Snakes are as fast as ladders: Evidence against the hypothesis that contrast facilitation mediates contour detection
Bibliographic record
Abstract
It is easy to detect a “snake” consisting of spatially separated, collinear elements, embedded in a field of randomly oriented elements (Field, Hayes & Hess, 1993, Vision Research, 33, 173–193). Performance is poor when elements are oriented 45 degrees to the contour, but improves when elements are orthogonal to the contour (“ladders”) (Ledgeway, Hess & Geisler, 2005, Vision Research, 45, 2511–2522). Contour detection has been related to the phenomenon of contrast facilitation, whereby the contrast threshold for detection of an element is reduced when it is flanked by other elements: many models assume that contours are detected through the modulation of neuronal activity by the facilitatory signals that underlie contrast facilitation. If this were the case, one would expect contour detection to show similar temporal properties to contrast facilitation. Cass & Spehar (2005, Vision Research, 45, 3060–3073) used a psychophysical procedure to estimate the speed of propagation of contrast facilitation signals; their results suggest that the facilitatory signals from collinear flankers propagate much more slowly than those from non-collinear flankers. We investigated the effect of temporally modulating the orientation of contour elements from collinear to diagonal, or from orthogonal to diagonal. If contour detection and contrast facilitation are mediated by the same mechanisms, then the integration of snake contours should be much slower, and should be disrupted at much lower temporal frequencies, than the integration of ladder contours. We found identical temporal properties for both contour types, suggesting that contour integration is mediated by different mechanisms from contrast facilitation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".