Accurate estimation of the temporal dynamics of bouncing events.
Bibliographic record
Abstract
The sound of a bouncing object is rich in dynamic acoustical information: subsequent bounces are more tightly spaced in time, have lower energy, and tend to excite less strongly the high-frequency resonant modes of the bounced-upon object. Previous studies on bouncing events show that dynamic information is not used to perceive the properties of the bouncing object. We tested whether this is the case when listeners are asked to predict the dynamic behavior of a bouncing event. Stimuli were recorded by dropping one of four different balls from various heights onto a hard linoleum surface. After hearing two, three, four, or five bounces, participants pressed a button to estimate the temporal location of the next bounce. No performance feedback was given. Participants never heard the bounce whose temporal location they were estimating. Bounce-time estimates were very accurate (r = 0.96). Acoustic analyzes revealed a strong focus on timing information and a secondary reliance on energetic information. Spectral information appeared to have negligible effects. These findings demonstrate the extreme versatility of human listeners in using variable acoustic cues to determine the dynamic behavior of real-world objects.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".