Evidence that global processing does not limit thresholds for RF shape discrimination
Bibliographic record
Abstract
In studies of shape processing, a crucial distinction is made between the global stages, which integrate across features to define shape, and earlier stages that encode individual components. We investigate whether shape discrimination thresholds for radial frequency (RF) patterns are limited at this global stage or whether the information in individual components supports threshold. We use achromatic and chromatic (L/M- and S-cone opponent) radial frequency (RF) patterns of different contour thicknesses (0.75-6 cpd). First, we show using sections of an RF4 that shape discrimination thresholds are invariant with cycle number from 1 to the complete pattern. Performance for a single cycle displayed alone is as good as for the whole RF, indicating that information within a single RF cycle is sufficient to support the whole shape discrimination threshold, arguing against an influence of global processing. Second, we find similar thresholds for the discrimination of RF patterns and modulated line stimuli, also arguing against global effects. Third, we calculate a metric for the intrinsic orientation variation in a stimulus cycle at threshold and show that this potentially accounts for the improvement in shape and line discrimination thresholds with modulation frequency from RF1 to RF6. Higher threshold discrimination for chromatic compared to achromatic patterns may reflect the poorer orientation discrimination of color vision, rather than a deficit for global processing. We propose that the global stages of shape processing are not revealed at threshold but are enabled only for well-defined shapes at suprathreshold modulations.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".