SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics

Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varies in natural settings. To enable the study of real-world linguistic meaning in the brain, we introduce SCRIBE, an approach for modeling context-sensitive meaning. Grounded in a neuroscience-informed semantic space and developed using an ensemble of open-source large language models, SCRIBE dynamically annotates meaning in context to produce interpretable, word-level semantic representations for an arbitrary text. Experiments using human judgments, controlled synthetic contexts, and brain activity recorded while participants listen to naturalistic stories, show that SCRIBE captures systematic context-dependent semantic shifts and predicts neural responses in language-selective brain regions, significantly outperforming non-contextual semantic representations. Variance partitioning further shows that SCRIBE captures unique brain-predictive information beyond syntactic features, supporting its ability to encode contextual semantic information relevant to the brain. Together, these results establish SCRIBE as a scalable and interpretable foundation for studying natural language semantics in the brain.