Classification image analysis of oriented texture detection
Bibliographic record
Abstract
Humans readily perceive the structure in oriented textures, e.g., Glass patterns. Here we seek to understand the computational factors that underlie their detection. In a series of experiments, random-dot patterns were displayed briefly (165 ms) within the central 14 deg of the visual field. Each stimulus consisted of 200 dipoles distributed over a discrete number of orientations. On noise-only trials, dipoles were uniformly distributed over all orientations. On signal-plus-noise trials, a portion of the 200 dipoles were oriented in a common direction, and the remaining were oriented randomly. Detection thresholds were estimated by varying the proportion of signal dipoles using the QUEST procedure in a Yes/No task with feedback. We derived an ideal observer for this task, and found that human efficiency for dipole texture detection is roughly 1%. We then considered a model observer that is ideal except for three types of inefficiency: 1) false matches between dipole dots (correspondence errors), 2) orientation uncertainty and bias, and 3) decline in sensitivity with eccentricity. By comparing detection performance for oriented textures based on dipole dots with performance for oriented line segments, we estimated that false matches reduce efficiency by a factor of approximately 3. Using a classification image technique in the orientation domain, we estimated observer bias and uncertainty in detecting elements at the signal orientation. Incorporating the estimated orientation uncertainty and bias into our model accounts for an additional factor of 5 reduction in efficiency. We used the same classification image technique to model the decrease in sensitivity with eccentricity, finding that this factor accounts for an additional factor of 6 reduction in efficiency. Our results suggested that the three factors: correspondence errors, orientation uncertainty, and decline in sensitivity with eccentricity, account for roughly 90% of the perceptual losses in the detection of oriented textures such as 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".