weights relevant dimensions as evidence, goals, and contexts change. I will also share my ideas for contributing to an AI-ready data platform. The aim is to develop user-friendly, remotely accessible research infrastructure that supports automated, large-scale MRI analysis and advances AI for Science. |
