  With the recent proliferation of large language models (LLMs), such as
Generative Pre-trained Transformers (GPT), there has been a significant shift
in exploring human and machine comprehension of semantic language meaning. This
shift calls for interdisciplinary research that bridges 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.
