A cognitive function test utilizing eye tracking technology in virtual reality
Bibliographic record
Abstract
AIM: There is a need for a cognitive function test that is less burdensome to perform cognitive function tests used to date and can detect mild changes in the cognitive function and mild cognitive impairment (MCI). We developed a cognitive function examination using a virtual reality device (VR-E). The purpose of this study was to verify its usability. METHODS: Seventy-seven participants (29 males and 48 females, average age 75.1 years old) were classified according to their Clinical Dementia Rating (CDR). To estimate the validity of VR-E in measuring cognitive function, we used the Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment-Japanese version (MoCA-J) scores as benchmarks. The MMSE was performed for all subjects, while the MoCA-J was performed for subjects with an MMSE score ≥20. RESULTS: VR-E scores were highest in the CDR 0 group (0.77±0.15, mean±SD), decreasing for subsequent groups (CDR 0.5: 0.65±0.19, CDR 1-3: 0.22±0.21). The receiver operating characteristic analysis showed that all three methods were able to distinguish CDR groups. For CDR 0 vs. 0.5, the areas under the curve for MMSE/MoCA-J/VR-E were 0.85/0.80/0.70, respectively, and those for CDR 0.5 vs. 1-3 were 0.89/0.92/0.90, respectively. The time required to complete VR-E was approximately 5 minutes. Of the 77 subjects, 12 were difficult to assess using the VR-E due to poor understanding or eye diseases or Meniere's syndrome. CONCLUSIONS: The present findings suggested that the VR-E can be used as a cognitive function test that correlates with existing standard assessments for dementia and MCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".