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 记忆系统的进化是为了告知生物体在探索过程中的持续学习和决策 and engage从事with an extremely极大complicated复杂world世界.特别是人类有一种非凡的能力， 复杂的事件序列：我们可以很容易地重建一个关于过去一小时或一天的叙述， 记忆在这种真实世界的记忆中，语义和因果联系变得极其重要， 定义了一个跨越时间的关系网络来指导回忆。例如，您的一天可能包含两个 “中心”活动：一个需要去几家商店购买用品的晚宴，以及一个早晨的电话 说你的孩子发烧了，需要回家;每一个都产生了大量的事件， 天这些时刻之间的丰富关联形成了一个“事件网络”， 你的决定将引导每一件事如何展开。虽然研究表明， 简单的项目是重要的记忆组织及其伴随的神经计算，没有现有的 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 在自然主义研究中，大多数使用被动观看的电影或故事;参与者没有选择 或追求的目标。这种对真实的的高阶网络属性和意志方面的关注的缺乏- 世界的经验阻碍了确定具有生态意义的皮层动力学的努力 记忆过程 我们试图了解记忆编码和现实事件的检索是如何实现的， 大脑系统之间的皮质表征和相互作用。要做到这一点，需要具有两个关键 美德.先知-愿首先，刺激必须足够复杂。长期以来，记忆研究人员一直专注于还原 具有孤立刺激的场景，故意破坏语义和因果关系。相比之下，我们 实验使用真实的事件，这些事件彼此之间有着丰富的联系， 事件网络结构的多样性。其次，参与者必须在创造记忆方面发挥积极作用。 真实的世界中的生物体可以有意识地与它们的输入流互动：在一个拥挤的聚会上，你可以选择 探索厨房或客厅，与生物学家或音乐家交谈，早点离开或待到黎明。我们 将测试参与者的意志行为，因为他们互动，并积极寻求有关他们的信息， 环境，形状事件网络和事件的神经表示。总之，这些实验将 提供了新的框架和工具来研究自然经验中的涌现高阶结构 控制着编码和回忆的神经机制。通过提高生态有效性水平 和刺激的复杂性在人类记忆研究中，我们希望能够帮助揭示大脑与行为的关系， 在更简单的范例中不明显，并增加实验室发现到现实世界的可翻译性 应用.