Detection of mild cognitive impairment using a virtual reality-based stroop task: a cross-sectional study of embodied behavioral markers
Bibliographic record
Abstract
BACKGROUND: Executive dysfunction is commonly impaired in individuals with mild cognitive impairment (MCI). Traditional tools like the Stroop test are widely used to evaluate this domain but lack ecological validity. Virtual reality (VR)-based cognitive assessments, grounded in embodied cognition, may offer a more immersive and sensitive approach to detecting subtle executive dysfunction. METHODS: This study developed and validated a novel VR-based Stroop Test (VRST) that simulates a real-life clothing-sorting task involving incongruent word-color stimuli. A total of 413 older adults (224 healthy controls and 189 with MCI) completed the VRST using a hand-held controller. Behavioral metrics including task completion time, 3D(three-dimensional) trajectory length, and hesitation latency were collected. Participants also underwent traditional assessments: the Korean version of the Montreal Cognitive Assessment (MoCA-K), the paper-based Stroop test, and the Corsi Block Test (CBT). Receiver operating characteristic curves and Spearman correlations were used to analyze discriminant power and construct validity. RESULTS: All VR-derived behavioral markers effectively differentiated older adults with MCI from HCs, with 3D trajectory length showing the highest area under the curve (0.981), followed by hesitation latency (0.967). These surpassed the MoCA-K (0.962). Significant correlations were observed between VRST outcomes and global cognition (MoCA-K), inhibition (Stroop), and working memory (CBT), supporting convergent validity. Importantly, baseline motor abilities did not significantly differ between groups, suggesting that executive function could contributed to performance differences. CONCLUSIONS: The VRST provides a valid, reliable, and scalable means of detecting MCI-related executive dysfunction through embodied cognitive-motor interaction. Its ability to capture subtle behavioral changes in a realistic context suggests strong potential for use in both clinical and community-based cognitive screening settings. TRIAL REGISTRATION: This study was retrospectively registered in the Thai Clinical Trial Registration with identifier TCTR 20250625011.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".