Age-related changes in memory alter decision-making

项目摘要。 虽然一些研究已经确定了老年人决策能力的缺陷，但其中许多研究 专注于强化学习及其相关的奖励网络。强化学习理论 我们的选择是由基于我们经验的平均值的期望塑造的，但这 可能会提供一个过于简单化的观点。最近的研究表明，在选择时的决策取决于 不仅是过去经验的平均值，而且是对特定个人经历的记忆及其 相关上下文（例如，他们所经历的时间和地点）。这种情境引导的记忆 由发起人开发的决策抽样（CGMS）模型断言，选择行为是 除了传统的强化外，还受到决策时检索的记忆内容的影响 学习（例如，近期奖励的影响）。因此，我们认为，与年龄相关的记忆缺陷可能 在影响老年人的选择行为方面发挥着重要作用。考虑到年龄相关的特征， 在记忆研究的缺陷，特别是在联想和情节记忆领域，该项目 解决记忆过程在多大程度上影响决策，以及是否与年龄有关的缺陷， 决策能力下降是由于在记忆中观察到的有据可查的与年龄有关的损害。跨3 在实验中，我们利用神经计算方法来研究记忆如何处理， 强化学习有助于整个生命周期的决策。为此，我们的目标是， 识别与老龄化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 战略布局在目标2中，我们将使用计算模型来确定年龄相关的情景差异是如何产生的。 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 特定的神经计算和网络，支持和解释老年人的选择行为， 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， 健康的老龄化和有针对性的学习干预，以支持早期发现和治疗与年龄有关的 老年人的认知功能障碍。这项研究与年龄较大的成年人有关， 大，但也与阿尔茨海默氏病和相关痴呆症的患者谁经历深刻的缺陷， 学习记忆和决策

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.