The intrinsic constraint approach to cue combination: An empirical and theoretical evaluation
Bibliographic record
Abstract
We elucidate two properties of the intrinsic constraint (IC) model of depth cue combination (F. Domini, C. Caudek, & H. Tassinari, 2006). First, we show that IC combines depth cues in a weighted sum that maximizes the signal-to-noise ratio of the combined estimate. Second, we show that IC predicts that any two depth-matched pairs of stimuli are separated by equal numbers of just noticeable differences (JNDs) in depth. That is, IC posits a strong link between perceived depth and depth discrimination, much like some Fechnerian theories of sensory scaling. We test this prediction, and we find that it does not hold. We also find that depth discrimination performance approximately follows Weber's law, whereas IC assumes that depth discrimination thresholds are independent of baseline stimulus depth.
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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 | 0.001 | 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 | 0.000 |
| 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".