An internally switched model of ocular tracking with prediction
Bibliographic record
Abstract
Ocular tracking of targets in biological systems involves switching between two strategies: slow pursuit and fast corrective saccades producing pursuit nystagmus. Here, a symmetric (bilateral) controller is used as a model for the oculomotor control system (OCS) to drive two cameras on a robotic head. It relies, as in biology, on internal switching in shared premotor circuits to alternate automatically between the two types of movements comprising nystagmus. The symmetric structural concept is gaining acceptance as evidence points to sharing of both fast phase and slow phase control in brainstem structures previously thought to be solely involved in one mode alone. This bilateral OCS model is a parsimonious design that is at once biomimetic and analytically simple. We extend prior results by incorporating more biological clues from floccular projections to establish rudimentary prediction mechanisms for both slow and fast phases; prediction is achieved by using retinal slip, which contains target velocity information. This provides a more accurate replication of the difference between fast phase and slow phase dynamics, and considers neural activity profiles in the superior colliculus to refine the controller performance. The resulting controller eliminates the need for saccades in steady state for low frequency inputs, and each saccade now has better accuracy, despite visual delays.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".