Department of Otolaryngology-Head and Neck Surgery, The Ohio State University, Columbus, Ohio.
Otol Neurotol. 2018 Dec;39(10):e956-e963. doi: 10.1097/MAO.0000000000001998.
Significant variability in speech recognition persists among postlingually deafened adults with cochlear implants (CIs). We hypothesize that scores of nonverbal reasoning predict sentence recognition in adult CI users.
Cognitive functions contribute to speech recognition outcomes in adults with hearing loss. These functions may be particularly important for CI users who must interpret highly degraded speech signals through their devices. This study used a visual measure of reasoning (the ability to solve novel problems), the Raven's Progressive Matrices (RPM), to predict sentence recognition in CI users.
Participants were 39 postlingually deafened adults with CIs and 43 age-matched normal-hearing (NH) controls. CI users were assessed for recognition of words in sentences in quiet, and NH controls listened to eight-channel vocoded versions to simulate the degraded signal delivered by a CI. A computerized visual task of the RPM, requiring participants to identify the correct missing piece in a 3×3 matrix of geometric designs, was also performed. Particular items from the RPM were examined for their associations with sentence recognition abilities, and a subset of items on the RPM was tested for the ability to predict degraded sentence recognition in the NH controls.
The overall number of items answered correctly on the 48-item RPM significantly correlated with sentence recognition in CI users (r = 0.35-0.47) and NH controls (r = 0.36-0.57). An abbreviated 12-item version of the RPM was created and performance also correlated with sentence recognition in CI users (r = 0.40-0.48) and NH controls (r = 0.49-0.56).
Nonverbal reasoning skills correlated with sentence recognition in both CI and NH subjects. Our findings provide further converging evidence that cognitive factors contribute to speech processing by adult CI users and can help explain variability in outcomes. Our abbreviated version of the RPM may serve as a clinically meaningful assessment for predicting sentence recognition outcomes in CI users.
人工耳蜗植入（CI）后失聪的成年人的言语识别存在显著差异。我们假设非言语推理分数可以预测成人 CI 用户的句子识别。
认知功能对听力损失成年人的言语识别结果有贡献。这些功能对于必须通过设备解释高度退化的语音信号的 CI 用户可能尤为重要。本研究使用一种视觉推理测量（解决新问题的能力），即瑞文渐进矩阵（RPM），来预测 CI 用户的句子识别。
参与者为 39 名后天失聪的成人 CI 用户和 43 名年龄匹配的正常听力（NH）对照组。CI 用户在安静环境中接受句子中单词识别的评估，NH 对照组则聆听八通道语音编码版本，以模拟 CI 提供的退化信号。还进行了 RPM 的计算机视觉任务，要求参与者识别 3×3 矩阵的几何设计中正确的缺失部分。RPM 的特定项目与句子识别能力相关，RPM 的一个子集项目用于测试 NH 对照组中预测退化句子识别的能力。
48 项 RPM 中答对的总体项目数与 CI 用户（r=0.35-0.47）和 NH 对照组（r=0.36-0.57）的句子识别显著相关。创建了 RPM 的一个 12 项缩写版本，其表现也与 CI 用户（r=0.40-0.48）和 NH 对照组（r=0.49-0.56）的句子识别相关。
非言语推理技能与 CI 和 NH 受试者的句子识别相关。我们的发现进一步证明了认知因素对成人 CI 用户言语处理的贡献，并可以帮助解释结果的变异性。我们 RPM 的缩写版本可能成为预测 CI 用户句子识别结果的有意义的临床评估。
