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Exploring Similarity between Neural and LLM Trajectories in Language Processing

Understanding the similarity between large language models (LLMs) and human brain activity is crucial for advancing both AI and cognitive neuroscience. In this study, we provide a multilinguistic, large-scale assessment of this similarity by systematically comparing 16 publicly available pretrained LLMs with human brain responses during natural language processing tasks in both English and Chinese. Specifically, we use ridge regression to assess the representational similarity between LLM embeddings and electroencephalography (EEG) signals, and analyze the similarity between the "neural trajectory" and the "LLM latent trajectory." This method captures key dynamic patterns, such as magnitude, angle, uncertainty, and confidence. Our findings highlight both similarities and crucial differences in processing strategies: (1) We show that middle-to-high layers of LLMs are central to semantic integration and correspond to the N400 component observed in EEG; (2) The brain exhibits continuous and iterative processing during reading, whereas LLMs often show discrete, stage-end bursts of activity, which suggests a stark contrast in their real-time semantic processing dynamics. This study could offer new insights into LLMs and neural processing, and also establish a critical framework for future investigations into the alignment between artificial intelligence and biological intelligence.