Welcome to the repository for the paper "Distinct aging-related profiles of allocentric knowledge recall following navigation in an immersive, naturalistic, city-like environment" (also nicknamed the NPRL "NavAging Paper").
This project examines aging-related differences in spatial navigation in an immersive, naturalistic virtual environment (NavCity) and associated allocentric knowledge recall.
This repository contains all analysis code, figures, and statistical tests associated with this study.
📌 Note on Code Versions
This repository is a frozen snapshot of the code used to produce the results in the published paper, preserved for reproducibility. For the latest version of the analysis code (with bug fixes, improvements, and extensions), see the active development repository for NavCity data analysis.
All data for this paper can be found on the associated NavAging OSF Project.
Below is an explanation of the folder structure in this repository. Feel free to reach out to the Neural Plasticity Research Lab via our website or contact Yasmine Bassil at [email protected] with any questions.
Bassil, Y., Kanukolanu, A., Funderburg, E., Cui, E., Brown, T., & Borich, M. R. (2025). Distinct aging-related profiles of allocentric knowledge recall following navigation in an immersive, naturalistic, city-like environment. PsyArXiv. https://osf.io/qmwyk.
Authors: Yasmine Bassil, Anisha Kanukolanu, Emma Funderburg, Emily Cui, Thackery Brown, Michael R. Borich
Affiliation: Neural Plasticity Research Lab, Emory University
Contact: Dr. Michael Borich, PhD, DPT, PT ([email protected])
Lab Website: npresearchlab.com
This repository provides complete reproducibility materials for our study examining aging-related differences in spatial navigation and allocentric knowledge recall using an immersive, naturalistic virtual environment (NavCity). The study compares younger adults (YAs) and older adults (OAs) across multiple cognitive and navigational assessments.
Key Features:
- Complete analysis pipeline from raw data to final figures
- Statistical analysis scripts
- Publication-ready figures
NavAging_Paper/
│
├── data_analysis/ # Data processing and analysis scripts
│ └── 0_runall.ipynb # Master script to process all raw data
│ └── 1_calculate_outcomes.ipynb # Calculates outcome measures from raw data
│ └── 2_merge_data.ipynb # Collects outcome measures per block per participant in one dataframe
│ └── 3_average_data.ipynb # Averages outcome measures over blocks per participant
│ └── 4_target_data.ipynb # Creates dataframes for overall paths per block across participants
│ └── 5_graph_data.ipynb # Creates overhead path map figures per block across participants
│ └── 6_post_analyses.ipynb # Cleans up data based on documented errors during data collection
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├── figure_creation/ # Scripts to generate manuscript figures
│
├── final_figures/ # Publication-ready figures (output)
│
├── stat_tests/ # Statistical analysis scripts
│
├── .gitignore
├── LICENSE
└── README.md
All experimental data can be found in the Open Science Framework (OSF) project associated with this study: NavAging OSF Project
Contains Jupyter notebooks and Python scripts for data processing and analysis.
0_runall.ipynb: Master orchestration script- Runs all analysis scripts (labeled 1 through 6) in sequence
- Processes raw NavCity data files
- Generates block-specific and session-averaged metrics
- Outputs cleaned datasets for statistical analysis
- Line 15: Set your local data directory for YA data
- Line 16: Set your local data directory for OA data
- Line 19: Set your local directory to analysis codes (scripts 0 through 6)
Outputs from analysis scripts will be located in the parent directory of the YA data and OA data, respectively.
To Run the Complete Pipeline:
- Clone this repository
- Install required packages (see Requirements)
- Update file paths in
0_runall.ipynb - Run all cells in
0_runall.ipynb
Statistical analysis scripts for hypothesis testing and generating results reported in the manuscript.
- Includes mixed-effects models
- Between-group comparisons (YA vs OA)
- Correlation analyses
- Effect size calculations
Scripts to generate all manuscript figures from processed data.
- Figure 2: Included plots from
1_average_plots.Rmdand2_by_block_plots.Rmd(for NavCity primary measures) - Figure 3: Included plots from
1_average_plots.Rmdand2_by_block_plots.Rmd(for NavCity secondary measures) - Figure 4: Included plots from
4_corr_plots.Rmdand5_nara_plots.Rmd - Figure 5: Included plots from
6_cohorts_average_plots.Rmdand2_cohorts_by_block_plots.Rmd(for NavCity primary measures) - Figure 6: Included plots from
6_cohorts_average_plots.Rmdand2_cohorts_by_block_plots.Rmd(for NavCity secondary measures)
This directory contains publication-ready figures in high-resolution formats (PNG, PDF, SVG).
All figures follow journal specifications:
- 300+ DPI resolution
- Colorblind-friendly palettes
- Clear axis labels and legends
Python 3.8+ with the following packages:
numpy>=1.20.0
pandas>=1.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
scipy>=1.7.0
statsmodels>=0.12.0
jupyter>=1.0.0- Minimum 8GB RAM recommended
- ~500MB disk space for repository
- Standard computing hardware sufficient
-
Clone the repository:
git clone https://github.com/npresearchlab/NavAging_Paper.git cd NavAging_Paper -
Install dependencies:
pip install numpy pandas matplotlib seaborn scipy statsmodels jupyter
-
Run the analysis pipeline:
- Open
data_analysis/0_runall.ipynbin preferred IDE - Update file paths in the configuration section
- Run all cells to reproduce analyses
- Open
-
Generate figures:
- Navigate to
figure_creation/ - Run figure generation scripts
- Outputs will be saved to
final_figures/
- Navigate to
Will be posted when citation is available.
Data: CC BY 4.0 - Data are freely available with attribution
Code: MIT License - Code is freely available for reuse and modification
We welcome questions, bug reports, and suggestions for improvements. Please:
- Check existing Issues
- Open a new issue with detailed description
- For data questions, contact Dr. Michael Borich at mborich [at] emory.edu
This research was supported by [funding sources]. We thank all study participants and the research team members who contributed to data collection and analysis.
- Lab Website: npresearchlab.com
- Preprint: NavAging Preprint
- OSF Project: OSF Data Repository
Last Updated: December 2025
Repository Maintainer: Yasmine Bassil, Neuroscience PhD Candidate, Neural Plasticity Research Lab, Emory University