Can Music Enhance Working Memory and Speech in Noise Perception in Cochlear Implant Users? Design Protocol for a Randomized Controlled Behavioral and Electrophysiological Study
Bibliographic record
Abstract
BACKGROUND: A cochlear implant (CI) enables deaf people to understand speech but due to technical restrictions, users face great limitations in noisy conditions. Music training has been shown to augment shared auditory and cognitive neural networks for processing speech and music and to improve auditory-motor coupling, which benefits speech perception in noisy listening conditions. These are promising prerequisites for studying multi-modal neurologic music training (NMT) for speech-in-noise (SIN) perception in adult cochlear implant (CI) users. Furthermore, a better understanding of the neurophysiological correlates when performing working memory (WM) and SIN tasks after multi-modal music training with CI users may provide clinicians with a better understanding of optimal rehabilitation. METHODS: Within 3 months, 81 post-lingual deafened adult CI recipients will undergo electrophysiological recordings and a four-week neurologic music therapy multi-modal training randomly assigned to one of three training focusses (pitch, rhythm, and timbre). Pre- and post-tests will analyze behavioral outcomes and apply a novel electrophysiological measurement approach that includes neural tracking to speech and alpha oscillation modulations to the sentence-final-word-identification-and-recall test (SWIR-EEG). Expected outcome: Short-term multi-modal music training will enhance WM and SIN performance in post-lingual deafened adult CI recipients and will be reflected in greater neural tracking and alpha oscillation modulations in prefrontal areas. Prospectively, outcomes could contribute to understanding the relationship between cognitive functioning and SIN besides the technical deficits of the CI. Targeted clinical application of music training for post-lingual deafened adult CI carriers to significantly improve SIN and positively impact the quality of life can be realized.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".