Aftereffect of motion-in-depth based on binocular cues: No effect of relative disparity between adaptation and test surfaces
Bibliographic record
Abstract
Previously, we found that a motion aftereffect (MAE) in depth can occur after adaptation to motion-in-depth in random-element stereograms (VSS 2005). In the present study, we investigated the depth selectivity of the MAE in depth. The adaptation stimulus consisted of two frontoparallel surfaces, one above and one below the fixation point. These surfaces were depicted by random-dot stereograms that were temporally correlated (RDS) or uncorrelated (DRDS). During the 2-min adaptation phase, the disparity of one surface increased and that of the other surface decreased linearly and repeatedly to simulate smooth motion-in-depth. The range of these disparity ramps was −26.2 to −8.72, −8.72 to +8.72, or +8.72 to +26.2 arcmin, where positive and negative values indicate crossed and uncrossed disparity. The test stimulus consisted of two stationary frontoparallel surfaces depicted by a RDS with a fixed pedestal disparity of either −17.4, 0, or +17.4 arcmin. Under RDS adaptation conditions, robust MAE in depth occurred. The duration of this MAE in depth did not depend on the relation between the disparity range of the adaptation stimulus and the pedestal disparity of the test stimulus. Under DRDS adaptation conditions, MAE in depth did not occur. These results suggest that the adaptable processes used to detect motion-in-depth from binocular cues are insensitive to pedestal disparity.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".