Motion and form processing in second order colour vision
Bibliographic record
Abstract
It is now accepted that human vision can extract information from the visual scene not just by using a first order system, but also by using a second order system that extracts variations in contrast or texture. In a recent study (Garcia-Suarez & Mullen, 2010), we found no global motion processing for second order chromatic limited lifetime RDK elements. Here we investigate second order from processing in colour vision. Second order stimuli were contrast modulated isoluminant red-green and achromatic static Gabor envelopes with spatial frequencies varying from 0.125 to 1.5 cycles/degree. The carrier was a static flat-spectrum noise, lowpass filtered (2 cycles/degree cut-off) to avoid luminance artefacts from chromatic aberration. The chromatic and achromatic carriers were matched in visibility. Detection thresholds and orientation identification thresholds were measured simultaneously for two observers using a 2AFC paradigm with the method of constant stimuli. For both chromatic and achromatic conditions, there is a significant gap between detection and orientation identification thresholds, with this gap being greater for the chromatic condition, suggesting that orientation performance is worse for chromatic than for achromatic second order stimuli once any differences in detection are taken into account. We found form processing for second order stimuli in colour vision, although it may be worse than the achromatic condition. These results suggest that the ventral pathway has specialized in the form processing of second order chromatic stimuli, whereas the MT area (global motion site) from the dorsal pathway only receives functional input from the achromatic system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".