A generative framework to bridge data-driven models and scientific theories in language neuroscience

However, these models are not scientific theories that describe the world in natural language. Instead, they are implemented in the form of vast neural networks with millions or billions of largely inscrutable parameters. One emblematic field is language neuroscience, where large language models (LLMs) are highly effective at predicting human brain responses to natural language, but are virtually impossible to interpret or analyze by hand [4-10]. To overcome this challenge, we introduce the generative explanation-mediated validation (GEM-V) framework. GEM-V translates deep learning models of language selectivity in the brain into concise verbal explanations, and then designs follow-up experiments to verify that these explanations are causally related to brain activity.