Tracking the dynamics of how schemas scaffold recall

项目摘要 我们生活中的每一次新体验都发生在熟悉的环境和情况中。然而，在这方面， 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关于世界的结构化先验知识（例如，以一种熟悉的 空间地图，或餐馆饭菜如何随时间展开的知识）可以为新的学习提供支撑。拟议 研究，我的目标是准确地描述如何以及在哪里先验知识和新信息是 代表，他们如何在编码中联系起来，以及他们如何在回忆中相互作用，让记忆被 找回了在我的F99阶段的第一个研究中，我测试了海马参与的假设， 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），且在回忆期间海马激活介导成功的提取 从绑定对象的存储位置获取绑定对象的位置。我还检验了一个假设， 大脑中空间位置的表征将减少存储在这些位置中的对象之间的干扰。 使用先验知识作为脚手架有一个潜在的缺点：当信息太多时， 连接到脚手架的一部分，旧的和新的记忆会相互干扰。那么， 有些人优先提取新的记忆，而不是旧的（现在无关的）记忆， 上断头台吗最近对有意遗忘的研究提出了解决这一限制的方法。具体到 我的第二个研究建议，我测试的假设（支持神经生理学证据，事先 神经成像结果和计算模型），以前编码的记忆可以被削弱， 适度激活它们的神经表征，从而“清洁”支架并减少干扰。在 在K 00阶段，我将扩展我的研究，以确定临床人群如何使用先验知识的病理学 来解释和记忆他们的经历，使用计算精神病学的工具;我还计划设计 新的技术工具来解决这些问题。总的来说，拟议的项目利用自然主义 和生态有效的刺激（以连续刺激和沉浸式虚拟现实的形式）， 先进的机器学习工具应用于大脑成像数据，研究如何从根本上 新的和旧的信息被链接以允许学习。从长远来看，该项目的研究结果 如何最佳地利用先前的知识来支持新的学习将导致开发工具， 帮助记忆力受损的人更好地利用先前的知识来支持新的学习，以及 为缺乏先验知识而无法正确学习的群体提供补救措施。

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.