Effects of Vertical Direction and Aperture Size on the Perception of Visual Acceleration
Bibliographic record
Abstract
It is not well understood whether the distance over which moving stimuli are visible affects our sensitivity to the presence of acceleration or our ability to track such stimuli. It is also uncertain whether our experience with gravity creates anisotropies in how we detect vertical acceleration and deceleration. To address these questions, we varied the vertical extent of the aperture through which we presented vertically accelerating and decelerating random dot arrays. We hypothesized that observers would better detect and pursue accelerating and decelerating stimuli that extend over larger than smaller distances. In Experiment 1, we tested the effects of vertical direction and aperture size on acceleration and deceleration detection accuracy. Results indicated that detection is better for downward motion and for large apertures, but there is no difference between vertical acceleration and deceleration detection. A control experiment revealed that our manipulation of vertical aperture size affects the ability to track vertical motion. Smooth pursuit is better (i.e., with higher peak velocities) for large apertures than for small apertures. Our findings suggest that the ability to detect vertical acceleration and deceleration varies as a function of the direction and vertical extent over which an observer can track the moving stimulus.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".