Attentional Capacity Limits Gap Detection during Concurrent Sound Segregation
Bibliographic record
Abstract
Detecting a brief silent interval (i.e., a gap) is more difficult when listeners perceive two concurrent sounds rather than one in a sound containing a mistuned harmonic in otherwise in-tune harmonics. This impairment in gap detection may reflect the interaction of low-level encoding or the division of attention between two sound objects, both of which could interfere with signal detection. To distinguish between these two alternatives, we compared ERPs during active and passive listening with complex harmonic tones that could include a gap, a mistuned harmonic, both features, or neither. During active listening, participants indicated whether they heard a gap irrespective of mistuning. During passive listening, participants watched a subtitled muted movie of their choice while the same sounds were presented. Gap detection was impaired when the complex sounds included a mistuned harmonic that popped out as a separate object. The ERP analysis revealed an early gap-related activity that was little affected by mistuning during the active or passive listening condition. However, during active listening, there was a marked decrease in the late positive wave that was thought to index attention and response-related processes. These results suggest that the limitation in detecting the gap is related to attentional processing, possibly divided attention induced by the concurrent sound objects, rather than deficits in preattentional sensory encoding.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".