Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
For neuroscientists studying language processing, GCT provides a method to bridge data-driven models and formal scientific theories by generating and testing causal explanations of brain selectivity.
The paper introduces generative causal testing (GCT) to explain language selectivity in the brain from predictive models, using LLM-generated stimuli to test explanations. GCT successfully explains selectivity in individual voxels and cortical ROIs, including newly identified microROIs in prefrontal cortex, and dissects fine-grained differences between brain areas.
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is unclear what features of the language stimulus drive the response in each brain area. We present generative causal testing (GCT), a framework for generating concise explanations of language selectivity in the brain from predictive models and then testing those explanations in follow-up experiments using LLM-generated stimuli.This approach is successful at explaining selectivity both in individual voxels and cortical regions of interest (ROIs), including newly identified microROIs in prefrontal cortex. We show that explanatory accuracy is closely related to the predictive power and stability of the underlying predictive models. Finally, we show that GCT can dissect fine-grained differences between brain areas with similar functional selectivity. These results demonstrate that LLMs can be used to bridge the widening gap between data-driven models and formal scientific theories.