Perceived curvature in depth: a test of cue combination models using motion and binocular disparity
Bibliographic record
Abstract
We elucidate two properties of the intersection of constraints (IC) model of depth cue combination (Domini et al., 2006, Vision Research, 46, 1707–1723). First we show that, like the modified weak fusion (MWF) model (Landy et al., 1995, Vision Research, 35, 389–412), IC combines depth cues in a weighted sum that maximizes the signal-to-noise ratio of the combined cue. Thus, IC is more similar to MWF than may at first appear. Second, we show that IC measures perceived depth in terms of just-noticeable differences (JND's), and hence predicts that the perceived depth difference between two stimuli is proportional to the number of JND's separating them, regardless of what combination of disparity and motion cues are in play. We tested this prediction. Method We created two motion-defined random dot cylinders, mC and mE, with circular and elliptical cross-sections, respectively. We also created two disparity-defined random dot cylinders, dC and dE, and we measured points of subjective equality to match the perceived depth of dC to mC, and of dE to mE. Using a 2AFC method of constants design, we then measured depth JND's for motion-defined cylinders, using mC as a baseline, and calculated the number of JND's separating mC and mE. Similarly, we measured depth JND's for disparity-defined cylinders, using dC as a baseline, and calculated the number of JND's separating dC and dE. Results The number of JND's separating mC and mE was significantly different from the number of JND's separating dC and dE, even though the the perceived depth difference between mC and mE was the same as that between dC and dE. Conclusions The perceived depth difference between two stimuli is not proportional to the number of JND's separating them. This finding poses no challenge to MWF, but contradicts a key prediction of the IC model.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".