First- and second-order motion processing are separate at low temporal frequencies but common at high temporal frequencies
Bibliographic record
Abstract
There is an ongoing debate on whether first- and second-order stimuli are processed by common or separate mechanisms. Some authors suggest that they are initially processed by separate mechanisms and that a rectification within the second-order pathway enables post-rectification mechanisms to process both types of stimuli. Others suggest that nonlinearities within a common motion mechanism enable the detection of both types of stimuli. In the present study, observers were asked to discriminate the direction of drifting luminance- (LM, first-order) and contrast-modulated (CM, second-order) signals embedded in LM or CM dynamic noise. The signals were drifting at either a low (2 Hz) or high (8 Hz) temporal frequency. At low temporal frequencies, the results showed no cross-modal interaction: LM noise affected LM discrimination thresholds but had no or little impact on CM discrimination, and CM noise affected CM discrimination thresholds but had no or little impact on LM discrimination. This double dissociation implies that, at low temporal frequencies, LM and CM stimuli are processed, at least at some point, by separated mechanisms. At high temporal frequencies, the results showed a complete cross-modal interaction: LM noise affected both the LM and CM discrimination thresholds in similar proportions, and CM noise also affected both the LM and CM discrimination thresholds in similar proportions. This complete cross-modal interaction suggests that, at high temporal frequencies, LM and CM stimuli are processed by common mechanisms.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".