Resolution of multiple talkers in a “cocktail party” depends on head movements
Why this work is in the frame
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Bibliographic record
Abstract
How we resolve and select single voices out of a complex auditory scene is a foundational problem in cognitive neuroscience and cognitive psychology: this is known as the cocktail party problem. In discovering the computational mechanisms by which we resolve spatially distinct sounds, we find that binaural sound localization cues can lead to a front-back ambiguity. Head movements may be critical in resolving these ambiguities. We developed a simple listening task in which participants count the number of distinct voices they hear in a front-field complex auditory scene – with and without head rotations. We found that there was an increase in performance for those listeners using head rotations. We further tested the front-back ambiguities by using the same listening task with talkers in both front and back-fields. This novel listening task allowed us to further test mechanisms of auditory scene analysis that determine the resolution of spatial auditory attention. * Indicates faculty mentor
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it