Performance on a spatially selective auditory attention listening task for sources in the horizontal or coronal planes
Bibliographic record
Abstract
Listeners can use spatially selective auditory attention (SSAA) to focus on one talker in a complex acoustic scene. SSAA has been primarily investigated with targets and distractors arrayed in the frontal horizontal plane. In this study we compared normally hearing human listeners' performance on static and dynamic SSAA tasks in a frontal target/distractor configuration to performance with sources arrayed in either the rear horizontal plane, the overhead coronal plane, or the “underhead” coronal plane, all of which provide similar binaural difference cues with which to differentiate target and distractor locations. To achieve the coronal plane configurations, the listener's head was tipped forward to align the normally vertical axis with the horizontal plane. In the SSAA task, listeners attempted to report a 4-digit sequence of spoken digits from the target location while ignoring two simultaneous equal-intensity sequences spoken by the same talker presented from flanking loudspeakers separated by ±22.5° from the target. Listeners either held their head still (static conditions) with the front (horizontal configurations) or top (coronal configurations) of the head oriented toward 0 azimuth, or were oscillated passively at ∼0.14 Hz with an amplitude of ±45 degrees about the vertical axis (dynamic conditions).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".