Analysis of Spatiotemporal Features in Virtual Navigation Game Across Different Age Groups: Quantitative research (Preprint)
Bibliographic record
Abstract
BACKGROUND Virtual reality (VR) navigation games are increasingly employed to investigate human navigational behavior and cognitive performance under controlled spatial conditions. However, there is a lack of empirical research on how spatial configurations and individual cognitive abilities influence wayfinding performance, particularly across different age groups. OBJECTIVE This study aims to investigate the critical associations between environmental factors (external factors) and cognitive abilities (internal factors) in shaping navigational behavior performance in wayfinding games across different age groups. METHODS We designed a virtual navigation game and recruited two groups, younger adults (n = 18) and older adults (n = 21), to complete identical goal-directed wayfinding tasks. Prior to the formal experiment, cognitive abilities were assessed through questionnaires, and pre-training was conducted to ensure participants were familiar with navigating within the virtual environment (VE). In this study, navigation efficiency was used as the primary spatiotemporal feature, and Spearman’s rank correlation was employed to examine its relationship with cognitive abilities. To quantify the spatial structure of the VE, we applied Space Syntax Analysis (SSA), including both Axial Map Analysis (AMA) and Visibility Graph Analysis (VGA), to investigate the associations between navigation efficiency and spatial configurations. Additionally, the Mann-Whitney U test was used to compare navigation behavior differences between the age groups from a spatiotemporal perspective. RESULTS Our results revealed that navigation behavior performance, particularly navigation efficiency, was significantly influenced by cognitive abilities and strongly correlated with several cognitive tests: the Montreal Cognitive Assessment (r = 0.495, p < 0.05), Part A of the Trail Making Test (r = -0.761, p < 0.01), and the Mental Rotation Test (r = 0.848, p < 0.01). Additionally, aging leaded to a significant decline in navigation efficiency (Z = -4.285, p < 0.001). Affected by the surrounding environmental factors of the axial path, navigation efficiency was closely related to connectivity (r = 0.675, p < 0.05), integration (r = 0.749, p < 0.01), and depth (r = -0.646, p < 0.05) derived from AMA. The integration experienced, based on participants' movement trajectories and VGA, showed significant age differences (Z = -2.097, p < 0.05) and was correlated with navigation efficiency (r = -0.366, p < 0.05). CONCLUSIONS The spatiotemporal features of virtual navigation games are shaped by both cognitive abilities and environmental structure. There is ample evidence confirming that navigation games have great potential to serve as an effective cognitive assessment tool. Our findings also reveal that younger participants or those with better cognitive abilities tend to prioritize traversing lower-integrated areas to enhance navigation efficiency, leading to more optimal navigation strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".