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 记忆信息的能力，无论是在短暂或长时间的延迟之后，都是人类的基本能力。有 对孤立地理解工作记忆和长期记忆的大量研究， 关于工作记忆和长期记忆之间相互作用的长期争论。本研究 该提案将通过调查和剖析网关假设来解决关键的知识差距， 描述工作记忆延迟活动如何预测后来记住的信息，并建立大脑- 利用对工作记忆的实时洞察的计算机接口延迟活动以改变以后的记忆。一 更好地理解工作记忆和长期记忆如何相互作用将有利于许多人 以这些系统的缺陷为特征的精神障碍。最近的实证研究表明， 揭示了工作记忆容量和长期记忆之间的联系，然而， 在工作记忆中维持多个项目的基础。例如，有一些神经信号 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信号 工作记忆中的项目。这些子过程中的一个或两个，数量和位置，可以预测长期的 记忆我将使用多元解码与时间分辨神经科学技术、EEG（Aim 1)和颅内EEG（目的2），以表征这些工作记忆子过程及其与 长期记忆然后，作为一个独立的调查员，我将建立工具，可以跟踪延迟活动在真实的 时间和适应性设计的实验视工作记忆的项目的数量和位置。这 将测试工作记忆和长期记忆之间的关系的详细和具体的概念化。 记忆本研究建议的研究和培训目标将由一个咨询小组进一步推进， 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。本研究 建议包括通常孤立的认知过程（工作记忆和长期记忆）， 互补的时间分辨方法（EEG和iEEG），以及计算复杂的多变量 分析能够敏感地解码工作记忆中的信息。最后，本研究将 开发创新的实时工具来跟踪记住的信息并预测未来的长期记忆 性能这项研究提案的短期目标是开发一个复合模型， 工作记忆延迟活动的每时每刻子过程预测长期记忆结果。这 将为工作记忆和长期记忆之间的关系提供新的见解。长期 我研究计划的目标是全面描述影响我们行为的各种因素 记住，为了建立可以增强记忆的工具。