The perception of concurrent sound objects in harmonic complexes impairs gap detection.
Bibliographic record
Abstract
Since the introduction of the concept of auditory scene analysis, there has been a paucity of work focusing on the theoretical explanation of how attention is allocated within a complex auditory scene. Here we examined signal detection in situations that promote either the fusion of tonal elements into a single sound object or the segregation of a mistuned element (i.e., harmonic) that "popped out" as a separate individuated auditory object and yielded the perception of concurrent sound objects. On each trial, participants indicated whether the incoming complex sound contained a brief gap or not. The gap (i.e., signal) was always inserted in the middle of one of the tonal elements. Our findings were consistent with an object-based account in which perception of two simultaneous auditory objects interfered with signal detection. This effect was observed for a wide range of gap durations and was greater when the mistuned harmonic was perceived as a separate object. These results suggest that attention may be initially shared among concurrent sound objects thereby reducing listeners' ability to process acoustic details belonging to a particular sound object. These findings provide new theoretical insight for our understanding of auditory attention and auditory scene analysis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".