With the recent explosion of large language models (LLMs), such as Generative Pretrained Transformers (GPT), the need to understand the ability of humans and machines to comprehend semantic language meaning has entered a new phase. This requires interdisciplinary research that bridges the fields of cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60\% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.

翻译：随着生成式预训练Transformer（GPT）等大型语言模型（LLMs）的迅猛发展，理解人类与机器语义理解能力的研究进入新阶段。这需要认知科学与自然语言处理（NLP）领域的跨学科研究。本预实验研究旨在探究个体在语义关系阅读理解任务中的神经状态。我们提出联合分析LLMs、眼动追踪与脑电图（EEG）数据的方法，研究阅读过程中大脑如何加工与关键词具有不同相关度的词汇。同时采用特征工程方法改进阅读高/低相关词汇时的注视相关EEG数据分类。在12名被试的单词级分类中，最佳验证准确率超过60%。与推理关键词高度相关的词汇（无注视词汇剔除时，每词平均注视次数1.0584 vs 0.6576；包含无注视词汇时，1.5126 vs 1.4026）显著获得更多注视。本研究首次尝试利用LLM知识实现单词级脑状态分类，为人类认知能力与通用人工智能（AGI）研究提供了重要启示，并为开发潜在阅读辅助技术提供了指导方向。