First- and second-order motion mechanisms are distinct at low but common at high temporal frequencies
Bibliographic record
Abstract
There is no consensus on the type of nonlinearity enabling motion processing of second-order stimuli. Some authors suggest that a nonlinearity specifically applied to second-order stimuli prior to motion processing (e.g., rectification process) recovers the spatial structure of the signal permitting subsequent first-order motion analyses (e.g., filter-rectify-filter model). Others suggest that nonlinearities within motion processing enable first-order-sensitive mechanisms to process second-order stimuli (e.g., gradient-based model). In the present study, we evaluated intra- and inter-attribute interactions by measuring the impact of dynamic noise modulators (either luminance (LM) or contrast-modulated (CM)) on the processing of moving LM and CM gratings. When the signal and noise were both of the same type, similar calculation efficiencies but different internal equivalent noises were observed at all temporal frequencies. At high temporal frequencies, each noise type affected both attributes by similar proportions suggesting that both attributes are processed by common mechanisms. Conversely, at low temporal frequencies, each noise type primarily impaired the processing of the attribute of the same type suggesting distinct mechanisms. We therefore conclude that two fundamentally different mechanisms are processing CM stimuli: one low-pass and distinct from the mechanisms processing LM stimuli and the other common to the mechanisms processing LM 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".