Form and motion processing of second-order stimuli in color vision
Bibliographic record
Abstract
We investigate whether there are second-order form and motion mechanisms in human color vision. Second-order stimuli are contrast modulations of a noise carrier. The contrast envelopes are static Gabors of different spatial frequencies (0.125-1 cycles/°) or drifting Gabors of different temporal frequencies (0.25 cycles/°, 0.5-4 Hz). Stimuli are isoluminant red-green or achromatic. Second-order form processing is measured using a simultaneous 2IFC (two-interval forced-choice) detection and orientation identification task, and direction identification is used for second-order motion processing. We find that for simple detection thresholds, chromatic performance is as good or better than achromatic performance, whereas for both motion and form tasks, chromatic performance is poorer than achromatic. Chromatic second-order form perception is very poor across all spatial and temporal frequencies measured and has a lowpass contrast modulation sensitivity function with a spatial cutoff of 1 cycle/° and temporal cutoff of 4 Hz. Chromatic second-order motion sensitivity is even poorer than for form and typically is limited to 1-2 Hz. To determine whether this residual motion processing might be based on feature tracking, we used the pedestal paradigm of Lu and Sperling (1995). We find that adding a static pedestal of the same spatial frequency as the drifting Gabor envelope, with its contrast set to 1-2 times its detection threshold, impairs motion direction performance for the chromatic stimuli but not the achromatic. This suggests that the motion of second-order chromatic stimuli is not processed by a second-order system but by a third-order, feature-tracking system, although a genuine second-order motion system exists for achromatic stimuli.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".