Perceptual grouping in the cocktail party: Contributions of voice-feature continuity
Bibliographic record
Abstract
Cocktail parties pose a difficult yet solvable problem for the auditory system. Previous work has shown that the cocktail-party problem is considerably easier when all sounds in the target stream are spoken by the same talker (the voice-continuity benefit). The present study investigated the contributions of two of the most salient voice features—glottal-pulse rate (GPR) and vocal-tract length (VTL)—to the voice-continuity benefit. Twenty young, normal-hearing listeners participated in two experiments. On each trial, listeners heard concurrent sequences of spoken digits from three different spatial locations and reported the digits coming from a target location. Critically, across conditions, GPR and VTL either remained constant or varied across target digits. Additionally, across experiments, the target location either remained constant (Experiment 1) or varied (Experiment 2) within a trial. In Experiment 1, listeners benefited from continuity in either voice feature, but VTL continuity was more helpful than GPR continuity. In Experiment 2, spatial discontinuity greatly hindered listeners' abilities to exploit continuity in GPR and VTL. The present results suggest that selective attention benefits from continuity in target voice features and that VTL and GPR play different roles for perceptual grouping and stream segregation in the cocktail party.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".