Generative causal testing to bridge data-driven models and scientific theories in language neuroscience

TL;DR

This paper introduces generative causal testing (GCT), a framework that uses large language models to generate and test hypotheses about language-related brain activity, bridging data-driven models and scientific theories in neuroscience.

Contribution

The paper presents GCT, a novel method for explaining and testing language selectivity in the brain using LLM-generated stimuli and causal testing, advancing understanding of neural language processing.

Findings

GCT successfully explains selectivity in individual voxels and cortical regions.

Explanatory accuracy correlates with model predictive power and stability.

GCT reveals fine-grained differences between brain areas with similar functions.

Abstract

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.