Cognitive Feedback: Decoding Human Feedback from Cognitive Signals

AbstractAlignment from human feedback has played a crucial role in enhancing the performance of large language models. However, conventional approaches typically require creating large amounts of explicit preference labels, which is costly, time-consuming, and demands sustained human attention. In this work, we propose Cognitive Feedback, a framework that infers preferences from electroencephalography (EEG) signals recorded while annotators simply read text, eliminating the need for explicit labeling. To our knowledge, this is the first empirical investigation of EEG-based feedback as an alternative to conventional human annotations for aligning language models. Experiments on controlled sentiment generation show that Cognitive Feedback achieves performance comparable to explicit human feedback, suggesting that brain-signal-derived preferences can provide a viable, lower-burden pathway for language model alignment.