Mechanisms of Image Stabilization in Central Vision Loss: Smooth Pursuit
Bibliographic record
Abstract
SIGNIFICANCE: In addition to understanding the adaptive mechanisms of eccentric viewing during smooth pursuit, the ocular motor adaptations of patients with central vision loss give us a better understanding of the basic mechanisms of smooth pursuit in the healthy visual system. PURPOSE: For patients with age-related macular degeneration and controls with normal vision, we examined the closed-loop gain of horizontal and vertical smooth pursuit eye movements as a function of stimulus speed and direction. We hypothesized that pursuit gain functions would be affected by stimulus speed and the location of the preferred retinal locus (PRL) in relation to the scotoma as determined by a fixation stability task using a microperimeter. Specifically, that a PRL on the left of the scotoma in the visual field would decrease the rightward gain relative to the leftward gain and a PRL below the scotoma in the visual field would decrease the upward gain. METHODS: Ten patients and 15 controls were tested in a step-ramp procedure with direction (left/right for horizontal motion; up/down for vertical motion), speed (5, 10, 15, 20, and 30 deg/s), and five replication conditions randomized and blocked by orientation (horizontal vs. vertical). RESULTS: Horizontal pursuit had a higher gain than vertical pursuit. The two eyes of the patients moved conjugately with similar smooth pursuit gains. For horizontal pursuit, all patients, regardless of PRL location, showed significantly better pursuit of leftward motion. For vertical pursuit, downward pursuit had a higher gain than upward pursuit for most patients. CONCLUSIONS: PRL location was not predictive of the directional preponderance of pursuit performance. These results imply that patients may not use the PRL that was initially found during a static fixation task; they may adapt to the task by using a PRL that appears more suitable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".