# Age-related changes in memory alter decision-making

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $72,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10602397
- **Project number:** 5F32AG072836-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Sharon M Noh
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $72,000
- **Award type:** 5
- **Project period:** 2022-01-20 → 2025-01-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10602397

## Citation

> US National Institutes of Health, RePORTER application 10602397, Age-related changes in memory alter decision-making (5F32AG072836-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10602397. Licensed CC0.

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