Poor early cortical differentiation of speech predicts perceptual difficulties of severely hearing-impaired listeners in multi-talker environments
Bibliographic record
Abstract
Hearing impairment disrupts processes of selective attention that help listeners attend to one sound source over competing sounds in the environment. Hearing prostheses (hearing aids and cochlear implants, CIs), do not fully remedy these issues. In normal hearing, mechanisms of selective attention arise through the facilitation and suppression of neural activity that represents sound sources. However, it is unclear how hearing impairment affects these neural processes, which is key to understanding why listening difficulty remains. Here, severely-impaired listeners treated with a CI, and age-matched normal-hearing controls, attended to one of two identical but spatially separated talkers while multichannel EEG was recorded. Whereas neural representations of attended and ignored speech were differentiated at early (~ 150 ms) cortical processing stages in controls, differentiation of talker representations only occurred later (~250 ms) in CI users. CI users, but not controls, also showed evidence for spatial suppression of the ignored talker through lateralized alpha (7-14 Hz) oscillations. However, CI users' perceptual performance was only predicted by early-stage talker differentiation. We conclude that multi-talker listening difficulty remains for impaired listeners due to deficits in early-stage separation of cortical speech representations, despite neural evidence that they use spatial information to guide selective attention.
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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 | 0.001 | 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".