Oriented texture detection: Ideal observer modelling and classification image analysis
Bibliographic record
Abstract
Perception of visual texture flows contributes to object segmentation, shape perception, and object recognition. To better understand the visual mechanisms underlying texture flow perception, we studied the factors limiting detection of simple forms of texture flows composed of local dot dipoles (Glass patterns) and related stimuli. To provide a benchmark for human performance, we derived an ideal observer for this task. We found that human detection thresholds were 8.0 times higher than ideal. We considered three factors that might account for this performance gap: (1) false matches between dipole dots (correspondence errors), (2) loss of sensitivity with increasing eccentricity, and (3) local orientation bandwidth. To estimate the effect of correspondence errors, we compared detection of Glass patterns with detection of matched line-segment stimuli, where no correspondence uncertainty exists. We found that eliminating correspondence errors reduced human thresholds by a factor of 1.8. We used a novel form of classification image analysis to directly estimate loss of sensitivity with eccentricity and local orientation bandwidth. Incorporating the eccentricity effects into the ideal observer model increased ideal thresholds by a factor of 2.9. Interestingly, estimated orientation bandwidth increased ideal thresholds by only 8%. Taking all three factors into account, human thresholds were only 58% higher than model thresholds. Our findings suggest that correspondence errors and eccentricity losses account for the great majority of the perceptual loss in the visual processing of Glass patterns.
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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 | 0.001 |
| 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".