Bibliographic record
Abstract
Binocular disparity provides the human visual system with estimates of both the three-dimensional shape of surfaces, and of the depth between them. Additionally, binocular disparity cues provide a compelling sense of the volume occupied by objects in space. However, studies of stereoscopic vision have tended to examine the perceived depth of isolated points, or the perceived structure of surfaces in depth, without addressing the associated sense of volume. Comparatively little is known about how the visual system represents stereoscopic volumes. The experiments reported here address this issue by examining observers’ ability to judge changes in the range and distribution of disparity-defined volumes of dots. Observers were presented with Gaussian distributed random-dot volumes in a three interval, odd-one-out detection task. Each interval was presented for 200ms, and contained a stereoscopic volume within an area of 4.8 x 4.8 degrees. In two (standard) intervals, dot disparities were drawn from a Gaussian distribution of fixed standard deviation 1.1arcmin. In the third (target) interval a proportion of dot disparities were drawn from a uniform distribution with a range of between ±1.1arcmin and ± 7.7arcmin, with the remaining dots drawn from the same Gaussian distribution as the standard intervals. For some ranges, an entirely uniform distribution could not be distinguished from the Gaussian standards. Instead, the ability to detect the odd interval depended largely on the range of the distribution, not its shape. Changes in dot density, and in the standard deviation of the Gaussian distribution did not lead to a general sensitivity for distribution shape, but instead resulted in changes to the range of uniform distributions where the target interval could not be reliably detected. Our results suggest that the visual system makes use of an impoverished representation of the structure of stereoscopic volumes. Meeting abstract presented at VSS 2012
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".