Audiovisual Speech Recognition in Adult Cochlear Implant Candidates: Relations with Cognitive-Linguistic Abilities

成人人工耳蜗植入候选者的视听语音识别:与认知语言能力的关系

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Abstract

OBJECTIVE: To examine audiovisual (AV) sentence recognition performance in adults with moderate-to-profound sensorineural hearing loss who are cochlear implant (CI) candidates, and to identify the demographic, audiologic, and cognitive-linguistic abilities that explain individual differences in AV performance. STUDY DESIGN: Cross-sectional observational study. SETTING: Tertiary referral center. PATIENTS: Twenty-four postlingually deafened adults (mean age = 70.3 years, SD = 11.2) meeting clinical CI candidacy criteria in both ears, with no known history of cognitive or neurological disorders. INTERVENTIONS: Participants completed the Multimodal Lexical Sentence Test (MLST) for Adults for AV sentence recognition and a battery of cognitive-linguistic tasks measuring short-term memory, working memory, inhibition-concentration, nonverbal reasoning, and speed of lexical/phonological access. MAIN OUTCOME MEASURES: AV sentence recognition accuracy on the MLST. RESULTS: MLST performance varied broadly across participants (range = 11%-98%, mean = 67.6, SD = 29.7) and was strongly correlated with auditory-only sentence recognition abilities (rho = 0.85, P < 0.001). Otherwise, AV sentence recognition was not significantly correlated with demographic or additional audiological variables. Short-term memory capacity, measured using visual Forward Digit Span, significantly explained MLST performance (partial rho = 0.44, P = 0.02), after controlling for auditory-only sentence recognition in quiet. Other cognitive-linguistic measures were not significantly associated with MLST scores. CONCLUSIONS: AV sentence recognition in adult CI candidates is partially explained by short-term memory capacity. Ecologically valid AV assessments may help identify cognitively mediated barriers to real-world communication that are not captured using conventional auditory-only speech recognition tests.

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