From Laboratory to Real Life: Psychometric Properties of a VR-Based Ecological Memory Assessment Tool
Bibliographic record
Abstract
The early identification of subtle cognitive decline is crucial for preventing neurodegenerative disorders, yet traditional neuropsychological assessments often lack ecological validity. We developed the Virtual Memory Ecological Battery (V-MEB), a novel VR-based assessment tool comprising seven interconnected subtests that evaluate multiple memory domains through immersive, everyday-life scenarios. This study examined the psychometric properties of the V-MEB in 82 participants including cognitively healthy individuals, those with borderline functioning, and mild cognitive impairment patients. Participants completed both traditional neuropsychological measures and the V-MEB using a Meta Quest 3 headset. Exploratory factor analysis of 17 continuous V-MEB performance measures, guided by parallel analysis, revealed a robust two-factor structure. Factor 1 (Spatial-Temporal Efficiency) demonstrated excellent convergent validity with established measures and good internal consistency$(\boldsymbol\alpha=\boldsymbol0.713)$. Factor 2 (Procedural Learning Strategies) showed selective correlations only with executive functioning measures, indicating unique assessment capabilities not captured by traditional tests. Strong correlations between V-MEB factors and prospective memory performance supported ecological validity. The V-MEB successfully operationalizes distinct cognitive domains through ecologically valid VR scenarios, offering both convergent assessment of established abilities and novel evaluation of procedural learning strategies. These findings support the V-MEB as a promising complement to traditional neuropsychological assessment with enhanced ecological validity.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".