From fragments to the whole: a comparison between cochlear implant users and normal-hearing listeners in music perception and enjoyment.
Bibliographic record
Abstract
BACKGROUND: Cochlear implants (CIs) allow many profoundly deaf individuals to regain speech understanding. However, the ability to understand speech does not necessarily guarantee music enjoyment. Enabling a CI user to recover the ability to perceive and enjoy the complexity of music remains a challenge determined by many factors. OBJECTIVES: (1) To construct a novel, attention-based, diagnostic software tool (Music EAR) for the assessment of music enjoyment and perception and (2) to compare the results among three listener groups. METHODS: Thirty-six subjects completed the Music EAR assessment tool: 12 normal-hearing musicians (NHMs), 12 normal-hearing nonmusicians (NHnMs), and 12 CI listeners. Subjects were required to (1) rate enjoyment of musical excerpts at three complexity levels; (2) differentiate five instrumental timbres; (3) recognize pitch pattern variation; and (4) identify target musical patterns embedded holistically in a melody. RESULTS: Enjoyment scores for CI users were comparable to those for NHMs and superior to those for NHnMs and revealed that implantees enjoyed classical music most. CI users performed significantly poorer in all categories of music perception compared to normal-hearing listeners. Overall CI user scores were lowest in those tasks requiring increased attention. Two high-performing subjects matched or outperformed NHnMs in pitch and timbre perception tasks. CONCLUSIONS: The Music EAR assessment tool provides a unique approach to the measurement of music perception and enjoyment in CI users. Together with auditory training evidence, the results provide considerable hope for further recovery of music appreciation through methodical rehabilitation.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".