Relating visual and pictorial space: Integration of binocular disparity and motion parallax
Bibliographic record
Abstract
Traditionally, perceptual spaces are defined by the medium through which the visual environment is conveyed (e.g., in a physical environment, through a picture, or on a screen). This approach overlooks the distinct contributions of different types of visual information, such as binocular disparity and motion parallax, that transform different visual environments to yield different perceptual spaces. The current study proposes a new approach to describe different perceptual spaces based on different visual information. A geometrical model was developed to delineate the transformations imposed by binocular disparity and motion parallax, including (a) a relief depth scaling along the observer's line of sight and (b) pictorial distortions that rotate the entire perceptual space, as well as the invariant properties after these transformations, including distance, three-dimensional shape, and allocentric direction. The model was fitted to the behavioral results from two experiments, wherein the participants rotated a human figure to point at different targets in virtual reality. The pointer was displayed on a virtual frame that could differentially manipulate the availability of binocular disparity and motion parallax. The model fitted the behavioral results well, and model comparisons validated the relief scaling in the form of depth expansion and the pictorial distortions in the form of an isotropic rotation. Fitted parameters showed that binocular disparity renders distance invariant but also introduces relief depth expansion to three-dimensional objects, whereas motion parallax keeps allocentric direction invariant. We discuss the implications of the mediating effects of binocular disparity and motion parallax when connecting different perceptual spaces.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".