Asymmetries in the discrimination of motion direction around the visual field
Bibliographic record
Abstract
The discriminability of motion direction is asymmetric, with some motion directions that are better discriminated than others. For example, discrimination of directions near the cardinal axes (upward/downward/leftward/rightward) tends to be better than oblique directions. Here, we tested discriminability for multiple motion directions at multiple polar angle locations. We found three systematic asymmetries. First, we found a large cardinal advantage in a cartesian reference frame - better discriminability for motion near cardinal reference directions than oblique directions. Second, we found a moderate cardinal advantage in a polar reference frame - better discriminability for motion near radial (inward/outward) and tangential (clockwise/counterclockwise) reference directions than other directions. Third, we found a small advantage for discriminating motion near radial compared to tangential reference directions. The three advantages combine in an approximately linear manner, and together predict variation in motion discrimination as a function of both motion direction and location around the visual field. For example, best performance is found for radial motion on the horizontal and vertical meridians, as these directions encompass all three advantages, whereas poorest performance is found for oblique motion stimuli located on the horizontal and vertical meridians, as these directions encompass all three disadvantages. Our results constrain models of motion perception and suggest that reference frames at multiple stages of the visual processing hierarchy limit performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".