Bibliographic record
Abstract
To assist the human operator, modern auditory interfaces increasingly rely on sound spatialisation to display auditory information and warning signals. However, we often operate in environments that apply vibrations to the whole body, e.g. when driving a vehicle. Here, we report three experiments investigating the effect of sinusoidal vibrations along the vertical axis on spatial hearing. The first was a free-field, narrow-band noise localisation experiment with 5- Hz vibration at 0.88 ms(-2). The other experiments used headphone-based sound lateralisation tasks. Experiment 2 investigated the effect of vibration frequency (4 vs. 8 Hz) at two different magnitudes (0.83 vs. 1.65 ms(-2)) on a left-right discrimination one-interval forced-choice task. Experiment 3 assessed the effect on a two-interval forced-choice location discrimination task with respect to the central and two peripheral reference locations. In spite of the broad range of methods, none of the experiments show a reliable effect of whole-body vibrations on localisation performance. PRACTITIONER SUMMARY: We report three experiments that used both free-field localisation and headphone lateralisation tasks to assess their sensitivity to whole-body vibrations at low frequencies. None of the experiments show a reliable effect of either frequency or magnitude of whole-body vibrations on localisation performance.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".