No second-order motion system sensitive to high temporal frequencies
Bibliographic record
Abstract
It has been shown that the perception of contrast-defined motion (i.e., a second-order stimulus) at high temporal frequencies cannot be explained solely by global distortion products (i.e., luminance artifacts due to preprocessing nonlinearities) processed by the first-order system. However, previous studies rejecting the first-order pathway hypothesis have assumed that the preprocessing nonlinearities are identical for all first-order motion units. If this is not the case, then introducing a nonlinearity within the stimulus could neutralize the global (i.e., mean) distortion product but would leave residual distortion products. We neutralized either global only or both global and residual distortion products by superimposing a luminance modulation onto the contrast modulation. At a temporal frequency too high for features to be tracked (15 Hz), we found a substantial texture (i.e., contrast-modulated) contribution to motion when neutralizing only global distortion products but not when neutralizing both global and residual distortion products. Furthermore, we found that the texture contribution to motion at this high temporal frequency, when it was not completely neutralized, depended on the phase difference between luminance and contrast modulations, which implied some common processing before the motion extraction stage. We concluded that the texture contribution to motion at high temporal frequencies was due to nonuniform preprocessing nonlinearities within the visual system, enabling first-order motion units to process distortion products, and not due to a dedicated second-order motion 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 | 0.000 | 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) | 0.002 | 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".