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。 虽然一些研究发现老年决策能力存在缺陷，但其中许多研究 特别关注强化学习及其相关的奖励网络。强化学习理论 指出我们的选择是由基于我们经验的运行平均值的期望决定的，但这 可能会提供过于简单化的观点。最近的研究表明，选择时的决策取决于 不仅基于过去经历的平均值，而且还基于对特定个人经历及其记忆的记忆 相关的背景（例如，他们经历的时间和地点）。这种情境引导记忆 申办者开发的决策抽样（CGMS）模型断言选择行为是 除了传统的强化之外，还受到做出决定时检索的记忆内容的影响 学习（例如，最近奖励的影响）。因此，我们认为与年龄相关的记忆缺陷可能会 对老年人的选择行为产生重要影响。鉴于与年龄相关的特征 记忆研究的缺陷，特别是在联想记忆和情景记忆领域，该项目 解决记忆过程在多大程度上影响决策，以及年龄相关的缺陷是否 决策是由于在记忆中观察到的有据可查的与年龄相关的损伤而做出的。跨越3 实验中，我们利用神经计算方法来检查记忆如何处理和 强化学习有助于整个生命周期的决策。在此过程中，我们的目标正是 确定与衰老决策失败相关的机制。在目标 1 中，我们操纵内存需求 和学习内容来评估记忆能力的个体差异如何影响决策 策略。在目标 2 中，我们将使用计算模型来确定情景中与年龄相关的差异如何 记忆和强化学习影响老年人与年轻人随后的选择行为 人口。在目标 3 中，我们将使用高分辨率功能磁共振成像来确定 支持和解释老年人在试验中的选择行为的特定神经计算和网络 以试验为基础。这些拟议研究的结果可以帮助制定培训干预措施，以促进 健康老龄化和目标学习干预措施，以支持与年龄相关的早期发现和治疗 老年人群的认知功能障碍。这项研究与老年人口相关 不仅如此，对于患有阿尔茨海默病和相关痴呆症的患者来说，他们也经历了严重的认知缺陷。 学习、记忆和决策。