Microsoft Research· Research / Phi

Understanding the brain with AI-driven explanations and experiments

At a glance LLM-based models can predict the human brain’s responses to language with high accuracy. But what drives that performance is essentially unreadable: a vast collection of learned parameters, not scientific theories anyone can read. Generative causal testing (GCT), developed in a collaboration between Microsoft Research, the University of California, Berkeley, the University of California, San Francisco, and Columbia University, distills these brain-prediction models into short verbal explanations of what each patch of cortex responds to: phrases like “food preparation” or “location names.” GCT then closes the loop: an LLM writes new stories designed to activate a targeted brain area, subjects hear them in the scanner, and the region lights up only if the explanation is right. In experiments, GCT confirmed known selectivity, teased apart neighboring place-processing regions long thought interchangeable, and revealed tiny prefrontal “micro-regions” tuned to specific concepts like dialogue, clock times, and measurements. The explainability problem in language neuroscience …

Announced

25 Jun 2026 16:00 UTC