Bibliographic record
Abstract
Motion parallax, or differential image motion resulting from movement of the observer, provides powerful cues for both segmentation and depth perception. During such observer movement, reflexive eye movements occur to counter the foveal image motion. We previously (JOV 2011) measured depth and segmentation performance from motion parallax while minimizing eye movements by keeping a defined fixation point. Here we examine the pattern and influence of eye movements during motion parallax, under different fixation conditions. Observers performed lateral head translation while an electromagnetic tracker recorded head position. Stimuli consisted of random dots, whose horizontal displacements were synchronized proportionately to head movement by a scale factor (syncing gain), and were modulated to generate motion. Eye movements were recorded using a remote optical eye-tracking system. First we measured the ability of observers to maintain fixation during depth and segmentation tasks, using a defined fixation point. The fixational behavior was highly imperfect, with eye position varying around the target fixation point in synchrony with head movement. Thus the reflexive eye movements only compensate for a fraction of the retinal slip due to head movement. With free fixation, segmentation performance was largely unaffected, but depth perception deteriorated at high syncing gains. The fixation pattern again exhibited imperfect cancellation of retinal slip, with variation in synchrony with the head movement. Furthermore during the depth task, observers primarily fixated on the virtual surfaces, whereas during the segmentation task fixation was mostly at their boundary. The amplitude of eye movements was independent of the actual stimulus motion. Thus reflexive eye movements during motion parallax are imperfect, but psychophysical performance is affected only in specific conditions. These results illustrate how gaze behavior can be task-dependent, and suggest that during natural motion parallax observers fixate on surfaces for depth information, whereas they fixate along boundaries for segmentation. Meeting abstract presented at VSS 2012
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".