The activations of language transformers like GPT-2 have been shown to
linearly map onto brain activity during speech comprehension. However, the
nature of these activations remains largely unknown and presumably conflate
distinct linguistic classes. Here, we propose a taxonomy to factorize the
high-dimensional activations of language models into four combinatorial
classes: lexical, compositional, syntactic, and semantic representations. We
then introduce a statistical method to decompose, through the lens of GPT-2's
activations, the brain activity of 345 subjects recorded with functional
magnetic resonance imaging (fMRI) during the listening of ~4.6 hours of
narrated text. The results highlight two findings. First, compositional
representations recruit a more widespread cortical network than lexical ones,
and encompass the bilateral temporal, parietal and prefrontal cortices. Second,
contrary to previous claims, syntax and semantics are not associated with
separated modules, but, instead, appear to share a common and distributed
neural substrate. Overall, this study introduces a versatile framework to
isolate, in the brain activity, the distributed representations of linguistic
constructs.

通过对 GPT-2 的激活进行统计的方法，将语言模型的高维度激活分解成四个组合类别：词汇、组合、句法和语义表示，并发现组合表示比词汇表示更广泛地招募了大脑皮层网络，并且句法和语义共享一个分布式神经基础。该研究提出了一个多功能的框架来分离神经活动中的语言建构分布式表示。