Neural signatures of hyper-realistic AI-generated faces: dissociating behavioral indistinguishability from implicit neural evaluation

Recent advances in generative artificial intelligence (AI) have produced hyper-realistic synthetic faces that are increasingly difficult to distinguish from real human faces, raising critical questions about how such stimuli are encoded by human brain. Behavioral studies indicate that observers frequently misclassify AI-generated faces as real and often judge them as more familiar/ attractive than genuine faces (hyperrealism). We investigated whether neural processing differentiates real from highly realistic AI-generated faces despite observers' limited behavioral discrimination ability. Thirty participants viewed 440 real and GAN-generated male/female faces while their EEG was recorded. Behavioral validation confirmed that AI-generated faces were poorly identified as artificial and were perceived as more familiar and aesthetically appealing. Face-evoked ERPs showed systematic modulation by realism despite task irrelevance. AI-generated faces elicited enhanced N250, P300, PN400, and late positivity; a reduced engagement of ventral temporal, parietal, and limbic networks was found for real faces, especially male ones, according to swLORETA. Although hyper-realistic faces surpass behavioral detection thresholds, the brain remains sensitive to their artificial origin. Neural markers of familiarity and aesthetic appraisal are enhanced for AI faces, likely reflecting algorithmic averaging that accentuates prototypical features and attenuates sexual dimorphism, thereby revealing a dissociation between explicit recognition and implicit neural evaluation.