Proceedings of the Annual Meeting of the Cognitive Science Society

Due to transmission delays, the perceptual information our brain can access quickly becomes outdated as events unfold in real-time. We suggest our perceptual system learns internal representations that encode sequences (or timelines) rather than single points to compensate for transmission de- lays. Specifically, we investigate the dynamic predictive coding (DPC) model in which high-level states predict the transition dynamics of lower-level states and represent lower-level state sequences. We show that a two-level DPC network trained to predict videos captures several aspects of the well-known flash-lag illusion and exhibits both predictive and postdictive effects resembling those observed in human visual motion processing. Our results support the view that visual perception relies on temporally abstracted representations that encode sequences (or timelines) rather than single time points.