Abrupt Scene Onsets and Gradually Emerging Scene Information Produce Distinct EEG Decoding Dynamics

Multivariate analyses of M/EEG data are typically performed on neural responses time-locked to discrete stimulus onsets. Such designs usually reveal high decoding performance during the initial transient response (0-500 ms), which subsequently drops to a lower, sustained level. Here, we examined time-resolved EEG decoding of natural scene processing when scenes gradually enter the visual field without a clear onset. We created video sequences in which one scene category (e.g., a beach) smoothly transitioned into another category (e.g., a forest) by blending two scenes into a single composite panorama and moving a square aperture across it. We compared EEG decoding for the first scenes within the transitions, which appeared with a sudden, artificial onset, to the second scenes, which emerged naturalistically as the videos progressed. For the first scenes, we observed robust category decoding from 60 ms after onset with a clear peak structure. For the second scene, category decoding was markedly weaker and showed no discernible peak structure. Realigning the appearance of category-diagnostic content for the second scene using deep neural networks did not enhance decoding or recover a peak structure. Further, classifiers trained on the first scene generalized to the second, but with a broad, temporally-diffuse pattern, instead of a diagonal pattern more consistent with a shared hierarchical processing timeline. Together, these findings demonstrate that time-resolved EEG decoding is sensitive to stimulus-presentation context. Accordingly, temporal decoding patterns obtained in conventional trial-based paradigms may not generalize unchanged to conditions involving gradual scene transitions and continuous visual input.