Oculomotor indicators of cognitive performance are modulated by neurodegeneration
Bibliographic record
Abstract
In this study, the extent to which eye movements can be used to estimate cognitive ability in neurologically intact individuals was evaluated in the absence of clear underlying neurodegenerative processes. In contrast to previous studies of Parkinson's Disease (PD) and multiple sclerosis that demonstrated a strong link between oculomotor parameters and clinical measures of cognition, this relationship is unaffected by disease in healthy participants, enabling a more direct assessment of the connection between eye movements and cognition. Accordingly, a modest portion (≤28%) of the observed variance in cognitive test scores could be explained by oculomotor parameters in 204 participants aged 18-79 with no differences between males and females observed. The relationship between oculomotor parameters and cognitive measures was further compared between neurologically intact individuals and a separate sample of 65 individuals with PD. Oculomotor parameters showed stronger correlations with cognitive measures in PD patients, likely contributing to the greater explanatory power of oculomotor-based models in this population. Finally, given that many oculomotor parameters are affected by age, the ability to estimate an individual's age without confounding neurodegeneration was assessed. As 33% of the variance in participants' age could be explained by oculomotor parameters, age may be estimated from oculomotor parameters, providing insight into the aging brain. Collectively, these findings highlight the connection between oculomotor function and clinical measures of cognition in the absence of neurodegeneration and indicate that these relationships are likely mediated by the functional integrity of brain networks involved in both motor control and cognitive processing.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".