Tracking Visual Statistical Learning with Steady-State Visual Evoked Potentials: Effects of Exemplar and Category Information

Abstract

This study examined visual statistical learning using EEG-based steady-state visual evoked potentials (SSVEP). Fifty-one adults were exposed to image sequences organized into triplets across three conditions (n = 17 per condition) in which the alignment of category-level and exemplar-level information was manipulated. Neural entrainment at the triplet frequency (1.11 Hz) differed significantly across conditions (ηp2 = .13), with stronger responses in the Single-Category and No-Category conditions than in the Mixed-Category condition. There were no differences at the image frequency (3.33 Hz; ηp2 = .05). Behavioral reaction times mirrored this pattern, showing faster responses to the last exemplar in the triplet in the Single-Category (ηp2 = .71) and No-Category (ηp2 = .22) conditions, but not in the Mixed-Category (ηp2 = .10) condition. Both signal-to-noise ratio (SNR) and inter-trial coherence (ITC) captured neural entrainment across fronto-central and parietal-occipital electrode clusters. These findings validate SSVEP as an online measure of visual statistical learning and demonstrate that category-exemplar mismatch interfered with statistical learning.