Bibliographic record
Abstract
Transsaccadic perception describes our perceived visual stability despite images shifting across the retina due to saccadic eye movements. Visual stability is thought to be maintained across saccades due to spatial updating mechanisms that track object locations over the saccade. However, transsaccadic research has focused on static objects; few studies have examined how we track moving objects during a saccade. Niemeier, Crawford, and Tweed (2003) found that intrasaccadic spatial displacements of visual stimuli are easier to detect when saccades are perpendicular to the direction of the displacement, compared to saccades parallel to displacement. Here, we examined whether similar effects of saccade direction influence transsaccadic motion tracking in a predicted-motion time-to-contact (TTC) task. Subjects maintained fixation on a cross while tracking a moving dot which translated across the screen towards a line. During movement, the dot would become occluded behind a grey bar. Subjects then estimated when the occluded dot had reached the line via a button press. During the fixation task, participants maintained fixation on a stationary fixation cross. In the saccade task, the cross moved to a new location when the dot disappeared behind the occluder, prompting a saccade. Saccades were either parallel or orthogonal to dot motion. Dot motion (right or left) and line location on occlude (near, middle, or far) were also varied. Eye position was monitored using a SMI RED-m eyetracker. Results showed that the effect for line location was only significant in the saccade task, and that error increased with increased line distance, suggesting that saccades introduce error in TTC estimations. In the fixation task, TTC estimations were most accurate when subjects fixated on the same side of the screen from which the dot originated. These novel findings provide insight into people’s accuracy in predicting the future location of objects across saccades.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".