The dichoptiscope: An instrument for investigating cues to motion in depth
Bibliographic record
Abstract
A stereoscope displays 2-D images with binocular disparities (stereograms), which fuse to form a 3-D stereoscopic object. But a stereoscopic object creates a conflict between vergence and accommodation. Also, motion in depth of a stereoscopic object simulated solely from change in target vergence produces anomalous motion parallax and anomalous changes in perspective. We describe a new instrument, which overcomes these problems. We call it the dichoptiscope. It resembles a mirror stereoscope, but instead of stereograms, it displays identical 2-D or 3-D physical objects to each eye. When a pair of the physical, monocular objects is fused, they create a dichoptic object that is visually identical to a real object. There is no conflict between vergence and accommodation, and motion parallax is normal. When the monocular objects move in real depth, the dichoptic object also moves in depth. The instrument allows the experimenter to control independently each of several cues to motion in depth. These cues include changes in the size of the images, changes in the vergence of the eyes, changes in binocular disparity within the moving object, and changes in the relative disparity between the moving object and a stationary object.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".