Aftereffects of motion in depth based on binocular cues
Bibliographic record
Abstract
Purpose. Lateral motion aftereffects (MAEs) have been studied extensively. Less is known about MAEs in depth. We investigated whether adaptation to stimuli moving in depth induces MAEs in depth. Methods. The adaptation stimulus consisted of two frontoparallel planes, depicted by random-element stereograms, one above and one below the fixation point. The two planes repeatedly moved in depth in opposite directions for 2 minutes. The motion-in-depth was specified by interocular velocity differences and/or changing disparity by using the random elements which were spatially and temporally correlated in the two eyes (RDS), those which were spatially uncorrelated but temporally correlated (URDS), or those which were spatially correlated but temporally uncorrelated (DRDS). The test stimulus consisted of a RDS, URDS, DRDS or monocularly viewed random elements that did not move in depth. The subject pressed a key when any apparent motion in depth of the test stimulus ceased. Results and discussion. Under some conditions the test stimulus appeared to move in depth in the direction opposite to that of the adaptation stimulus (negative MAE). Specifically, adaptation to motion-in-depth of RDS and URDS produced MAEs in many test stimuli, while adaptation to DRDS produced little or no MAE in most test stimuli. While further experimentation is required, this finding suggests that adaptation to interocular velocity differences produces substantial MAEs in depth, but that adaptation to changing disparity produces little or no MAE. Also, a monocular test stimulus showed a MAE in a diagonal direction in depth. The depth component of the MAE under monocular test conditions indicates that binocular processes are involved in generating MAEs in depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.003 | 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".