Effects of Saccade Size, Target Position, and Allocentric Cues in Transsaccadic Motion Perception
Bibliographic record
Abstract
During each saccade the image of the world shifts across our retina yet we have little trouble keeping tracking of object locations in our surroundings. Transsaccadic perception of moving objects remains unclear. Previous transsaccadic perception studies investigating how well observers detect an object’s intrasaccadic displacement have used either stationary stimuli or moving stimuli over relatively small, orthogonal saccades relative to motion direction (Gysen et al. 2002). Here, we extend this literature by examining transsaccadic motion perception using smoothly translating motion targets (dot) over different saccade directions, amplitudes, and background conditions. Subjects were required to make a saccade when a fixation point moved from center screen to a different location. On some trials, the dot jumped forward or backward during the saccade. Subjects made a 2AFC response to indicate if they detected a displacement or not. Eye movements were measured using the SMI RED eye tracker. The first experiment we systematically varied saccade amplitude and direction to determine how different saccade metrics might influence subject accuracy in detecting intrasaccadic displacement of moving stimuli. We also examined the extent to which displacement size and relative post-saccadic location of the motion stimulus might influence detection performance. In the second experiment we varied the number and stability of allocentric cues presented in the background during the same transsaccadic tracking task as experiment one. In experiment one we found subjects were most accurate in detecting intrasaccadic displacement of moving targets when: 1) Saccades were small, retinal eccentricity of target was small, and target displacement was large. The second experiment confirmed these findings and we also found that allocentric background cues aided participant’s performance when they were stable or moving in the same direction as the target. Our novel findings suggest the same basic processes are involved in transsaccadic perception for both static and dynamic stimuli.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".