How individual differences shape ERP responses to visual statistical learning.

Statistical learning (SL) enables the extraction of regularities from sensory input, yet the neural dynamics supporting this process-particularly in the visual modality-remain incompletely understood. Sixty-seven adults were familiarized with a continuous stream of shape sequences containing statistical structure that defined shape triplets. We recorded EEG to familiar sequences (presented in isolation) and unfamiliar foils. Both early (N100) and late (N400) event related potential (ERP) components were significantly more negative for unfamiliar than familiar sequences, reflecting robust neural sensitivity to learned structure. Notably, these familiarity effects were evident in both high- and low-performing participants and were not predicted by overall behavioral sensitivity, suggesting that neural indices of learning can emerge independently of explicit recognition. Follow-up analyses incorporating trial-level accuracy revealed a striking crossover interaction: for sensitive participants, ERP familiarity effects were stronger on correct trials, whereas for insensitive participants, effects were larger on incorrect trials. These findings highlight a dissociation between neural and behavioral measures of statistical learning and underscore the value of ERPs in capturing latent learning processes that may elude conscious awareness.