Effects of Attention on Neuroelectric Correlates of Auditory Stream Segregation
Bibliographic record
Abstract
A general assumption underlying auditory scene analysis is that the initial grouping of acoustic elements is independent of attention. The effects of attention on auditory stream segregation were investigated by recording event-related potentials (ERPs) while participants either attended to sound stimuli and indicated whether they heard one or two streams or watched a muted movie. The stimuli were pure-tone ABA--patterns that repeated for 10.8 sec with a stimulus onset asynchrony between A and B tones of 100 msec in which the A tone was fixed at 500 Hz, the B tone could be 500, 625, 750, or 1000 Hz, and--was a silence. In both listening conditions, an enhancement of the auditory-evoked response (P1-N1-P2 and N1c) to the B tone varied with Deltaf and correlated with perception of streaming. The ERP from 150 to 250 msec after the beginning of the repeating ABA- patterns became more positive during the course of the trial and was diminished when participants ignored the tones, consistent with behavioral studies indicating that streaming takes several seconds to build up. The N1c enhancement and the buildup over time were larger at right than left temporal electrodes, suggesting a right-hemisphere dominance for stream segregation. Sources in Heschl's gyrus accounted for the ERP modulations related to Deltaf-based segregation and buildup. These findings provide evidence for two cortical mechanisms of streaming: automatic segregation of sounds and attention-dependent buildup process that integrates successive tones within streams over several seconds.
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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) | 0.000 | 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) | 0.002 | 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".