Modeling biases of perceived slant in curved surfaces
Bibliographic record
Abstract
Veridical perception of surface slant is important to everyday tasks such as traversing terrain and interacting with or placing objects on surfaces. However, natural surfaces contain higher-order depth variation, or curvature, which may impact how slant is perceived. We propose a computational model which predicts that curvature, real or distortion-induced, biases the perception of surface slant. The model is based on the perspective projection of surfaces to form “retinal images” containing monocular and binocular texture cues (gradients) for slant estimation. Curvature was either intrinsic to the modelled surface or induced by non-uniform magnification i.e. radial distortion (typical in wide-angle lenses and head-mounted display optics). The resulting binocular and monocular texture gradients derived from these conditions make specific predictions regarding perceived surface slant. In a series of psychophysical experiments we tested these predictions using slant discrimination and magnitude estimation tasks. Our results confirm that local slant estimation is biased in a manner consistent with apparent surface curvature. Further we show that for concave surfaces, irrespective of whether curvature is intrinsic or distortion-induced, there is a net underestimation of global surface slant. Somewhat surprisingly, we also find that the observed biases in global slant are driven largely by the texture gradients and not by the concurrent changes in binocular disparity. This is due to vertical asymmetry in texture gradients of curved surfaces with overall slant. Our results show that while there is a potentially complex interaction between surface curvature and slant perception, much of the perceptual data can be predicted by a relatively simple model based on perspective projection. The work highlights the importance of evaluating the impact of higher-order variations on perceived surface attitude, particularly in virtual environments in which curvature may be intrinsic or caused by optical distortion.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".