The use of accurate versus heuristic auditory and visual cues for time-to-collision judgments
Bibliographic record
Abstract
Estimating time-to-collision (TTC) is needed when pedestrians cross a road and a vehicle is approaching. An accurate auditory cue to judge the TTC of a sound source approaching at constant velocity is provided by the ratio of an object’s instantaneous sound intensity to its instantaneous rate of change in sound intensity (auditory τ). However, heuristic-based auditory cues might also be used, as suggested by research in vision. We presented auditory and visual simulations of approaching objects. Auditory and visual TTC cues were decorrelated by slightly shifting auditory TTC against visual TTC. This permitted the estimation of cue weights for auditory cues (e.g., auditory τ, final sound pressure level) and visual cues (e.g., visual τ, final optical size) in three sensory conditions: auditory-only, visual-only, and audiovisual. Results showed that TTC estimates in the auditory-only condition were primarily based on an auditory heuristic cue (final sound pressure level) rather than on auditory τ. In the visual-only condition, visual τ was more important than the heuristic cues. In the audiovisual condition, participants relied more strongly on visual cues than auditory cues. We discuss the need for more refined auditory simulations to gain further insight into the cue weighting in everyday situations.
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".