Saccadic omission revisited: What saccade-induced smear looks like
Bibliographic record
Abstract
During active visual exploration, saccadic eye movements rapidly shift the visual image across the human retina. Although these high-speed shifts occur at a high rate and introduce considerable amounts of motion smear during natural vision, our perceptual experience is oblivious to it. This saccadic omission, however, does not entail that saccadeinduced motion smear cannot be perceived in principle. Using tachistoscopic displays of natural scenes, we rendered saccade-induced smear highly conspicuous. By systematically manipulating peri-saccadic display durations we studied the dynamics of smear in a time-resolved manner, assessing identification performance of smeared scenes, as well as perceived smear amount and direction. Both measures showed distinctive, U-shaped time courses throughout the saccade, indicating that generation and reduction of perceived smear occurred during saccades. Moreover, low spatial frequencies and orientations parallel to the direction of the ongoing saccade were identified as the predominant visual features encoded in motion smear. We explain these findings using computational models that assume no more than saccadic velocity and human contrast sensitivity profiles, and present a motion-filter model capable of predicting observers’ perceived amount of smear based on their eyes’ trajectories, suggesting a direct link between perceptual and saccade dynamics. Replays of the visual consequences of saccades during fixation led to virtually identical results as actively making saccades, whereas the additional simulation of perisaccadic contrast suppression heavily reduced this similarity, providing strong evidence that no extra-retinal process was needed to explain our results. Saccadic omission of motion smear may be conceptualized as a parsimonious visual mechanism that emerges naturally from the interplay of retinal consequences of saccades and early visual processing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".