Visual velocity aftereffects in radial flow: Inherited and unique features
Bibliographic record
Abstract
A realistic radial flow field contains a range of local velocities, yet global movement is perceived at a single speed. The present study explores the contribution of complex motion sensors to this velocity percept, by recording changes in speed perception and speed sensitivity after adaptation to “scrambled”, or to coherently expanding/contracting large-scale radial flow. A drifting concentric sine grating, conveying motion in depth, was confined to non-abutting display sectors, defining different global patterns of flow. After adapting to a flow pattern in one display region, observers compared its speed to that in a non-adapted region. Velocity aftereffects (VAE-s) from continuous unchanging motion were independent of the pattern of flow: apparent speed was reduced and speed discrimination improved in inverse proportion to the speed of the test. Sensitivity to speed differences, however, was pattern specific - it was superior in expansion. Also, adapted expansion recovered its apparent speed when tested against non-adapted contraction, and direction reversals of the adapter attenuated scrambled, but not coherent VAE-s. No VAE-s were recorded for test motions opposite to the adapted direction. We conclude that higher-order optic flow mechanisms are not uniquely involved in velocity estimation per se, but modulate velocity judgments in response to changes in the ongoing flow. Independent expanding and contracting velocities rival and do not suppress one another when juxtaposed in space or in time. This unique motion opponency appears to be transient and depends on the 3D quality of the flow. It ensures that the speed of approaching objects is correctly perceived, regardless of stimulus history.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".