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This study uses EEG to investigate how word predictability influences N400 brain responses across lexical categories, showing that content words exhibit greater N400 differences than function words, and decoding techniques outperform traditional ERP analysis.
This paper investigates whether language models' next-word prediction aligns with human cognitive processing by analyzing EEG signals and event-related potentials, finding that only surprisal correlates with human brain responses, especially for open-class words.
This study uses language model embeddings to quantify semantic association in self-paced reading and EEG data, examining how different implementations affect measures of reading difficulty.