Long-term consequences of visual working memory

视觉工作记忆的长期后果

• 批准号: 10523326
• 负责人: Megan Teresa deBettencourt
• 金额: $ 10.72万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-08 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10523326
• 关键词: Address; Adult; Auditory; Behavioral; Brain; Chicago; Clinical; Computers; Delayed Memory; Detection; Electroencephalography; Empirical Research; Epilepsy; Failure; Future; Goals; Human; Individual; Intervention; Knowledge; Link; Location; Machine Learning; Measures; Memory; Memory impairment; Mental disorders; Methods; Mind; Modeling; Multivariate Analysis; Neurosciences; Outcome; Patients; Pattern; Performance; Phase; Procedures; Process; Research; Research Personnel; Research Proposals; Research Training; Scalp structure; Short-Term Memory; Signal Transduction; System; Techniques; Testing; Time; Universities; Visual; base; brain computer interface; cognitive process; cognitive system; design; experimental study; improved; innovation; insight; long term memory; memory recognition; nervous system disorder; neural patterning; neuromechanism; practical application; programs; relating to nervous system; research study; success; tool

Project Summary The ability to remember information, whether after short or long delays, is a fundamental human ability. There is enormous research into understanding working memory and long-term memory in isolation, but also a longstanding debate about the interactions between working memory and long-term memory. This research proposal would address a critical knowledge gap by investigating and dissecting the gateway hypothesis, to characterize how working memory delay activity predicts what information is later remembered and to build brain- computer interfaces that leverage real-time insight into working memory delay activity to alter later memory. A better understanding how working memory and long-term memory interact would be beneficial for the numerous psychiatric disorders which are characterized by deficits in these systems. Recent empirical research has revealed links between working memory capacity and long-term memory, however multiple sub-processes underlie maintaining multiple items in working memory. For example, there are neural signatures that correspond to the number of items in working memory, and distinct neural signatures that correspond to the spatial locations of items in working memory. Either or both of those sub-processes, number and location, could predict long-term memory. I will use multivariate decoding in conjunction with time resolved neuroscience techniques, EEG (Aim 1) and intracranial EEG (Aim 2) to characterize these working memory sub-processes and their relationship to long-term memory. Then, as an independent investigator, I will build tools that can track delay activity in real time and adaptively design experiments contingent to the number and location of items of working memory. This will test a detailed and specific conceptualization of the relationship between working memory and long-term memory. The research and training goals of this research proposal will be furthered by an advising team of cognitive, systems, and clinical neuroscientists at the University of Chicago and UC Berkeley. This research proposal encompasses cognitive processes that are often siloed (working memory and long-term memory), complementary temporally resolved methods (EEG and iEEG), and computationally sophisticated multivariate analyses capable of sensitively decoding information in working memory. Finally, this research proposal will develop innovative real-time tools to track information held in mind and forecast future long-term memory performance. The short-term goal of this research proposal is to develop a composite model of how distinct moment-by-moment subprocesses of working memory delay activity predict long-term memory outcome. This will provide new insights into the relationship between working memory and long-term memory. The long-term goal for my research program is to comprehensively characterize the diverse factors that influence what we remember, in order to build tools that can enhance memory.

项目摘要 记住信息的能力，无论是短暂的还是长时间的，都是人类的基本能力。有 大量研究孤立地理解工作记忆和长期记忆的研究，但也是一个 关于工作记忆与长期记忆之间的相互作用的长期辩论。这项研究 提案将通过调查和剖析门户假设来解决关键的知识差距 表征工作记忆延迟活动如何预测后来被记住的信息并构建大脑 - 将实时洞察力的计算机接口到工作内存延迟活动以更改以后的内存。一个 更好地了解工作记忆和长期记忆如何相互作用将对众多 以这些系统缺陷为特征的精神疾病。最近的实证研究已经 揭示了工作记忆能力与长期内存之间的联系，但是多个子过程 在工作记忆中维护多个项目的基础。例如，有一些神经签名 对于工作记忆中的项目数量以及与空间位置相对应的不同神经特征 工作记忆中的项目。这些子过程，数字和位置都可以预测长期 记忆。我将与时间解决的神经科学技术结合使用多元解码（AIM） 1）和颅内脑电图（目标2）来表征这些工作记忆子处理及其与它们与 长期记忆。然后，作为一名独立研究者，我将构建可以跟踪实际延迟活动的工具 时间和自适应设计实验取决于工作记忆项目的数量和位置。这 将测试工作记忆与长期关系之间关系的详细概念化 记忆。该研究建议的研究和培训目标将由一个咨询团队进一步 芝加哥大学和加州大学伯克利分校的认知，系统和临床神经科学家。这项研究 建议包含通常被孤立的认知过程（工作记忆和长期记忆）， 互补的时间解决方法（EEG和IEEG）以及计算复杂的多变量 分析能够在工作记忆中敏感地解码信息。最后，这项研究建议将 开发创新的实时工具来跟踪持有的信息并预测未来的长期记忆 表现。该研究建议的短期目标是开发一个复合模型，以了解如何不同 工作记忆延迟活动的逐矩子过程预测了长期记忆结果。这 将为工作记忆与长期内存之间的关系提供新的见解。长期 我的研究计划的目标是全面地描述影响我们什么的不同因素 请记住，为了构建可以增强内存的工具。