How we perceive the trajectory of an approaching object
Bibliographic record
Abstract
Various equations that describe how observers could recover the trajectory of an approaching object have been put forward. Many are relatively complex formulations that recover the veridical trajectory by scaling retinal cues, such as looming and changing disparity. However, these equations do not seem to describe human perception as observers typically misjudge trajectory angles. Thus, we examine whether a simpler formulation--one that does not predict veridical judgments-may better explain performance. We test the hypothesis that perceived trajectory is based on a speed ratio: the ratio of lateral angular speed to the sum of looming and changing disparity signals. To discriminate between this and alternative proposals, we examined the effect of object size on trajectory perception: The speed ratio hypothesis predicts that perceived trajectory will become less eccentric with increasing object size, while the alternatives predict that perceived trajectory will be independent of object size. Observers performed a trajectory judgment task in which they compared the trajectory direction of two approaching objects, of the same or different size, seen in separate intervals. We estimated perceptually parallel trajectories from their responses. In Experiment 1, objects differed in horizontal and vertical size, and in Experiment 2, they differed only in vertical size. In both experiments, observers' data showed a clear effect of object size and were close to predictions of the speed ratio hypothesis. We conclude that the alternate proposals we tested were not supported and that the speed ratio account is a sufficient account of the data.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".