Event networks and the neural representations that support real-world memory

支持现实世界记忆的事件网络和神经表征

• 批准号: 10717508
• 负责人: JANICE CHEN
• 金额: $ 54.36万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10717508
• 关键词: Attention; Behavior; Belief; Brain; Child; Collection; Complex; Corpus striatum structure; Crowding; Data; Decision Making; Development; Dimensions; Disease; Environment; Event; Fever; Functional Magnetic Resonance Imaging; Goals; Graph; Higher Order Chromatin Structure; Hippocampus; Home; Hour; Human; Internet; Judgment; Laboratory Finding; Learning; Life; Link; Location; Machine Learning; Measures; Memory; Mental Health; Modeling; Organism; Participant; Pattern; Persons; Prevalence; Property; Research; Research Personnel; Role; Schizophrenia; Semantics; Services; Shapes; Stimulus; Stream; Structure; System; Techniques; Telephone; Testing; Time; Visit; Volition; Work; behavior influence; brain behavior; experience; experimental study; improved; innovation; memory encoding; memory process; memory retrieval; movie; musician; neural; neural correlate; neuromechanism; novel; real world application; reconstruction; tool

Project Summary Memory systems evolved to inform the continual learning and decision making of organisms as they explore and engage with an enormously complicated world. Humans in particular have a remarkable ability to recount complex sequences of events: we can easily reconstruct a narrative about the past hour or day purely from memory. In such real-world remembering, semantic and causal associations become exceedingly important, defining a web of relational connections across time to guide recall. For example, your day might contain two "hub" events: a dinner party that requires visiting several shops to pick up supplies, and a morning phone call saying that your child has a fever and needs to go home; each spawns a multitude of events that make up your day. Rich associations among these moments form an “event network” whose local and global properties shape recall; your decisions guide how each event will unfold. While studies show that relations between simple items are important for memory organization and its accompanying neural computations, no existing models consider the higher-order structure of networks composed from inter-related naturalistic events. Even among naturalistic studies, most use passively-viewed movies or stories; participants have no choices to make or goals to pursue. This lack of attention to the higher-order network properties and volitional aspects of real- world experiences has hindered efforts to identify the cortical dynamics that underlie ecologically meaningful memory processes. We seek to understand how memory encoding and retrieval of realistic events is implemented, in terms of cortical representations and interactions between brain systems. Doing so requires paradigms with two critical attributes. First, the stimuli must be sufficiently complex. Memory researchers have long focused on reductive scenarios with isolated stimuli that intentionally destroy semantic and causal connections. In contrast, our experiments use realistic events that are richly associated with each other and will naturally generate a diversity of event network structures. Second, participants must take an active role in creating their memories. Organisms in the real world can volitionally interact with their input stream: at a crowded party, you can choose to explore the kitchen or the living room, talk to the biologist or the musician, leave early or stay until dawn. We will test how participants' volitional behaviors, as they interact with and actively seek information about their environment, shape event networks and neural representations of events. Altogether, these experiments will provide novel frameworks and tools to examine how emergent higher-order structure in natural experiences governs the neural mechanisms underlying encoding and recall. By advancing the level of ecological validity and stimulus complexity in human memory research, we expect to help uncover brain-behavior relationships not apparent in simpler paradigms, and increase the translatability of laboratory findings to real-world applications.

项目摘要 记忆系统演变为探索生物时的持续学习和决策 并与一个非常复杂的世界互动。人类特别具有重大叙述的能力 事件的复杂序列：我们可以轻松地重建有关过去一个小时或一天的叙述 记忆。在这种现实世界中，语义和因果关系变得越来越重要， 跨时间定义关系网络以指导召回。例如，您的一天可能包含两个 “集线器”活动：一个晚宴，需要访问几家商店来收取用品，还有一个早上电话 说你的孩子发烧，需要回家；每个都产生了许多构成您的事件 天。这些时刻中的丰富联系形成了一个“事件网络”，其本地和全球属性 形状回忆；您的决定指导每个事件的展开方式。研究表明 简单项目对于记忆组织及其参与的神经计算很重要，没有现有 模型考虑由相互关联的自然事件组成的网络的高阶结构。甚至 在自然研究中，大多数使用被动观看的电影或故事；参与者没有选择 或购买目标。这种对更高阶段的网络属性的关注和实际方面的关注 世界经验阻碍了识别生态意义上的皮质动态的努力 内存过程。 我们试图了解如何根据 大脑系统之间的皮质表示和相互作用。这样做需要两个关键的范式 属性。首先，刺激必须足够复杂。记忆研究人员长期以来关注减少 具有孤立刺激的场景，故意破坏语义和因果关系。相反，我们的 实验使用彼此丰富相关的现实事件，自然会产生 事件网络结构的多样性。其次，参与者必须在创造自己的记忆中发挥积极作用。 现实世界中的有机体可以自愿与他们的输入流相互作用：在拥挤的聚会上，您可以选择 要探索厨房或客厅，请与生物学家或音乐家交谈，提早离开或呆直到黎明。我们 将测试参与者在与之互动并积极寻求有关其的信息时的意志行为 事件的环境，形状事件网络和神经表示。这些实验总共 提供新颖的框架和工具，以检查自然体验中新兴的高阶结构 控制编码和回忆的基础神经机制。通过提高生态有效性的水平 和人类记忆研究中的刺激复杂性，我们希望帮助发现脑行为关系 在更简单的范式中并不明显，并将实验室发现的可翻译性增加到现实世界 申请。