EEG-Based Decoding of Neural Mechanisms Underlying Impersonal Pronoun Resolution

Pronoun resolution is essential for language comprehension, yet the neural mechanisms underlying this process remain poorly characterized. Here, we investigate the neural dynamics of impersonal pronoun processing using electroencephalography combined with machine learning approaches. We developed a novel experimental paradigm that contrasts impersonal pronoun resolution with direct nominal reference processing. Using electroencephalography (EEG) recordings and machine learning techniques, including local learning-based clustering feature selection (LLCFS), recursive feature elimination (RFE), and logistic regression (LR), we analyzed neural responses from twenty participants. Our approach revealed differential EEG feature patterns across frontal, temporal, and parietal electrodes within multiple frequency bands during pronoun resolution compared to nominal reference tasks, achieving classification accuracies of 78.52% for subject-dependent and 60.10% for cross-subject validation. Behavioral data revealed longer reaction times and lower accuracy for pronoun resolution compared to nominal reference tasks. Combined with differential EEG patterns, these findings demonstrate that pronoun resolution engages more complex mechanisms of referent selection and verification compared to nominal reference tasks. The results establish potential EEG-based indicators for language processing assessment, offering new directions for cross-linguistic investigations.