Stereoscopic surface interpolation from illusory contours
Bibliographic record
Abstract
Stereoscopic Kanizsa figures are an example of 3D illusory surface interpolation. In such stimuli, luminance-defined disparity signals exist only along the edges of the inducing elements, but observers reliably perceive a coherent surface in depth that extends across the central blank region. It is widely assumed that the 3D shape of the illusory surface is solely determined by the local disparity at the inducer edges, and then extrapolated to form a surface representation. Here we evaluate another alternative: that the illusory contours themselves also provide a disparity signal that contributes to the perceived 3D shape of the illusory surface. We measured stereoscopic depth magnitude percepts using a collection of Kanizsa figures with a wide range of inducer disparities. For comparison, we assessed similar figures with luminance-defined surfaces and a third condition where both illusory and luminance edges were present. Prior to testing, we measured individual diplopia thresholds for the inducer contours. Also, using a matching paradigm, we equated the perceived strength of the surfaces defined by luminance alone and by illusory contours. During testing, 3D Kanizsa figures were displayed in random order on a mirror stereoscope, and depth magnitude estimates were made using a touch sensitive sensor strip. We found that all 6 participants made depth magnitude estimates consistent with simple interpolation geometry, for all stimuli in the fused range; moreover, in this range of disparities, estimates for the illusory and luminance-defined stimuli were identical. At larger disparities, all observers reported increasing depth percepts with increasing disparity for the illusory figures. However, for half of our observers, depth estimates plateaued for the luminance-defined surfaces only. The correspondence between the suprathreshold depth percepts from luminance-defined and illusory Kanizsa surfaces, and the resilience of depth percepts from illusory figures suggests that in some cases binocular disparity can be extracted from illusory edges. Meeting abstract presented at VSS 2016
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".