# A neurocomputational model of age-related differences in navigation.

> **NIH NIH R21** · UNIVERSITY OF ARIZONA · 2024 · $230,250

## Abstract

Project Abstract: A neurocomputational model of age-related differences in
navigation
For many individuals, navigation skills decline with increasing age, particularly for novel
environments with which the individual is not already highly familiar. Impairments in
navigation are particularly evident for those with mild cognitive impairment, a significant
risk factor for Alzheimer’s disease and Alzheimer’s related dementia (ADRD). Yet, our
understanding of age-related differences in navigation remains underdeveloped. The
dominant explanation for age-related differences in navigation relates to impairments in
the cognitive map, yet the explanatory capabilities of this model are limited, particularly
in explaining the heterogeneity of age-related differences in navigation. Here, we seek
to develop a novel computational model that employs an approximate Bayesian
framework to better account for age-related differences in navigation. We focus in
particular on testing hypotheses related to greater reliance on multimodal cue integration
(Aim 1) and strategy rigidity (Aim 2) in older adults. These ideas will be tested using
highly immersive virtual reality in which participants don a head-mounted display and
free ambulate hallways. The experiment in Aim 1 will provide critical tests of our
computational model by employing conditions involving both matching and mismatching
visual and idiothetic (body-based) cues. This will allow us to test Aim 1 and whether
older adults rely on multimodal integration to a greater extent than younger adults. The
experiment in Aim 2 will test whether older adults rely on previous trials to a greater
extent than younger adults, particularly for matching and mismatching trials. This will
allow us to test the extent to which older adults might show strategy rigidity by over-
relying on past trials. Aim 1&2 will also allow us to test ideas related to individual
differences in navigation strategies, which can be assessed based on the variability in
subject responses within the different conditions tested. Finally, we will test a subset of
individuals with mild cognitive impairment (MCI) to provide initial and exploratory testing
of their navigational deficits in our experiments. The outcomes of this project have the
potential to provide a new understanding of age-related differences in navigation by
developing a sophisticated and novel computational model that we test in detail with
experiments in immersive virtual reality.

## Key facts

- **NIH application ID:** 10840971
- **Project number:** 5R21AG081558-02
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** ARNE D EKSTROM
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $230,250
- **Award type:** 5
- **Project period:** 2023-05-15 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840971

## Citation

> US National Institutes of Health, RePORTER application 10840971, A neurocomputational model of age-related differences in navigation. (5R21AG081558-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10840971. Licensed CC0.

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