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This paper introduces a novel EEG-based framework for assessing the cognitive impacts of LLM interactions on users, focusing on attention, cognitive load, and decision-making. The framework integrates an Interaction-Aware Language Transformer (IALT) and an Interaction-Optimized Reasoning Strategy (IORS) to model and refine reasoning paths in a cognitively aligned manner. Experiments on four EEG datasets (DEAP, AMIGOS, SEED, DREAMER) demonstrate that the proposed method outperforms existing models in emotion classification accuracy and alignment with cognitive signals, even with low-density EEG configurations.
LLMs' cognitive impact can now be assessed with fine-grained, real-time neural data, revealing actionable insights for designing more adaptive and cognitively aware AI systems.
Introduction The increasing integration of large language models (LLMs) into human-AI collaboration necessitates a deeper understanding of their cognitive impacts on users. Traditional evaluation methods have primarily focused on task performance, overlooking the underlying neural dynamics during interaction. Methods In this study, we introduce a novel framework that leverages electroencephalography (EEG) signals to assess how LLM interactions affect cognitive processes such as attention, cognitive load, and decision-making. Our framework integrates an Interaction-Aware Language Transformer (IALT), which enhances token-level modeling through dynamic attention mechanisms, and an Interaction-Optimized Reasoning Strategy (IORS), which employs reinforcement learning to refine reasoning paths in a cognitively aligned manner. Results By coupling these innovations with real-time neural data, the framework provides a fine-grained, interpretable assessment of LLM-induced cognitive changes. Extensive experiments on four benchmark EEG datasets Database for Emotion Analysis using Physiological Signals (DEAP), A Dataset for Affect, Personality and Mood Research on Individuals and Groups (AMIGOS), SJTU Emotion EEG Dataset (SEED), and Database for Emotion Recognition through EEG and ECG Signals (DREAMER) demonstrate that our method outperforms existing models in both emotion classification accuracy and alignment with cognitive signals. The architecture maintains high performance across varied EEG configurations, including low-density, noise-prone portable systems, highlighting its robustness and practical applicability. Discussion These findings offer actionable insights for designing more adaptive and cognitively aware LLM systems, and open new avenues for research at the intersection of artificial intelligence and neuroscience.
