Interactions between cues to visual motion in depth
Bibliographic record
Abstract
Information about the motion in depth of an object along the midline of a stationary observer is provided by changes in image size (looming), changes in vergence produced by changes in binocular disparity of the images of the object, and changes in relative disparity between the moving object and a stationary object. Each of these cues was independently varied in the dichoptiscope, which is described in Howard, Fukuda, and Allison (2013). The stimuli were a small central dot and a textured surface moving to and fro in depth along the midline. Observers tracked the motion with the unseen hand. Image looming was normal or absent. The change in vergence was absent, normal, more than normal, or reversed relative to normal. Changing relative disparity between the moving stimulus and a stationary surface was present or absent. Changing vergence alone produced no motion in depth for the textured surface but it produced some motion of the dot. Looming alone produced strong motion in depth for the texture but not for the dot. When the direction of motion indicated by looming was opposite that indicated by changing relative disparity observers could use either cue. The cues dissociated rather than combined.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".