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Neural Differentiation Tracks Improved Recall of Competing Memories Following Interleaved Study and Retrieval Practice

机译:交错学习和检索练习后神经分化跟踪改善了对记忆的回忆

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摘要

Selective retrieval of overlapping memories can generate competition. How does the brain adaptively resolve this competition? One possibility is that competing memories are inhibited; in support of this view, numerous studies have found that selective retrieval leads to forgetting of memories that are related to the just-retrieved memory. However, this retrieval-induced forgetting (RIF) effect can be eliminated or even reversed if participants are given opportunities to restudy the materials between retrieval attempts. Here, we outline an explanation for such a reversal, rooted in a neural network model of RIF that predicts representational differentiation when restudy is interleaved with selective retrieval. To test this hypothesis, we measured changes in pattern similarity of the BOLD fMRI signal elicited by related memories after undergoing interleaved competitive retrieval and restudy. Reduced pattern similarity within the hippocampus positively correlated with retrieval-induced facilitation of competing memories. This result is consistent with an adaptive differentiation process that allows individuals to learn to distinguish between once-confusable memories.
机译:重叠存储器的选择性检索会产生竞争。大脑如何自适应地解决这一竞争?一种可能性是竞争性记忆被抑制。为了支持这种观点,许多研究发现选择性检索导致忘记与刚取回的记忆有关的记忆。但是,如果参与者有机会在重新尝试之间重新学习资料,则这种消除搜索遗忘(RIF)的作用可以消除甚至逆转。在这里,我们概述了这种逆转的一种解释,这种解释源于RIF的神经网络模型,该模型在重新研究与选择性检索交错时预测表示差异。为了验证这一假设,我们在进行交错竞争检索和重新研究后,测量了相关记忆引起的BOLD fMRI信号的模式相似性变化。海马内模式相似性降低与检索记忆促进竞争性记忆正相关。该结果与自适应分化过程一致,该过程使个人能够学习区分曾经容易混淆的记忆。

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