Effortful Listening: The Processing of Degraded Speech Depends Critically on Attention
Bibliographic record
Abstract
The conditions of everyday life are such that people often hear speech that has been degraded (e.g., by background noise or electronic transmission) or when they are distracted by other tasks. However, it remains unclear what role attention plays in processing speech that is difficult to understand. In the current study, we used functional magnetic resonance imaging to assess the degree to which spoken sentences were processed under distraction, and whether this depended on the acoustic quality (intelligibility) of the speech. On every trial, adult human participants attended to one of three simultaneously presented stimuli: a sentence (at one of four acoustic clarity levels), an auditory distracter, or a visual distracter. A postscan recognition test showed that clear speech was processed even when not attended, but that attention greatly enhanced the processing of degraded speech. Furthermore, speech-sensitive cortex could be parcellated according to how speech-evoked responses were modulated by attention. Responses in auditory cortex and areas along the superior temporal sulcus (STS) took the same form regardless of attention, although responses to distorted speech in portions of both posterior and anterior STS were enhanced under directed attention. In contrast, frontal regions, including left inferior frontal gyrus, were only engaged when listeners were attending to speech and these regions exhibited elevated responses to degraded, compared with clear, speech. We suggest this response is a neural marker of effortful listening. Together, our results suggest that attention enhances the processing of degraded speech by engaging higher-order mechanisms that modulate perceptual auditory processing.
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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".