Submitted to Psychological Review

Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of similar memories. Here, we present a new model of how the brain gives rise to RIF. The core of the model is a recently developed neural network learning algorithm (based on neural theta oscillations) 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 from the RIF literature, 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. We also use the model to address non-monotonic effects of retrieval practice, whereby repeated practice first helps, then hurts recall of competing memories. For all of these questions, 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 for-getting 3 The scope of the paper.............