Generalization of familiarity-related neural patterns to emotional facial expressions

Emotional facial expressions are known to bias face processing, yet it remains unclear whether such effects extend to neural signals associated with face familiarity. In this secondary analysis of openly available EEG data, we used cross‑dataset multivariate pattern analysis (MVPA) to test whether established neural signatures of face familiarity generalize across emotional expressions. Participants viewed faces in two independent experiments: one involving explicit emotion categorization (happy, angry, sad, neutral) and another involving personally familiar and unfamiliar identities. Classifiers trained to distinguish familiar from unfamiliar faces were cross‑applied to emotional expressions, and vice versa, using complementary relabeling strategies. Across analyses, neural patterns for angry expressions showed the strongest and most sustained generalization to familiarity‑related neural signals, emerging around 200 ms post‑stimulus and persisting throughout the trial (peak Cohen's d = 1.35 over posterior regions). Neural patterns for happy and sad expressions showed weaker and more transient generalization (200-400 ms), while neutral expressions consistently aligned with patterns for unfamiliarity. These findings demonstrate that threat‑related facial expressions exhibit neural dynamics that show convergence in pattern structure with established familiarity signals, extending prior evidence that emotional expressions, particularly anger, systematically modulate face representations beyond identity.