Multi-input GNL-HybELS: An automated tool for the analysis of oculomotor dynamics during visual-vestibular interactions
Bibliographic record
Abstract
The eyes play a major role in our everyday activities. Eye movements are controlled by the oculomotor system, which enables us to stay focused on visual targets, switch visual attention, and compensate for external perturbations. This system's response to isolated visual or vestibular stimuli has been studied for decades, but what seems to be more critical is to know how it would respond to a combination of these stimuli, because in most natural situations, multiple stimuli are present. It is now believed that sensory fusion does not affect the dynamics of oculomotor modalities, despite studies suggesting otherwise. However, these interactions have not been studied in mathematical detail due to the lack of proper analysis tools and poor stimulus conditions. Here we propose an automated tool to analyze oculomotor responses without a-priori classification of nystagmus segments, where visual and vestibular stimuli are uncorrelated. Our method simultaneously classifies and identifies the responses of a multi-input multi-mode system. We validated our method on simulations, estimating sensory delays, semicircular canal time constant, and dynamics in both slow and fast phases of the response. Using this method, we can now investigate the effect of sensory fusion on the dynamics of oculomotor subsystems. With the analysis power of our new method, clinical protocols can now be improved to test these subsystems more efficiently and objectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".