Participates in 7 visits over a six-month duration. Subjects will be given several tests that require them to listen to sounds presented to their cochlear implant and answer questions about those sounds.

Participates in 1 visit lasting 3 hours. Will be given several tests that require you to listen to sounds and answer questions about those sounds. The sounds will be distorted in ways that approximate how a cochlear implant sounds.

The computer model will use standard Cochlear Implant settings to evaluate psychophysical and speech understanding abilities and sound quality with standard CI settings and then use these measures to build subject-specific models and determine experimental active electrode conditions to be tested: model-optimized, and two best-electrodes settings (2 types); 3) evaluate speech understanding and sound quality measures after using experimental settings regularly for 1.5 months; and 4) compare performance across experimental and clinical conditions.

The analysis will include four levels of repeated measures with active CI electrode conditions. These levels include the clinical baseline condition (all available electrodes active) and three experimental conditions (model-optimized, best-electrodes, and restricted best-electrodes).

Vowel and consonant identification testing will include 9 vowels in /hVd/ context and 16 consonants in /aCa/ context presented in quiet. Each phoneme will be presented 15 times. Vowel and consonant testing will be done during initial speech testing using clinical CI settings. Subject confusion matrices (i.e. number of correct responses and specific confusions) will serve as the basis for model construction.

Word recognition testing will consist of a Consonant-Nucleus-Consonant (CNC) monosyllabic word test that includes 30 lists of 50 words each presented in quiet. Participants will complete three CNC word lists per testing session, and word lists will be blocked across testing sessions so no word list is repeated. Word recognition scores will be used to evaluate performance with clinical and experimental CI settings. Word scores with clinical settings will also be used for model development.

Sentence recognition testing will consist of AzBio sentences tested in +10 decibel (dB) signal-to-noise ratio (SNR) multi-talker babble. The test includes 33 lists of 20 sentences with approximately 150 words per list. Participants will complete two lists per testing session, and lists will be blocked across testing sessions so no lists are repeated. Sentence recognition scores in noise will help evaluate generalization of performance with experimental settings to other speech tasks and in other listening conditions. Speech stimuli will be presented at 65 dB sound pressure level (SPL) (C-weighted) in a sound attenuated room as measured from CI user's microphone.

Electrode discrimination will be measured as a JND relative to every active CI electrode in both basal and apical directions. Individual electrodes will be stimulated with 500ms pulse trains at comfortable loudness-balanced levels. JND for each reference electrode will be measured adaptively using a 4-interval 2-alternative forced-choice (2AFC) 2-down/1-up procedure. Electrode discrimination Just Noticeable Differences (JNDs) will serve as inputs to build the proposed subject-specific models.