Saccadic adaptation in the presence of artificial central scotomas
Bibliographic record
Abstract
Abstract Saccadic adaptation can occur over a short period of time through a constant adjustment of the saccade target during the saccade, resulting in saccadic re-referencing which directs the saccade to a location different from the target that elicited the saccade. Saccade re-referencing could be used to help patients with age-related macular degeneration (AMD) to optimally use their residual visual function. However, it remains unknown whether saccade adaptation can take place in the presence of central scotomas (i.e., without central vision). We tested participants in two experiments in a conventional double-step paradigm with a central gaze-contingent artificial scotoma. Experiment 1 ( N = 12) comprised a backward adaptation paradigm with a visible and an invisible 3° diameter scotomas. Experiment 2 ( N = 13) comprised a forward adaptation paradigm with invisible 2° and 4° diameter scotomas. In Experiment 1, we observed significant adaptation in both the visible and invisible scotoma conditions comparable to the control condition with no scotoma. This was the case even when the saccade landed such that the target was occluded by the scotoma. We observed that adaptation occurred based on peripheral viewing of the stepped target during the deceleration period. In Experiment 2, we found that both scotoma conditions showed adaptation again comparable to the control condition with no scotoma. We conclude that saccadic adaptation can occur with central scotomas, showing that it does not require central vision and is driven primarily by peripheral retinal error.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".