Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanisms. Conventional experiment-driven approaches hinge on manually constructed visual stimulus collections and corresponding brain activity recordings, constraining the support and coverage of concept localization. Additionally, these stimuli often consist of concept objects in unnatural contexts and are potentially biased by subjective preferences, thus prompting concerns about the validity and generalizability of the identified regions. To address these limitations, we propose a data-driven exploration approach. By synthesizing extensive brain activity recordings, we statistically localize various concept-selective regions. Our proposed MindSimulator leverages advanced generative technologies to learn the probability distribution of brain activity conditioned on concept-oriented visual stimuli. This enables the creation of simulated brain recordings that reflect real neural response patterns. Using the synthetic recordings, we successfully localize several well-studied concept-selective regions and validate them against empirical findings, achieving promising prediction accuracy. The feasibility opens avenues for exploring novel concept-selective regions and provides prior hypotheses for future neuroscience research.

Graph neural networks (GNNs) have recently gained attention for capturing complex relations, prompting researchers to explore their potential in document classification. Existing studies serving this purpose fall into two directions: inductive learning focusing on personalized context relations within documents and transductive learning targeting the global distribution relations among documents in a corpus. Both directions extract distinct types of beneficial structural information and yield encouraging outcomes. However, due to the incompatibility of underlying graph structures and learning settings, developing an enhanced model that effectively integrates local and global relational learning within existing frameworks is challenging. To address this issue, we propose a new GNN-based document representation learning framework that incorporates multiview global graphs at both the word and document levels, focusing on learning the diverse global distribution information of texts at different granularities. Additionally, a contextual encoder derives the initial representations of document nodes from the updated representations of word nodes, integrating personalized context relations into document representations during this process. Finally, we tailor a node representation learning strategy for the multiview global graphs, called the multiview graph sampling and updating module, which allows our framework to operate efficiently during training without being constrained by the scale of the global graph. Experiments indicate that our framework generally enhances performance by integrating both global and local relational learning. When combined with large-scale language models, our framework achieves state-of-the-art results for GNN-based models across multiple datasets.