Impact of Auditory-Motor Musical Training on Melodic Pattern Recognition in Cochlear Implant Users
Bibliographic record
Abstract
OBJECTIVE: Cochlear implant (CI) users struggle with tasks of pitch-based prosody perception. Pitch pattern recognition is vital for both music comprehension and understanding the prosody of speech, which signals emotion and intent. Research in normal-hearing individuals shows that auditory-motor training, in which participants produce the auditory pattern they are learning, is more effective than passive auditory training. We investigated whether auditory-motor training of CI users improves complex sound perception, such as vocal emotion recognition and pitch pattern recognition, compared with purely auditory training. STUDY DESIGN: Prospective cohort study. SETTING: Tertiary academic center. PATIENTS: Fifteen postlingually deafened adults with CIs. INTERVENTION(S): Participants were divided into 3 one-month training groups: auditory-motor (intervention), auditory-only (active control), and no training (control). Auditory-motor training was conducted with the "Contours" software program and auditory-only training was completed with the "AngelSound" software program. MAIN OUTCOME MEASURE: Pre and posttest examinations included tests of speech perception (consonant-nucleus-consonant, hearing-in-noise test sentence recognition), speech prosody perception, pitch discrimination, and melodic contour identification. RESULTS: Participants in the auditory-motor training group performed better than those in the auditory-only and no-training (p < 0.05) for the melodic contour identification task. No significant training effect was noted on tasks of speech perception, speech prosody perception, or pitch discrimination. CONCLUSIONS: These data suggest that short-term auditory-motor music training of CI users impacts pitch pattern recognition. This study offers approaches for enriching the world of complex sound in the CI user.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".