Visual sensitivity to acceleration: Effects of motion orientation, velocity, and size
Bibliographic record
Abstract
Traditional studies of acceleration perception have measured acceleration sensitivity in terms of the ratio of final to initial velocity or the proportion of change in velocity relative to the average velocity. From these studies, it is unclear as to how sensitivity to visual acceleration is affected by stimulus properties such as motion orientation, base velocity, and size. Here, we measured visual sensitivity to acceleration by parameterizing acceleration as it is defined: the change in velocity per unit time. Observers (n = 18) were asked to discriminate an accelerated stimulus from a constant velocity stimulus equated for mean velocity and size. Acceleration was adjusted according to the QUEST staircase procedure and thresholds, defined as the acceleration discriminated at the 82% correct-level, were obtained for positive and negative acceleration, horizontal and vertical motion, two base velocities, and two trajectory sizes. Consistent with previous findings, thresholds, if expressed according to proportion of velocity change relative to the base velocity were relatively constant across base velocities and sizes. Critically, we show that absolute acceleration thresholds varied in a manner analogous to Weber's law. We show also that thresholds were better for motions along the horizontal axis than the vertical axis, but only at the high base velocity and smaller size. Furthermore, acceleration sensitivity was not affected by the sign of acceleration or stimulus direction within the principle axes. These findings are discussed in the context of predictions of acceleration sensitivity from previous data for the perception of animate and inanimate motions.
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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 | 0.005 |
| 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 | 0.001 |
| 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".