Explorative Data Analysis of Eye-tracking Datafor Cognitive Assessments
Bibliographic record
Abstract
This thesis evaluates a newly developed cognitive screening test using machine learning techniques. Existing cognitive tests can be challenging for individuals with multiple cognitive disabilities to complete; hence, this new test uses eye-tracking to facilitate performing the test. Analyzing the data collected from participants taking the test by building a pipeline, this thesis employs a Random Forest regressor to build a model and evaluates it using two existing cognitive tests: the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). The evaluation reports a reasonably good average prediction error value for both the MoCA and MMSE, which argues for an acceptable model. However, reports of a high variation among the prediction errors suggest that a further analysis of the test is advisable. Selection among features suggests that a feature set should include a variety of features, encompassing both spatial and temporal aspects, to enhance performance. While the eye-tracking test is currently under development, the thesis concludes that it may be of great use to health professionals in the future, aiding them in their work.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".