Age-related changes in memory alter decision-making

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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.