VALIDATION OF A 5-MINUTE WEB CAMERA EYE-TRACKING COGNITIVE ASSESSMENT
Bibliographic record
Abstract
Alzheimer’s disease is a form of dementia impacting memory and cognitive function in 131 million individuals worldwide. Early cognitive decline detection allows for earlier intervention, but valid and user-friendly assessment options are lacking. The purpose of this investigation was to validate a 5-minute web camera eye-tracking assessment for cognitive function. This prospective study included 49 adults (n=32 females, n=17 males; age=52.7 ± 27.3) who were divided into two age cohorts: older (ages 65+ years, n=25) and young-middle aged adults (ages 18–46 years, n=24). Of the older cohort, four had mild cognitive impairment (MCI) (Montreal Cognitive Assessment [MoCA] <26). Testing included the MoCA, NIH Toolbox cognitive assessments (Flanker Inhibitory Control and Attention, Dimensional Change Card Sort, Pattern Comparison Processing Speed [PCPS], and Picture Sequence Memory tests), Digit Symbol (DS), dual-task (habitual and fast), and 5-minute eye-tracking assessments. A Pearson’s Correlation determined relationships between the NIH Toolbox cognitive assessments and the eye-tracking test and a one-way ANOVA determined differences between cognitively intact older adults and individuals with MCI. Significant correlations (p<.05) were found for the PCPS (r=.32), DS (r=.48), and dual-task (habitual: r=.52 and fast: r=.41). The eye-tracking assessment was able to discriminate between cognitively intact adults and individuals with MCI. These results suggest the 5-minute eye-tracking assessment is a valid method for assessing cognition among adults with and without cognitive impairment. The 5-minute eye-tracking test displayed convergent validity with currently used measures of cognition, indicating it may be a widely scalable option used in place of the longer traditional testing methods.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".