On the combination of illusory and luminance-defined stereoscopic surfaces
Bibliographic record
Abstract
In stereoscopic Kanizsa figures the shape of the interpolated illusory surface is determined by luminance-defined disparity signals that exist only along edges of inducing elements. Despite ambiguity in the position of illusory boundaries, observers reliably perceive a coherent surface in depth between the inducers. However, the ambiguity of illusory boundaries may contribute additional uncertainty in the depth percept beyond what is expected from measurement noise alone. We evaluated the intrinsic uncertainty of illusory boundaries by measuring the reliability of depth percepts elicited by stereoscopic illusory surfaces using a cue combination paradigm. We systematically assessed the accuracy and precision of suprathreshold depth percepts using Kanizsa figures with a range of inducer disparities. For comparison, we assessed perceptually equated luminance-defined surfaces, with and without inducing elements. The location of the surface peak in these configurations was defined by illusory boundaries, low contrast luminance-defined edges, or a combination of both. Accuracy was evaluated using a disparity-matching procedure and precision was assessed using a 2IFC depth discrimination paradigm. A maximum likelihood, linear cue combination model was used to evaluate the relative contribution of illusory and luminance-defined signals to the perceived depth of the combined surface. Our analysis showed that the variance in estimated depth was consistent with optimal combination, but the points of subjective equality (PSE) showed that observers consistently underweighted the contribution of illusory boundaries. This systematic underweighting is consistent with a combination rule that attributes additional intrinsic uncertainty to the location of the illusory boundary due to the lack of luminance-defined features that define its location in 3D space. While previous studies have shown that illusory and luminance-defined contours are processed similarly in some ways, our results demonstrate that illusory contours have additional intrinsic uncertainty beyond what we would expect from perceptually matched luminance-defined contours. Meeting abstract presented at VSS 2017
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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".