Occlusion-based stereopsis with alternating presentation of the stereo half images
Bibliographic record
Abstract
Recently, it was reported that stereoscopic phenomena, including stereopsis and binocular luster, could be differentiated according to dichoptic alternation frequency thresholds (Ludwig, et al., 2007, AP&P 69(1), 92-102; Rychkova & Ninio, 2011, iPerception (2), 50-68). In these studies, left and right eye images were alternated at various rates and the threshold was defined as the minimum alternation frequency for which participants reliably reported depth or luster. In these two studies, alternation thresholds for various stereoscopic phenomena ranged from approximately 3Hz to approximately11Hz. We applied this technique to explore temporal integration for several occlusion-based stereoscopic phenomena (e.g. da Vinci stereopsis (Nakayama & Shimojo, 1990, Vis Res 30(11), 1811-1825), monocular gap stereopsis (Gillam et al., 1999, Vis Res 39(3), 493-502), and phantom stereopsis (Anderson, 1994, Nat 367, 365-368)). Perceived depth in these stimuli is thought to result from the binocular combination of non-corresponding monocular occlusion zones that are geometrically consistent with an occlusion resolution. Participants viewed alternating dichoptic images in a mirror stereoscope. Each stereo-pair depicted two depth orders of the same stimulus, separated spatially. One depth order was geometrically consistent with a nearer target and the other was either consistent with a farther target or inconsistent with occlusion altogether. Using the method of adjustment, participants either increased the alternation rate of the stereo half images until they could identify which of the two stimuli depicted the surface arrangement with the nearer target, or they reduced the alternation rate until they could not make this discrimination. We computed mean alternation rates between randomized ascending and descending trials for each participant. Depth discrimination was reliable across a range alternation rates from approximately 3Hz to approximately 10Hz. Temporal integration of alternating stereo half images of occlusion-based stereograms differs across occlusion-based stereo phenomena but falls within the range observed for disparity-based stereopsis and binocular luster. 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 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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".