The Regulation of Automatic Acceptance Priming on Instructed Fear: An ERP Study

Instructed fear is the learning of fear through verbal information, enabling efficient recognition of threat signals, making the decoding and recognition of fear-related electrophysiological signals crucial, and providing potential advancements for affective computing and brain-machine interface applications. Feature extraction of brain signals related to instructed fear has seen considerable progress in electrophysiological research, the neural mechanisms underlying the automatic regulation of fear remain to be explored. In this study, we used a speech transmission fear paradigm to induce fear and applied different automatic emotion regulation paradigms to initiate an acceptance strategy. By combining behavioral and electrophysiological methods, we examined the impact of automatic acceptance on instructed fear. The results showed that the automatic acceptance strategy could partially regulate instructed fear, significantly reducing participants' subjective feelings toward threat stimulus. However, the effects on beliefs and behavior were more limited. From the electrophysiological data, compared to neutral priming, acceptance priming led to smaller EPN and P3b amplitudes, suggesting a reduction in emotional arousal and alertness during the early stages of emotion recognition and attention.