Attention-Driven Modulation of Auditory Cortex Activity during Selective Listening in a Multispeaker Setting
Bibliographic record
Abstract
Real-world listening settings often consist of multiple concurrent sound streams. To limit perceptual interference during selective listening, the auditory system segregates and filters the relevant sensory input. Previous work provided evidence that the auditory cortex is critically involved in this process and selectively gates attended input toward subsequent processing stages. We studied at which level of auditory cortex processing this filtering of attended information occurs using functional magnetic resonance imaging (fMRI) and a naturalistic selective listening task. Forty-five human listeners (of either sex) attended to one of two continuous speech streams, presented either concurrently or in isolation. Functional data were analyzed using an inter-subject analysis to assess stimulus-specific components of ongoing auditory cortex activity. Our results suggest that stimulus-related activity in the primary auditory cortex and the adjacent planum temporale are hardly affected by attention, whereas brain responses at higher stages of the auditory cortex processing hierarchy become progressively more selective for the attended input. Consistent with these findings, a complementary analysis of stimulus-driven functional connectivity further demonstrated that information on the to-be-ignored speech stream is shared between the primary auditory cortex and the planum temporale but largely fails to reach higher processing stages. Our findings suggest that the neural processing of ignored speech cannot be effectively suppressed at the level of early cortical processing of acoustic features but is gradually attenuated once the competing speech streams are fully segregated.
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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 | 0.001 |
| 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) | 0.001 | 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".