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This paper explores whether valence features can reflect morality in natural language by analyzing human annotations of moral scenarios, finding significant correlations and achieving a Matthew's correlation coefficient of 0.764 for binary morality classification.
This paper discovers a shared valence axis (V-axis) across modern LLMs and human EEG signals, showing that a single direction from LLM internal representations aligns with neural responses to emotional stimuli. It also identifies the saturation regularity, explaining why LLM-derived supervision fails to improve EEG decoding and how leveraging residual diversity boosts performance.