A probabilistic model of transsaccadic integration
Bibliographic record
Abstract
We move our eyes to view different parts of our surroundings. But how do we integrate these views from one fixation to the next? Several studies have shown that we are, in some ways, surprisingly bad at piecing together a picture of the world; for example we are blind even to considerable changes in the visual display when these changes occur during saccades. Other studies, though, reveal that quite precise visual spatial information survives the eye movement. To explain this performance, we note that there are two methods the brain could use to combine views from different fixations. One method, called updating by oculomotor physiologists, uses motor or proprioceptive information about eye motion to account for the resulting shifts of the retinal image. The other method, called mosaicking by computer scientists who use it for example in radio telescopy, relies on visual information alone, using common features in separate snapshots to glue the snapshots together in a unified picture. Both updating and mosaicking are error-prone in different ways. Our model of transsaccadic integration uses both methods, combining them in a way that optimizes the expected alignment of successive images. This optimal combination, which depends on the statistical properties of the visual, proprioceptive and motor signals and of the outside world, explains some of the strengths and flaws in human transsaccadic integration.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".