Retrieval-Induced Facilitation in Real-World Scenarios

现实场景中检索诱导的促进

• 批准号: 10464415
• 负责人: Ashley Williams
• 金额: $ 4.41万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10464415
• 关键词: Address; Affect; Automobile Driving; Behavioral; Behavioral Paradigm; Biological Models; Clinical; Complex; Computer Models; Cues; Data; Deglutition; Elements; Environment; Episodic memory; Event; Experimental Models; Exposure to; Hippocampus (Brain); Image; Impairment; Information Retrieval; Laboratory Study; Lead; Learning; Life; Life Experience; Memory; Memory impairment; Methods; Modeling; Nature; Neocortex; Participant; Patients; Performance; Persons; Population; Process; Research; Retrieval; Schizophrenia; Signal Transduction; Stimulus; Stream; System; Techniques; Testing; Time; Training; Wing; Work; base; cognitive neuroscience; entorhinal cortex; experience; forgetting; innovation; memory retrieval; paired stimuli

Project Summary Episodic memory, or the retrieval of information from events, is a complex process that is dynamic in nature. When retrieving this information, there are changes that occur. These changes can either enhance (i.e. retrieval-induced facilitation [RIFA]) or inhibit (i.e. retrieval-induced forgetting) memories for other, related information, and the mechanism of this enhancement/inhibition is unclear. In previous research, these concepts have been explored in studies using static images, word pairs, or literary passages to test these phenomena. In real life, we receive a constant, continuous stream of information that we segment into events with distinct boundaries. There is good reason to believe that retrieving one part of an event might lead to RIFA for other parts of the same event, but that event boundaries might restrict the benefits of retrieval practice. I propose to use an innovative behavioral paradigm in combination with computational modeling to address how retrieval impacts memory for naturalistic events. I have developed a task in which participants are exposed to videos depicting relatable events (i.e. going grocery shopping) that are separated by event boundaries. Aim 1 is to understand the relationship between RIFA and event segmentation. Based on prior research indicating a connection between memory retrieval and event segmentation, it is proposed that retrieval practice will facilitate retention of other information from the same event, but RIFA will not occur between events. In Aim 2, using computational modeling, we propose a possible mechanism of RIFA. Based on prior models, such as the Complementary Learning Systems (CLS) model, this mechanism likely involves both error-driven and Hebbian learning, with emphasis on the connection between the entorhinal cortex, the hippocampus, and the neocortex. In the proposed study, we will expand upon previous models on RIFA to better characterize the relationship between these regions in event-based, realistic scenarios. We expect that with retrieval practice the hippocampus will become more efficient at driving learning in the neocortex, which will ultimately lead to better accuracy for information within the same event as the retrieval practiced information due to a shared event context. Starting with a simple context change to mimic a change in event, we will increasingly make the model more complex by training the model to use prediction errors to mark event boundaries, then ultimately, inputting naturalistic videos to best mimic a real-life scenario. This project builds on my previous work and addressing these aims will give me new training in the most advanced methods in cognitive neuroscience, specifically computational modeling. More broadly, we have shown that memory retrieval practice can be a clinically valuable approach in patients with schizophrenia (Ragland et al, 2020), and these studies will give us the information we need to optimize this method to enhance everyday episodic memory in psychiatric populations.

项目摘要 情景记忆，或从事件中提取信息，是一个复杂的过程，本质上是动态的。 检索此信息时，会发生更改。这些变化可以增强（即， 检索诱导的促进[RIFA]）或抑制（即检索诱导的遗忘）对其他相关记忆的记忆 信息，并且这种增强/抑制的机制尚不清楚。在以前的研究中，这些 在一些研究中，人们利用静态图像、成对的单词或文学段落来测试这些概念， 现象。在真实的生活中，我们接收到一个恒定的、连续的信息流，我们将其分割成事件 有着明确的界限。我们有充分的理由相信，恢复一个事件的一部分可能会导致红火蚁 对于同一事件的其他部分，但事件边界可能会限制检索实践的好处。我 我建议使用一种创新的行为范式，结合计算建模来解决如何 提取影响对自然事件的记忆。我设计了一个任务，让参与者 描绘由事件边界分开的相关事件（即，去杂货店购物）的视频。要求1 是了解红火蚁和事件分割之间的关系。基于之前的研究表明， 记忆提取和事件分割之间的联系，提出提取实践将 便于保留同一事件的其他信息，但RIFA不会在事件之间发生。在目标2中， 利用计算机模拟，我们提出了一个可能的机制，红火蚁。基于先前的模型，例如 互补学习系统（CLS）模型，这种机制可能涉及错误驱动和赫布 学习，重点是内嗅皮层，海马和新皮层之间的联系。 在拟议的研究中，我们将扩展之前关于红火蚁的模型，以更好地描述这种关系 在基于事件的现实场景中，这些区域之间。我们希望通过检索实践， 海马体将在新皮层中更有效地驱动学习，这将最终导致更好的学习。 由于共享事件，在与检索实践信息相同的事件内的信息的准确性 上下文从一个简单的上下文变化开始，模仿事件的变化，我们将越来越多地使模型 通过训练模型来使用预测误差来标记事件边界而变得更复杂，那么最终， 输入自然主义视频以最好地模仿现实生活场景。这个项目建立在我以前的工作， 实现这些目标将使我在认知神经科学的最先进方法方面得到新的训练， 特别是计算机建模。更广泛地说，我们已经表明，记忆提取的做法可以是一个 在精神分裂症患者中使用具有临床价值的方法（Ragland et al，2020），这些研究将为我们提供 我们需要优化这种方法来增强精神病患者的日常情景记忆的信息 人口。