用大模型生成刺激，解释大脑对语言的响应机制
Generative causal testing to bridge data-driven models and scientific theories in language neuroscience
- 通过生成式因果测试，从语言模型推导脑区选择性成因
- 在单个体素和皮层区域均实现高精度解释，包括前额叶新发现微区
- 适合研究神经科学与人工智能交叉的学者，推动理论与数据融合
大型语言模型的表征能有效预测语言刺激下的BOLD fMRI反应，但其内部机制仍不透明：尚不清楚语言刺激的哪些特征驱动了各脑区的响应。本文提出生成式因果测试（GCT）框架，利用预测模型生成语言选择性的简洁解释，并通过大模型生成的新刺激在后续实验中验证这些解释。该方法成功解释了单个体素及感兴趣皮层区域（ROIs），包括前额叶中新识别的微ROIs。我们发现解释准确性与底层预测模型的预测能力及稳定性密切相关。此外，GCT可区分功能选择性相似的脑区间细微差异。结果表明，语言模型可作为桥梁，弥合数据驱动模型与正式科学理论之间的鸿沟。
原文摘要 · Abstract (English)
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.
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