Bibliographic record
Abstract
Previous research on motion perception has found that static luminance texture produces an increase in the perceived speed of moving stimuli. The experiments discussed here study the influence of texture on another aspect of motion perception: the motion aftereffect (MAE). In Experiment 1, we measured static MAE duration in three conditions: 1- luminance modulated gratings to which no texture was added 2- luminance modulated gratings to which static texture (static luminance noise) was added 3- contrast modulated noise. Our results demonstrate that adding static luminance texture to a drifting luminance-modulated sinewave grating greatly diminishes static MAE duration and can even completely eliminate the static MAE. Our results also show that no difference in static MAE duration occurred between luminance-modulated gratings to which static luminance texture was added and contrast-modulated noise. This suggests that the failure of contrast-modulated texture to elicit a static MAE may not come from a fundamental difference in the processing of first- and second-order motion, but from the luminance texture inherently present in these second-order stimuli. Our results and the static MAE are discussed in a Bayesian context in which adaptation creates a shift in the prior's center in the direction opposite to the direction of adaptation and texture is used as a landmark. This is consistent with a recalibration and error-correcting account of the MAE. In Experiment 2, we studied the effects of texture characteristics on the MAE by notch-filtering the luminance noise in Fourier space. Preliminary data show that filtering out high pass information along the axis of motion produces longer MAE durations, but that filtering out high-pass information along an axis orthogonal to the axis of motion does not. This is consistent with the proposal that the visual system uses luminance texture in the assessment of motion.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".