Feature integration within and across visual streams occurs at different visual processing stages
Bibliographic record
Abstract
Direction repulsion is a perceptual illusion in which the directions of two superimposed surfaces are repulsed away from the real directions of motion. The repulsion is reduced when the surfaces differ in dorsal stream features such as speed. We have previously shown that segmenting the surfaces by color, a ventral stream feature, did not affect repulsion but instead reduced the time needed to process both surfaces. The current study investigated whether segmenting two superimposed surfaces by a feature coprocessed with direction in the dorsal stream (i.e., speed) would also reduce processing time. We found that increasing the speed of one or both surfaces reduced direction repulsion. Since color segmentation does not affect direction repulsion, these results suggest that motion processing integrates speed and direction prior to forming an object representation that includes ventral stream features such as color. Like our previous results for differences in color, differences in speed also decreased processing time. Therefore, the reduction in processing time derives from a later processing stage where both ventral and dorsal features bound into the object representations can reduce the time needed for decision making when those features differentiate the superimposed surfaces from each other.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".