Bibliographic record
Abstract
In the furrow illusion (Anstis, 2012), the perceived path of a moving target follows the veridical path orientation when viewed foveally, but follows the orientation of the texture when viewed peripherally. These radically different motion percepts depending on whether the stimulus is viewed foveally or peripherally has led Anstis to conclude that the furrow illusion reveals "profound differences in the way that the periphery and fovea process visual motion." In the current study, we rather argue that the different percepts can be explained by reduced position acuity with eccentricity and therefore do not imply different ways of processing motion per se. If feature tracking, which is position-based, is involved in the perception of the veridical motion direction, then impairing the feature tracking motion system should strengthen the illusion. To reduce contribution of the feature tracking motion system, we used a crowding paradigm consisting in presenting many nearby targets. We found that under crowding conditions, the furrow illusion was stronger. We conclude that feature tracking was involved in the perception of the veridical motion direction, which is compatible with the hypothesis that the different motion percepts at fixation and in the periphery are due to a reduced position acuity with eccentricity affecting feature tracking, not to different ways of processing motion per se.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".