Sensitivity for global shape detection
Bibliographic record
Abstract
In order to understand the nature of the mechanisms responsible for global shape detection, we measured coherence thresholds in a 2IFC task where subjects judged which of two arrays of Gabors contained global circular structure. The stimulus was composed of an array of oriented Gabor patches positioned on a polar grid. Thresholds were obtained for different array parameters (e.g. different area, density, number and positions of elements) as well as for different element parameters (e.g. different carrier spatial frequencies, contrasts, polarities and orientations). Global structure was detected when ~10% of the elements were coherently oriented. Neither the properties of the array (density, area, number or position of elements), nor those of the individual elements (carrier spatial frequency, contrast, polarity) altered coherence thresholds. Varying contrast or carrier spatial frequency within individual arrays also did not alter performance. Sensitivity was invariant to positional perturbations of the array grid. Only jittering the local orientation of elements decreased sensitivity. The underlying mechanisms are broadly tuned for contrast, spatial frequency and the spatial positioning of image samples. Detecting circular structure is a robust process and, in this case, a purely global one. Sensitivity was highest for circular as opposed to radial or spiral shapes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".