Relating visual and pictorial space: Binocular disparity for distance, motion parallax for direction
Bibliographic record
Abstract
Interacting with people and three-dimensional objects depicted on a screen is perceptually different from interacting with them in real life. This difference resides in their corresponding perceptual spaces: The former involves pictorial space, and the latter, visual space. Studies have examined the perceptual geometry of pictorial or visual space, but rarely their connection. The current study connected the two spaces using a pointing task and investigated how binocular disparity and motion parallax affect this connection. In a virtual environment, a pointing virtual character was displayed within a frame and the participants rotated him to point at targets in visual space. What binocular disparity and motion parallax specified was independently manipulated, either the two-dimensional surface or its depicted three-dimensional content. In Experiment 1, we changed the virtual character's distance to the screen and found that binocular disparity determines the distance relationship between visual and pictorial space, but also introduces a relief depth expansion of the perceived virtual character. In Experiment 2, we changed the participants' viewing angle relative to the screen and found that motion parallax determines the directional relationship between visual and pictorial space. We discuss the theoretical and practical implications of our results in the context of video-mediated telecommunication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".