Different extrapolation of moving object locations in perception, smooth pursuit, and saccades
Bibliographic record
Abstract
The ability to accurately perceive and track moving objects is crucial for many everyday activities. In this study, we use a "double-drift stimulus" to explore the processing of visual motion signals that underlie perception, pursuit, and saccade responses to a moving object. Participants were presented with peripheral moving apertures filled with noise that either drifted orthogonally to the aperture's direction or had no net motion. Participants were asked to saccade to and track these targets with their gaze as soon as they appeared and then to report their direction. In the trials with internal motion, the target disappeared at saccade onset so that the first 100 ms of the postsaccadic pursuit response was driven uniquely by peripheral information gathered before saccade onset. This provided independent measures of perceptual, pursuit, and saccadic responses to the double-drift stimulus on a trial-by-trial basis. Our analysis revealed systematic differences between saccadic responses, on one hand, and perceptual and pursuit responses, on the other. These differences are unlikely to be caused by differences in the processing of motion signals because both saccades and pursuits seem to rely on shared target position and velocity information. We conclude that our results are instead due to a difference in how the processing mechanisms underlying perception, pursuit, and saccades combine motor signals with target position. These findings advance our understanding of the mechanisms underlying dissociation in visual processing between perception and eye movements.
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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".