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Updated: Sep 23, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
Published on: March 24, 2023
Contribution of Verbal Learning & Memory and Spectro-Temporal Discrimination to Speech Recognition in Cochlear
Michael S Harris1,2, Benjamin L Hamel3, Kristin Wichert4
1Department of Otolaryngology & Communication Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Verbal learning and memory (VL&M) abilities significantly impact cochlear implant (CI) users' speech recognition. These cognitive skills offer additional benefits beyond basic sound processing for improved auditory outcomes.
Area of Science:
- Audiology and Speech-Language Pathology
- Neuroscience
- Cognitive Science
Background:
- Cochlear implant (CI) effectiveness varies significantly among users.
- Verbal learning and memory (VL&M) are increasingly recognized as potential factors influencing speech recognition in CI users.
Purpose of the Study:
- To investigate the relationship between visual measures of VL&M and speech recognition in adult CI users.
- To determine if VL&M contributes to speech recognition outcomes independently of spectro-temporal processing abilities.
Main Methods:
- A cross-sectional study involving 30 adult postlingually deaf CI users.
- Utilized a visual version of the California Verbal Learning Test-Second Edition (v-CVLT-II) for VL&M assessment.
- Employed the Spectral-Temporally Modulated Ripple Test (SMRT) for spectro-temporal processing and conducted speech recognition tasks.
Main Results:
- Significant correlations were found between specific v-CVLT-II measures and speech recognition outcomes.
- Spectro-temporal processing (SMRT performance) was correlated with all speech recognition measures.
- Hierarchical regression revealed that VL&M independently contributed to speech recognition beyond spectro-temporal discrimination.
Conclusions:
- Both spectro-temporal discrimination and VL&M are associated with speech recognition performance in CI users.
- VL&M provides an independent contribution to speech recognition, even after accounting for spectro-temporal processing abilities.
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