Visual processing of the impending collision of a looming object: Time to collision revisited
Bibliographic record
Abstract
As an object approaches an observer's eye, the optical variable tau, defined as the inverse relative expansion rate of the object's image on the retina (D. N. Lee, 1976), approximates the time to collision (TTC). Many studies have provided support that human observers use TTC, but evidence for the exclusive use of TTC generated by tau remains inconclusive. In the present study, observers were presented with a visual display of two sequentially approaching objects and asked to compare their TTCs at the moment these objects vanished. Upon dissociating several variables that may have potentially contributed to TTC perception, we found that observers were most sensitive to TTC information when completing the task and less sensitive to non-time variables, such as those that specified distance to collision, speed, and object size. Moreover, when we manipulated presented variables to provide conflicting TTC information, TTC specified by tau was weighted much more than TTC derived from distance and speed. In conclusion, our results suggested that even in the presence of other monocular sources of information, observers still had a greater tendency to specifically use optical tau when making relative TTC judgments.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".