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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.005 | 0.005 |
| 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".