Tracking the dynamics of how schemas scaffold recall

项目摘要 我们生活中的每一次新经历都发生在熟悉的环境和情况的背景下。然而， 大多数关于记忆的研究都集中在人工记忆单词列表、符号或图片；这些 研究没有有意义地解决如何组织关于世界的先验知识(例如，以熟悉的形式 空间地图，或餐馆用餐如何随着时间的推移而展开的知识)可以为新的学习奠定基础。在建议的 研究，我的目标是准确地描述先前的知识和新信息是如何以及在哪里 它们在编码时是如何联系在一起的，以及它们在回忆时如何相互作用，从而允许记忆被 已取回。在我的F99阶段的第一个拟议研究中，我测试了这样一个假设，即海马区在 学习过程中的事件边界将新信息(即对象)绑定到现有知识的支架(即 熟悉位置知识)，以及回忆过程中的海马体激活调节成功的提取 从存储绑定对象的位置删除绑定对象。我还测试了这样一种假设，即 大脑中空间位置的表示将减少存储在这些位置的对象之间的干扰。 使用先验知识作为脚手架有一个潜在的缺点：当有太多的信息时 依附在脚手架的一部分，新旧记忆会相互干扰。那么，怎么可能 有些人把重新获得新的记忆放在与旧的(现在不相关的)记忆联系起来的优先顺序上 送到刑台上？最近关于刻意遗忘的研究提出了解决这一局限的方法。具体而言，在 我提出的第二项研究，我检验了这一假设(由神经生理学证据支持，之前 神经成像结果和计算模型)，以前编码的记忆可以通过 适度激活他们的神经表征，从而“清理”脚手架，减少干扰。在……里面 在K00阶段，我将扩展我的研究，以确定临床人群如何使用先前知识的病理学 为了解释和记住他们的经历，使用计算精神病学的工具；我还计划设计 解决这些问题的新技术工具。总体而言，拟议的项目使用了自然主义 以及生态有效的刺激(以连续刺激和身临其境的虚拟现实的形式)与 先进的机器学习工具应用于脑成像数据，以研究基本性质如何 新信息和旧信息被链接在一起，以便学习。从长远来看，这个项目的发现是关于 如何最大限度地利用现有知识来支持新的学习将导致开发工具来 帮助记忆受损的人更好地利用先前的知识来支持新的学习，以及 对先验知识不足使其无法正确学习的群体采取补救措施。

Project Summary Every new experience in our life takes place within the context of familiar environments and situations. However, most research on memory has focused on the artificial memorization of word lists, symbols or pictures; these studies do not meaningfully address how structured prior knowledge about the world (e.g., in the form of a familiar spatial map, or knowledge of how restaurant meals unfold over time) can scaffold new learning. In the proposed studies, I aim to precisely characterize how and where prior knowledge and new information are represented, how they get linked at encoding, and how they interact at recall to allow memories to be retrieved. In the first proposed study of my F99 phase, I test the hypothesis that hippocampal engagement at event boundaries during learning binds new information (i.e. objects) to the scaffold of existing knowledge (i.e. knowledge of a familiar location), and that hippocampal activation during recall mediates the successful retrieval of the bound object from the location in which it was stored. I also test the hypothesis that distinctive representations of spatial locations in the brain will reduce interference between objects stored in those locations. There is a potential downside to using prior knowledge as a scaffold: When there is too much information attached to one part of the scaffold, old and new memories will interfere with each other. How, then, could someone prioritize the retrieval of new memories over older (now-irrelevant) memories that were linked to the scaffold? Recent research on intentional forgetting suggests a solution to this limitation. Specifically, in my second proposed study, I test the hypothesis (supported by neurophysiological evidence, prior neuroimaging results, and computational models) that previously encoded memories can be weakened by moderately activating their neural representation, thereby “cleaning” the scaffold and reducing interference. In the K00 phase, I will extend my research to identify pathologies in how clinical populations use prior knowledge to interpret and remember their experiences, using tools from computational psychiatry; I also plan to design new technological tools to address these issues. Overall, the proposed project makes use of naturalistic and ecologically valid stimuli (in the form of continuous stimuli and immersive virtual reality) paired with advanced machine learning tools applied to brain imaging data, to study the fundamental nature of how new and old information are linked to allow learning. In the long-term, the findings from this project regarding how prior knowledge can be optimally leveraged to support new learning will lead to the development of tools to help memory-impaired individuals make better use of prior knowledge to support new learning, as well as remedies for groups where deficiencies in prior knowledge prevent them from learning properly.