Submitted to Psychological Review

Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, we present a new model of how the brain gives rise to RIF in both seman-tic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular oscillations in feedback inhibition to strengthen weak parts of target memories and to weaken competing memories. We use the model to address several puzzling findings relating to RIF including: why retrieval practice leads to more forgetting than simply presenting the target item; how RIF is affected by the strength of competing memories and the strength of the target (to-be-retrieved) memory; and why RIF sometimes generalizes to “independent cues”, and sometimes does not. For all of these ques-tions, we show that the model can account for existing results, and we generate novel predictions regarding boundary conditions on these results. Contents Introduction: The puzzle of retrieval-induced forgetting 4 The scope of the paper..........