The Magnocellular visual pathway and the flash-lag illusion
Bibliographic record
Abstract
Determining how the visual system locates moving stimuli continues to be an experimental and theoretical challenge. By making a moving visual stimulus equiluminant with its background, and immersing it in luminance noise, the spatial lead it normal enjoys over a flashed stimulus (the flash-lag illusion) was completely eliminated (the illusion was actually reversed for 6 out of 11 participants). As this manipulation is typically used to reduce Magnocellular (M) visual pathway processing, this is strong evidence that processing in this pathway advances the moving stimulus' perceived position. However, when the flashed stimulus was also made equiluminant in luminance noise, the illusion reappeared, indicating that M pathway processing contributed to its perception too. The presence of the illusion when both stimuli were equiluminant in luminance noise indicates that the illusion can be generated in the absence of M cell activation. To explicate the result with moving stimuli, we displayed two adjacent moving stimuli, one luminance-modulated, and the other equiluminant in noise. The latter was perceived to significantly lag the former (an 'M-P-Hess' illusion), and 39% of the difference in flash-lag illusions, with comparable moving stimulus contrasts, could be accounted for by this illusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".