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. 2026 Jan 13:1-25.
doi: 10.1017/psy.2025.10069. Online ahead of print.

Navigating Cognitive Maps: Statistical Analysis of 3D Path Data in Minecraft

Jizhi Zhang  1 , Alessandra Shuster  1 , Allison B Morehouse  1 , Sara Mednick  1 , Zhaoxia Yu  1 , Weining Shen  1 , Katharine C Simon  1
Affiliations

Navigating Cognitive Maps: Statistical Analysis of 3D Path Data in Minecraft

Jizhi Zhang et al. Psychometrika. .

Abstract

Understanding spatial navigation and memory formation is critical to exploring how humans learn and adapt in complex environments. To investigate these processes, we conducted an experiment using the Minecraft Memory and Navigation Task, collecting detailed three-dimensional (3D) path data in a virtual open-world setting. Statistically, we developed a novel methodology to convert complex high-dimensional 3D movement data into functional representations, enabling standardized comparisons and analyses across participants and environments. We applied techniques such as functional clustering and regression to identify navigation patterns and their relationships with cognitive map development and memory retention. Our analysis uncovered two significant insights: first, participants who adopted moderately exploratory behaviors during training demonstrated superior retention of object locations; second, inefficient navigation strategies were strongly linked to poorer spatial memory and navigation performance. These findings highlight the effectiveness of our methodology in advancing the study of navigation behaviors and cognitive processes in dynamic 3D environments.

Keywords: Dijkstra’s algorithm; functional data analysis; functional regression; spatial memory; spatial navigation.

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Conflict of interest statement

The authors declare none.

Figures

Figure 1
Figure 1
Screenshots of four game environments.
Figure 2
Figure 2
An example of comparisons between 2D and 3D paths.
Figure 3
Figure 3
Example of paths from participant no. 2025 during training session 1.
Figure 4
Figure 4
Functional clustering analysis results.
Figure 5
Figure 5
Comparison of sample curves to their optimal paths for each of the four clusters. Note: Each subfigure represents a cluster and it contains four different environments.
Figure 6
Figure 6
Analysis of optimal path costs across different environments.
Figure 7
Figure 7
Average cost difference curves for low-cost segments from 182 study participants.
Figure 8
Figure 8
Functional coefficient curves and associated 95% confidence bands for formula image , formula image , and formula image .
Figure A1
Figure A1
Weighted graph from source A.
Figure C1
Figure C1
Cluster-averaged trajectories and associated confidence bands using FPCA-based clustering.
Figure D1
Figure D1
Estimated functional coefficient curves for the merged cluster 3 & 4 average cost difference curve as covariate, along with 95% bootstrap confidence bands, for the three test outcomes formula image , formula image , and formula image .
Figure E1
Figure E1
Silhouette score plot for clustering results in Section 3.4.
See this image and copyright information in PMC

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