Updating of spatial selective auditory attention under-compensates for listener head movement
Bibliographic record
Abstract
Listeners can use spatial selective auditory attention (SSAA) to focus on one talker in a complex acoustic scene. In our dynamic SSAA task, listeners oscillate their heads ~±40 deg at ~0.5 Hz while five different simultaneous sequences of four spoken digits are presented from loudspeakers at 0 deg azimuth (the target) and ±22.5 deg and ±45 deg azimuth (four distractors); listeners report the target sequence heard. We have observed [ASA, Minneapolis 2018] that listeners are more likely to misreport distractors that are centrally located in head-centered coordinates at the moment of presentation, and that under static conditions, performance declines with increasing target eccentricity—suggesting either that listeners cannot rapidly update the focus of their SSAA to compensate head motion (“lag” ) or that they have difficulty directing SSAA eccentrically (“low gain” ). To differentiate these alternatives, spatio-temporal maps of SSAA, conditioned on head orientation and direction of motion during each digit, were derived by computing the percentage of reported digits corresponding to those emitted by each loudspeaker. The spatial pattern of errors depended primarily on head orientation and not on direction of motion, suggesting that in this task SSAA tends to remain centrally focused in head-centered coordinates (low gain) rather than lagging dynamic head position.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".