Bibliographic record
Abstract
Low- and high-pitched sounds are perceptually associated with low and high visuospatial elevations, respectively. The spatial properties of this association are not well understood. Here we report two experiments that investigated whether low and high tones can be used as spatial cues to upright for self-orientation and identified the spatial frame(s) of reference used in perceptually binding auditory pitch to visuospatial 'up' and 'down'. In experiment 1, participants' perceptual upright (PU) was measured while lying on their right side with and without high- and low-pitched sounds played through speakers above their left ear and below their right ear. The sounds were ineffective in moving the perceived upright from a direction intermediate between the body and gravity towards the direction indicated by the sounds. In experiment 2, we measured the biasing effects of ascending and descending tones played through headphones on ambiguous vertical or horizontal visual motion created by combining gratings drifting in opposite directions while participants either sat upright or laid on their right side. Ascending and descending tones biased the interpretation of ambiguous motion along both the gravitational vertical and the long-axis of the body with the strongest effect along the body axis. The combination of these two effects showed that axis of maximum effect of sound corresponded approximately to the direction of the perceptual upright, compatible with the idea that 'high' and 'low' sounds are defined along this axis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".