Tau and Depth Cues Influence the Position of Braking in Virtual Environment
Bibliographic record
Abstract
To investigate whether both the sources of visual information tau cue and depth cue were utilized to guide braking, in the present study we used the virtual reality technology which could decouple the dilation rate of visual object and depth cue. Participants were instructed to park a car to an obstacle as closely as possible and avoid making collision. Results showed: (1) on the condition of same initial distance from car to an obstacle, participants tended to brake in advance on tau speed-up condition, which caused longer distance from braking to an obstacle than on control condition(tau and depth cue couple);While participants tended to postpone braking on tau speed-down condition, which caused the shorter distance from braking to an obstacle than on control condition; (2) On tau speed-up and tau speed-down condition, participants automatically fine-tuned the actual braking position to avoid making collision. These results suggested that both tau cue and depth cue were processed and utilized to direct the behavior of braking by our visual perceptual system.
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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 | 0.003 |
| 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) | 0.002 | 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".