An Investigation of the Potential to Influence Braking Behaviour Through Manipulation of Optical Looming Cues in a Simulated Driving Task
Bibliographic record
Abstract
This paper reports on an investigation of how manipulation of optical looming cues can influence braking behaviour, for automobile driving in a low-fidelity simulator. Twenty participants were instructed to follow a leading vehicle (LV) and appropriately respond to braking events of the LV, which occurred randomly and at different deceleration rates. During some braking events, the size of the LV was manipulated in different ways, without subjects being aware, in a manner concordant with the optical expansion that would have been observed during braking if the LV had been displaced to be closer or further away. Results showed that subjects braked sooner when confronting an expanding LV and later for a contracting LV, relative to a constant-size LV, to an extent corresponding to the magnitude of the manipulation. The experiment supports the theory that drivers use TTC information derived from optic looming to control braking.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".