Early electrophysiological responses reflect precision-weighted prediction errors to face features.

Facial expressions serve as a key source of information about an individual's emotional state, intentions, and the surrounding environment. Efficient error processing of facial features is therefore particularly important for successful social interaction. Nonetheless, while simple feature-selective error processing has been well documented, the literature on error processing in complex visual features, such as those of facial stimuli, that may not generate specific predictions at the fine-grained level of simple ones, presents mixed findings. The present study aimed to determine whether facial feature-related processing activity reflects precision-weighted prediction errors (pwPEs), and to investigate how rapidly these feature-selective error signals emerge within the EEG epoch. Using the Hierarchical Gaussian Filter to model pwPE evolution, a consistent relationship was observed between pwPE trajectories and neuronal activity, with peak effects emerging at approximately 140 ms post-stimulus onset. These findings provide evidence that facial expression prediction errors are computed early during face visual processing. During social interactions, this timely error signaling likely enables the continuous updating of predictive models about a social partner's likely responses to environmental stimuli or interpersonal actions, facilitating rapid and adaptive behaviours that promote smooth social engagement.