Age-related changes in memory alter decision-making

与年龄相关的记忆变化会改变决策

• 批准号: 10602397
• 负责人: Sharon M Noh
• 金额: $ 7.2万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-20 至 2025-01-19
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10602397
• 关键词: Address; Affect; Age; Aging; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Area; Behavior; Choice Behavior; Clinical; Cognitive aging; Compensation; Complex; Computer Models; Decision Making; Development; Early Diagnosis; Early Intervention; Early treatment; Educational Intervention; Elderly; Episodic memory; Face; Failure; Functional Magnetic Resonance Imaging; Functional disorder; Hippocampus; Impaired cognition; Impairment; Individual; Individual Differences; Intervention; Learning; Longevity; Love; Measures; Memory; Memory Loss; Memory impairment; Modeling; Participant; Pattern; Play; Population; Process; Psychological reinforcement; Recording of previous events; Research; Resolution; Rewards; Role; Running; Sampling; Shapes; Stroke; System; Time; Variant; Work; age related; aging population; behavior measurement; cognitive ability; computational basis; expectation; experience; experimental study; healthy aging; human old age (65+); improved; insight; memory encoding; memory process; nervous system disorder; neural; neural circuit; neural patterning; neuroimaging; neuromechanism; recruit; support network; theories; young adult

PROJECT SUMMARY. While several studies have identified deficits in decision-making abilities in old age, many of these studies focus specifically on reinforcement learning and its associated reward networks. Reinforcement learning theory states that our choices are shaped by expectations based on a running average of our experiences, but this may provide an overly simplistic view. Recent work has shown that decision-making at time of choice depends not only on the average of past experiences, but also on memories of specific individual experiences and their associated contexts (e.g., the time and place in which they were experienced). This context-guided memory sampling (CGMS) model of decision-making, developed by the sponsor, asserts that choice behavior is influenced by memory content that is retrieved when making a decision, in addition to traditional reinforcement learning (e.g., the influence of recent rewards). We propose that age-related deficits in memory, therefore, may play a significant role in influencing choice behavior in older adults. Given the well-characterized age-related deficits in memory research, particularly in the areas of associative and episodic memory, this project addresses to what extent memory processes influence decision-making, and whether age-related deficits in decision-making are due to the well-documented age-related impairments observed in memory. Across 3 experiments, we utilize a neuro-computational approach to examine how memory processes and reinforcement learning contribute to decision-making across the lifespan. In doing so, we aim to precisely identify the mechanisms associated with decision failures in aging. In Aim 1, we manipulate memory demands and learning content to assess how individual differences in memory ability influence decision-making strategies. In Aim 2, we will use computational modeling to identify how age-related differences in episodic memory and reinforcement learning influence subsequent choice behaviors in older vs. younger adult populations. In Aim 3, we will use high-resolution functional magnetic resonance imaging to determine the specific neural computations and networks that support and explain choice behavior in older adults on a trial- by-trial basis. Findings from these proposed studies can help develop training interventions that promote healthy aging and target learning interventions to support early detection and treatment of age-related cognitive dysfunctions in older adult populations. This research is relevant to the older adult population at large, but also to patients with Alzheimer’s disease and related dementias who experience profound deficits in learning, memory, and decision-making.

项目摘要。 尽管几项研究已经确定了老年决策能力的定义，但其中许多研究 专门关注强化学习及其相关的奖励网络。强化学习理论 指出我们的选择是根据我们的经验平均值的期望来塑造的，但这 可能会提供过于简单的观点。最近的工作表明，选择时决策取决于 不仅是过去经验的平均值，而且还要记忆特定的个人经历及其 相关上下文（例如，它们经历的时间和地点）。此上下文引导的内存 由赞助商开发的决策模型（CGM）模型，断言选择行为是 除了传统的强化外，还受到做出决定时检测到的记忆内容的影响 学习（例如，最近的奖励的影响）。因此，我们建议记忆中与年龄相关的缺陷 在老年人的影响力选择行为中起着重要作用。考虑到与年龄相关的良好 记忆研究中的缺陷，特别是在关联和情节记忆领域，该项目 地址在多大程度上影响决策，以及与年龄相关的缺陷是否在 决策是由于记忆中观察到的与年龄相关的障碍所致。跨3 实验，我们利用一种神经计算方法来检查记忆过程和 强化学习有助于整个生命周期的决策。在这样做的过程中，我们的目标是精确 确定与衰老决策失败相关的机制。在AIM 1中，我们操纵记忆需求 学习内容以评估记忆能力的个体差异如何影响决策 策略。在AIM 2中，我们将使用计算建模来确定情节与年龄相关的差异 记忆和加强学习影响年龄较大与年轻人的随后选择行为 人群。在AIM 3中，我们将使用高分辨率的功能磁共振成像来确定 在试验中支持和解释老年人的选择行为的特定神经计算和网络 - 基础。这些拟议的研究的发现可以帮助制定培训干预措施来促进 健康的衰老和目标学习干预措施，以支持与年龄相关的早期检测和治疗 老年人人群的认知功能障碍。这项研究与老年人群有关 很大，也适用于患有阿尔茨海默氏病和相关痴呆症的患者，他们经历了深刻的定义 学习，记忆和决策。