Evidence suggests that when memories are reactivated they become labile and can be updated or even erased. Reactivation induces plasticity in memory representations, rendering them fragile, much as they were after initial acquisition. When a memory has been reactivated it must be re-stabilized, which requires reconsolidation. A recent set of studies established the phenomenon of memory reconsolidation for episodic memory (Hupbach et al., 2007, 2008, 2011). That reconsolidation effects apply to explicit memory, which requires conscious recollection, has far reaching implications. In the Hupbach et al. studies the ability of subtle reminders to trigger reconsolidation was investigated; these reminders consisted of the same spatial context, the same experimenter and a reminder question. Given we live in a predictable world, episodes are not random occurrences of events in time and space, but instead consist of statistical and semantic regularities. This leaves open the question of whether semantic relations and statistical regularities between episodes can trigger a reactivation of episodic memory. If so, how would this affect the status of the reactivated memory? This dissertation explored the role of semantic relatedness between the elements of different episodes in memory reactivation and subsequent updating. We focused particularly on categorical and contextual aspects of semantic relations. A series of experiments considered different kinds of semantic relations between elements of episodes, providing evidence of memory reactivation and updating as a consequence of basic level category relations between items in two separate episodes. We also tested the predictions of the Temporal Context Model (TCM) (Sederberg et al., 2011) for our experimental paradigm and show that the current TCM model is not able to account for all the effects of semantic relatedness in the reconsolidation paradigm. Finally, we explore an alternative approach that seeks to explain memory reconsolidation as Bayesian Inference. Our results provide support for this Bayesian framework, showing the potential of it for exploring different aspects of memory organization.