Pooling strategies for the integration of orientation signals depend on their spatial configuration
Bibliographic record
Abstract
The visual system combines samples from the retinal image into representations of spatially extensive textures. Local orientation signals can be pooled over a texture to estimate global orientation, with psychophysical performance improving as a function of signal area. We used a novel stimulus to investigate how orientation signals are combined over space (whether observers could ignore signalsfrom irrelevant locations), and the effect of spatial configuration on this pooling. Stimuli were 24×24 element arrays of 4c/deg log-Gabors, spaced 1 degree apart. A proportion of these elements had a coherent orientation (horizontal/vertical), with the remainder assigned random orientations.The observer’s task was to identify the global orientation. The spatial configuration of the signal was modulated by a checkerboard-like pattern of square checks containing either potential signal elements or only irrelevant noise. The distribution of signal elements within the array was manipulated by varying the size and location of these checks within a fixed-diameter stimulus. A blocked staircase procedure found the threshold coherence for identification. An ideal detector would pool over just the relevant locations (vector-averaging and filter-maxing models make identical predictions for these signal combination effects), however humans only did this for medium (5×5 to 9×9) check sizes, and for large (15×15) check sizes when the signal was placed at the fovea. For small (1×1 to 3×3) check sizes and large (15×15) peripheral checks the pooling occurred indiscriminately over relevant and irrelevant locations. These findings suggest orientation signals are combined mandatorily over short ranges and in the periphery, but flexibly otherwise.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".