Studies of multisensory integration in sound localization and updating of auditory spatial attention during head motion
Bibliographic record
Abstract
In daily life, our heads are in continual motion, but much of our knowledge of spatial hearing is based on experimental paradigms in which this behavior is discouraged or prevented. Head motion can benefit spatial hearing though the creation of dynamic acoustical cues, but also creates challenges such as the need to update head-centered spatial representations or the locus of spatial auditory attention. To utilize acoustic information generated by, or to compensate for, head movements, the auditory system must integrate self-motion information provided by other sensory systems. This presentation will review and contextualize a series of studies from the author's laboratory focused on the psychophysics of dynamic sound localization and the weighting of sensory information from vestibular, proprioceptive, and visual modalities in dynamic localization and maintenance of spatially selective auditory attention during head rotation. Notable findings include a velocity-independent ∼100-ms minimum stimulus duration for disambiguation of front/rear location in dynamic localization and the apparent dominance of vestibular information in the interpretation of dynamic localization cues and in attentional updating. The approach taken provides a step towards understanding the effects of naturalistic behavior on spatial hearing while maintaining significant experimental control and repeatability of stimuli and head movements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".