A neural network model of when to retrieve and encode episodic memories.
A neural network model of when to retrieve and encode episodic memories.
复制标题
何时检索和编码情节记忆的神经网络模型。
DOI：
10.7554/elife.74445
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发表时间：
2022-02-10
期刊：
影响因子：
7.7
通讯作者：
Norman, Kenneth A.
中科院分区：
文献类型：
--
作者：
Lu, Qihong;Hasson, Uri;Norman, Kenneth A.
最近的人类行为和神经成像结果表明，人们在编码和提取情节记忆时是有选择性的。为了解释这些发现，我们训练了一个记忆增强的神经网络，让它使用情景记忆来支持在过去情况有时会重复发生的环境中对即将到来的状态的预测。我们发现，作为几个因素的函数，该网络学会了选择性提取，包括它对即将到来的状态的不确定性。此外，我们发现，在事件结束时(但不是在事件中期)选择性地对情节记忆进行编码会导致更好的后续预测性能。在所有这些情况下，选择性检索和编码的好处可以从降低检索无关记忆的风险的角度来解释。总体而言，这些建模结果提供了一个资源理性的解释，解释了为什么情节检索和编码应该是有选择性的，并导致几个可测试的预测。人脑可以记录特定事件的细节快照--例如事件发生的地点和时间--并在以后检索这些信息。回忆这些“情节记忆”可以帮助我们更好地了解我们当前的环境，并预测接下来会发生什么。对情景记忆的研究通常包括观察志愿者在执行简单而明确的任务时的情况，例如学习和回忆随机单词对的列表。然而，当没有人对我们进行测验时，当我们进行日常活动时，情节记忆是如何在野外发挥作用的就不太清楚了。最近，研究人员开始研究在更自然的情况下的记忆，例如，志愿者看电影时的记忆。在这里，Lu等人。已经建立了一个计算模型，可以预测我们的大脑在这些实验中何时存储和检索情节记忆。研究小组给了模型一个对应于事件不同阶段的输入序列，并要求它预测接下来会发生什么。直觉上，人们可能会认为，情节记忆的最佳用途是尽可能频繁地存储和检索快照。然而，Lu等人。研究发现，当模型更具选择性时，表现最好--即，优先存储事件结束时的情节记忆，并等待恢复它们，直到模型对当前情况的理解出现缺口。这一策略可能会帮助大脑避免提取不相关的记忆，这些记忆可能(反过来)导致大脑做出错误的预测并产生负面结果。这一模型使研究人员有可能预测大脑在特定实验中何时可能存储和检索情节记忆。卢等人公开分享了该模型的代码，以便其他研究人员能够在他们的研究中使用它来了解大脑在日常情况下是如何使用情节记忆的。
Recent human behavioral and neuroimaging results suggest that people are selective in when they encode and retrieve episodic memories. To explain these findings, we trained a memory-augmented neural network to use its episodic memory to support prediction of upcoming states in an environment where past situations sometimes reoccur. We found that the network learned to retrieve selectively as a function of several factors, including its uncertainty about the upcoming state. Additionally, we found that selectively encoding episodic memories at the end of an event (but not mid-event) led to better subsequent prediction performance. In all of these cases, the benefits of selective retrieval and encoding can be explained in terms of reducing the risk of retrieving irrelevant memories. Overall, these modeling results provide a resource-rational account of why episodic retrieval and encoding should be selective and lead to several testable predictions. The human brain can record snapshots of details from specific events – such as where and when the event took place – and retrieve this information later. Recalling these ‘episodic memories’ can help us gain a better understanding of our current surroundings and predict what will happen next. Studies of episodic memory have typically involved observing volunteers while they perform simple, well-defined tasks, such as learning and recalling lists of random pairs of words. However, it is less clear how episodic memory works ‘in the wild’ when no one is quizzing us, and we are going about everyday activities. Recently, researchers have started to study memory in more naturalistic situations, for example, while volunteers watch a movie. Here, Lu et al. have built a computational model that can predict when our brains store and retrieve episodic memories during these experiments. The team gave the model a sequence of inputs corresponding to different stages of an event, and asked it to predict what was coming next. Intuitively, one might think that the best use of episodic memory would be to store and retrieve snapshots as frequently as possible. However, Lu et al. found that the model performed best when it was more selective – that is, preferentially storing episodic memories at the end of events and waiting to recover them until there was a gap in the model’s understanding of the current situation. This strategy may help the brain to avoid retrieving irrelevant memories that might (in turn) result in the brain making incorrect predictions with negative outcomes. This model makes it possible for researchers to predict when the brain may store and retrieve episodic memories in a particular experiment. Lu et al. have openly shared the code for the model so that other researchers will be able to use it in their studies to understand how the brain uses episodic memory in everyday situations.