Violation Differentiation

When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here, we explore what happens when such previously mispredicted items are later reencountered. According to prior neural network simulations, this sequence of events-misprediction and subsequent restudy-should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context. We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared with items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Therefore, the consequences of prediction error go beyond memory weakening. If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening. SIGNIFICANCE STATEMENT Competition between overlapping memories leads to weakening of nontarget memories over time, making it easier to access target memories. However, a nontarget memory in one context might become a target memory in another context. How do such memories get restrengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here, we provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning.当某一项目在特定语境下被预测却未被证实（即预测违背）时，该项目的记忆会被削弱（Kim等，2014）。本研究旨在探讨此前被预测错误的项目在后续再次被呈现时会发生何种变化。 既往神经网络模拟研究表明，这一事件序列——预测错误及后续的重新学习——应当会使该项目的神经表征脱离其引发预测错误的先前语境。具体而言，预测错误会削弱表征中与先前语境共享特征的连接权重，而重新学习则可将与先前语境不共享的新特征纳入该表征。这一预测错误与重新学习的循环过程，其净效应应当是推动该项目的神经表征脱离先前语境对应的神经表征。 本研究通过功能磁共振成像（fMRI）技术，追踪海马体（hippocampus）——这一负责记忆表征与预测生成的关键脑区——中项目特异性血氧水平依赖（Blood Oxygen Level Dependent, BOLD）活动模式的变化，对上述假说进行了验证。在左侧CA2/3/DG脑区中，相较于仅无预测错误的对照条件下的项目，反复经历预测错误并接受重新学习的项目展现出更强的神经分化效应。本研究还以逐试次（trial-by-trial）的方式测量了预测强度，结果发现某一项目的预测错误程度越高，其神经分化效应越强，进一步支持了本研究的假说。 由此可见，预测错误的影响不止于记忆削弱。若被预测错误的项目得到重新学习，大脑会自适应地分化其记忆表征，以提升后续预测的准确性，并避免其进一步被削弱。 研究意义 重叠记忆之间的竞争会随时间推移削弱非目标记忆，使目标记忆更易被提取。然而，某一语境下的非目标记忆，在另一语境下可能成为目标记忆。那么，此类记忆如何在不再次加剧竞争的前提下得到重新强化？计算模型表明，大脑通过以下方式处理这一问题：削弱与先前语境共享特征的神经连接，并新增与先前语境无关的新特征的连接。其结果是神经表征脱离先前语境的对应表征。本研究通过功能磁共振成像（fMRI）追踪海马体中单个记忆的神经表征及其随学习发生的变化，为该理论提供了实验证据支持。