Many events of interest in human activity-based multimedia applications exhibit a high degree of temporal structure. This structure generates expectancies regarding the occurrence and location of subsequent events. In the context of switching state-space models, we develop a general Bayesian framework for representing temporal expectancies and fusing them with raw sense-data to improve both event segmentation and temporal structure identification. Furthermore, we develop a new cognitive model for event anticipation which adapts to incoming sense-data in real time. Comparative advantages of the proposed framework are realized in controlled experiments involving partially-observed, quasi-periodic event streams.