Representation and measurement of stereoscopic volumes
Bibliographic record
Abstract
Binocular disparity information provides the human visual system with a basis for the compelling perception of both three-dimensional (3-D) object shape, and of the 3-D space between objects. However, while an extensive body of research exists into the perception of disparity-defined surface shape, relatively little research has been conducted on the associated perception of disparity-defined volume. In this paper, we report three experiments that examine this aspect of binocular vision. Participants were asked to make judgments about the 3-D spread, location-in-depth, and 3-D shape of stereoscopic volumes. Volumes were comprised of random dots with disparities drawn from a uniform distribution, a Gaussian distribution, or a combination of both. These results were compared to two models: One of these made judgments about stereoscopic volumes using information about the distributions of disparities in each stimulus, while the other was limited to only maximum and minimum disparity information. Psychophysical results were best accounted for by the maximum-minimum decision rule model. This suggests that, although binocular vision affords a compelling phenomenal sense of 3-D volume, when required to make judgments about such volumes, the visual system's default strategies make only limited use of available binocular disparity signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".