SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics
To enable the study of real-world linguistic meaning in the brain, we introduce SCRIBE, a framework grounded in a neuroscience-informed semantic space that leverages an ensemble of open-source large language models to generate interpretable, word-level semantic representations of arbitrary text.
ProofPaper ↗
Key points
- 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.
- In a behavioral study with 921 participants, we show that human semantic feature judgments vary systematically with context, and that SCRIBE captures these shifts, reaching 75-81% of the estimated human noise ceiling.
- Using brain activity recorded while participants listen to naturalistic stories, we further demonstrate that SCRIBE predicts neural responses in language-selective brain regions, significantly outperforming non-contextual semantic representations.
- Variance partitioning reveals that SCRIBE explains unique brain-predictive information beyond syntactic features, suggesting that its representations capture aspects of linguistic meaning not accounted for by the measured syntactic features.
Sources (1)
- [1]SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded SemanticsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:51 AM
To enable the study of real-world linguistic meaning in the brain, we introduce SCRIBE, a framework grounded in a neuroscience-informed semantic space that leverages an ensemble of open-source large language models to generate interpretable, word-level semantic representations of arbitrary text.
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.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 15, 2026Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization
- Oct 15, 2026MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
- Oct 14, 2026ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models
- Oct 14, 2026RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios
- Oct 14, 2026The Design Space of Tri-Modal Masked Diffusion Models
- Oct 13, 2026Plan or Retrieve First? A Scale Study of Tool Discovery Architectures