Cognitive impairment assessment using eye-tracking: multilevel saccade paradigms with differential analysis and attention-based neural networks
Bibliographic record
Abstract
Abstract Objective . The accurate assessment of cognitive impairment plays a vital role in more targeted treatments for Dementia. Eye movement analysis is a non-invasive and objective method that offers fine-grained insight into cognitive functioning, complementing conventional screening tools. However, single-task eye-tracking paradigms and simplistic analysis methods limit the potential for comprehensive and fine-grained assessment of cognitive impairment. To address this limitation, we propose a multilevel saccade paradigm combined with differential analysis and an attention-based neural network to enhance eye-tracking—based cognitive impairment assessment. Approach . Firstly, a set of saccade-based paradigms with graded difficulty levels is developed, including prosaccade, antisaccade, and random pro-/antisaccade paradigms. Each paradigm incorporates eye movement assessments in both horizontal and vertical directions. Secondly, we recruit 90 subjects for eye-tracking assessments to build a large-scale dataset. The subjects consisted of 36 healthy young controls, 15 healthy elderly controls, 23 individuals with mild cognitive impairment, and 16 individuals with dementia. Each subject completed the Montreal Cognitive Assessment (MoCA). Third, the Mann—Whitney U test is employed to identify eye movement features that show significant differences across the four groups. Correlation analysis with MoCA scores further validated the effectiveness of these eye movement features in distinguishing cognitive impairment. Finally, XGBoost is employed to perform classification and to validate the effectiveness of the eye movement feature selection scheme derived from the difficulty-graded saccade paradigms. An attention-based neural network is also integrated to enhance classification accuracy and improve feature selection by identifying the most informative eye movement features. Main results . The model achieved an area under the receiver operating characteristic curve of 0.94, a classification accuracy of 0.80, and a Matthews correlation coefficient of 0.73. Among all features extracted from the different saccade paradigms, the time to first correct AOI and saccade latency parameters from the random pro-antisaccade paradigm demonstrate the highest contribution to classification performance. Significance . By integrating graded saccade paradigms with statistical analysis and attention neural network, this study enhances the granularity and accuracy of eye-tracking—based cognitive assessment, offering a scalable and non-invasive tool for early detection and monitoring of cognitive decline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".