Shared Neural Codes for Emotion Recognition in Emoji and Human Faces

Facial expressions are critical social signals, essential for human communication. This study used EEG to investigate the neural dynamics of the processing of emotional expressions in real and emoji faces, using a data-driven approach. Across two experiments with identical paradigms, two separate sets of participants viewed facial expressions (happy, angry, sad, neutral) in real faces (4 female and 4 male identities, n = 24) or emojis (6 platforms, n = 25) while performing a two-alternative forced-choice emotion recognition task. Time-resolved multivariate classification and spatio-temporal searchlight analyses revealed robust decoding of emotional expressions within and across experiments. Consistent effects emerged early and peaked between 145-160 ms over posterior-occipital and parietal regions. Notably, robust cross-classification between real and emoji faces demonstrated that face-like emoji stimuli evoke neural responses comparable to those elicited by real faces, with more sustained effects over right posterior sites. These findings suggest that the brain uses overlapping spatio-temporal codes for naturalistic and symbolic facial expressions, providing new insights into the neural coding of social signals and the representational overlap between natural and artificial emotional expressions.