Post-auricular orientation of auditory attention in sound field versus virtual sound space
Bibliographic record
Abstract
The post-auricular muscle in many species’ changes orientation of external ears to improve hearing for biologically relevant sounds. The muscle exists in humans but cannot similarly change direction of their ears. Objectives focused on measuring activity of muscle during speech-in-noise task where orientations of speaker and noise were controlled experimentally to determine how signal-to-noise varies as function of presentation mode and azimuth (target speech and noise co-localized, 45°, or spatially separated, 135° and 45°, respectively). It was hypothesized that activity would be elicited in same proportion of subjects when evoked via earphones compared to speakers; there would be no significant differences in magnitude between conditions; maximum engagement would be observed with speech 135°, noise 45°. Activity was recorded with electrodes affixed around ears, outer canthi and neck, while listeners completed a spatialized listening test (locations of speaker/noise controlled experimentally). There was significant main effect of channel; significant interaction between presentation mode and channel; no significant differences between presentation modes for other muscles; no significant effect of azimuth. Engagement in virtual sound-space suggests that muscle activation occurs consequently of spatially directed attention, even when changes in pinna orientation are unlikely to have effect on sound heard.
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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 | 0.001 |
| 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".