Motion aftereffect and motion fading: Same underlying mechanisms?
Bibliographic record
Abstract
Purpose: Prolonged viewing of a moving stimulus results in illusory motion of a test stimulus in the opposite direction (MAE) and illusory slowing (motion fading, MF) of the adaptation stimulus. The two phenomena address different aspects of motion, but they might (or might not) be effects of the same underlying neural mechanisms. Here we studied this hypothesis. Methods: In 3 experiments, we compared MAE and MF directly with the same adaptation stimuli and the same observers. Magnitudes of MAE and MF for different adaptation durations, 1st and 2nd order stimuli, monocular and dichoptic presentation, and center/surround structure with high and low contrast were recorded. Results: MAE and MF size varied similarly as a function of adaptation duration and for 1st and 2nd order stimuli. Interocular transfer (IOT) for MAE was significantly smaller for 1st than 2nd order stimuli, but for MF, IOT was equivalent for both stimulus kinds. MAE was stronger for high than low contrast stimuli, but MF was better for low than high contrast stimuli. Conclusion: The results support the hypothesis that both phenomena do not arise from the same underlying neural mechanisms, and that MF is determined more by mechanisms at higher levels of processing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".