High-Variability Sentence Recognition in Long-Term Cochlear Implant Users: Associations With Rapid Phonological Coding and Executive Functioning.
High-Variability Sentence Recognition in Long-Term Cochlear Implant Users: Associations With Rapid Phonological Coding and Executive Functioning.
复制标题
DOI：
10.1097/aud.0000000000000691
复制
发表时间：
2019
期刊：
影响因子：
3.7
通讯作者：
Kronenberger WG
中科院分区：
文献类型：
--
作者：
Smith GNL;Pisoni DB;Kronenberger WG
本研究的目的是确定长期人工耳蜗 (CI) 用户在执行感知上具有挑战性的高可变性句子识别任务时，是否会比听力正常 (NH) 的同龄人在快速语音编码技能方面表现出更大的变异性，并且更依赖缓慢费力的补偿性执行功能 (EF) 技能。我们测试了以下三个假设：首先，即使在对 CI 用户在传统的低变异性句子识别测试中较差的句子识别性能进行调整之后，CI 用户在涉及高说话者和方言变异性的句子识别测试中也会表现出比 NH 对照组更低的分数。其次，与 NH 用户相比，CI 用户的快速自动快速语音编码技能的变异性与高变异性句子识别任务的表现关联性更强。第三，与 NH 用户相比，补偿性 EF 策略与 CI 用户在高变异性句子识别任务中的表现关系更为密切。两组 9 至 29 岁的儿童、青少年和年轻人参与了这项横断面研究：49 名长期 CI 使用者（≥ 7 岁）和 56 名 NH 对照者。所有参与者都接受了快速语音编码（儿童非单词重复测试）、传统句子识别（哈佛句子识别测试）和两种新颖的高可变性句子识别测试的测试，这些测试改变了语音的索引属性[感知稳健英语句子测试开放集测试（PRESTO）和PRESTO外国口音英语测试]。 EF 的测量包括言语工作记忆 (WM)、空间 WM、控制认知流畅性和抑制浓度。即使在统计控制传统句子识别技能后，CI 用户在高变异性句子识别的两项测试中得分仍低于 NH 用户。即使在统计控制基本句子感知技能后，CI 样本的快速语音编码和高变异性句子识别分数之间的相关性也比 NH 样本更强。散点图揭示了 CI 用户和 NH 用户的快速语音编码技能与高变异性句子识别性能之间关系的不同范围和斜率。尽管在使用保守的 Bonferroni 型校正后，在 CI 或 NH 样本中没有发现 EF 策略和句子识别之间存在统计上显着的相关性，但 CI 样本中言语 WM 和句子识别之间相关性的中到高效应大小表明需要进一步研究这种关系。这些发现为神经认知模型提供了一致的支持，提出了两种语音语言处理通道：一个尽可能占主导地位的快速自动通道和一个在快速自动通道无法完全管理的感知挑战性语音处理任务期间激活的补偿性缓慢费力处理通道（轻松语言理解、理解努力听力的框架和听觉神经认知模型）。即使在控制简单句子识别后，CI 用户在高变异性句子识别测量方面的表现也明显低于 NH 用户。非单词重复分数显示 CI 和 NH 样本之间几乎没有重叠，非单词重复分数和高变异性句子识别之间的相关性与 CI 样本中高变异性句子识别比 NH 样本更依赖于快速自动语音编码的参与一致。建议进一步研究 CI 用户的口头 WM-句子识别关系。对快速自动语音处理和慢速 EF 技能的评估可以更好地理解临床环境中 CI 用户的语音感知结果。
The objective of the present study was to determine whether long-term cochlear implant (CI) users would show greater variability in rapid phonological coding skills and greater reliance on slow-effortful compensatory executive functioning (EF) skills than normal hearing (NH) peers on perceptually challenging high-variability sentence recognition tasks. We tested the following three hypotheses: First, CI users would show lower scores on sentence recognition tests involving high speaker and dialect variability than NH controls, even after adjusting for poorer sentence recognition performance by CI users on a conventional low-variability sentence recognition test. Second, variability in fast-automatic rapid phonological coding skills would be more strongly associated with performance on high-variability sentence recognition tasks for CI users than NH peers. Third, compensatory EF strategies would be more strongly associated with performance on high-variability sentence recognition tasks for CI users than NH peers. Two groups of children, adolescents, and young adults aged 9 to 29 years participated in this cross-sectional study: 49 long-term CI users (≥ 7 years) and 56 NH controls. All participants were tested on measures of rapid phonological coding (Children’s Test of Nonword Repetition), conventional sentence recognition (Harvard Sentence Recognition Test), and two novel high-variability sentence recognition tests that varied the indexical attributes of speech [Perceptually Robust English Sentence Test Open-set test (PRESTO) and PRESTO Foreign-Accented English test]. Measures of EF included verbal working memory (WM), spatial WM, controlled cognitive fluency, and inhibition-concentration. CI users scored lower than NH peers on both tests of high-variability sentence recognition even after conventional sentence recognition skills were statistically controlled. Correlations between rapid phonological coding and high-variability sentence recognition scores were stronger for the CI sample than for the NH sample even after basic sentence perception skills were statistically controlled. Scatterplots revealed different ranges and slopes for the relationship between rapid phonological coding skills and high-variability sentence recognition performance in CI users and NH peers. Although no statistically significant correlations between EF strategies and sentence recognition were found in the CI or NH sample after use of a conservative Bonferroni-type correction, medium to high effect sizes for correlations between verbal WM and sentence recognition in the CI sample suggest that further investigation of this relationship is needed. These findings provide converging support for neurocognitive models that propose two channels for speech-language processing: a fast-automatic channel that predominates whenever possible and a compensatory slow-effortful processing channel that is activated during perceptually-challenging speech processing tasks that are not fully managed by the fast-automatic channel (Ease of Language Understanding, Framework for Understanding Effortful Listening, and Auditory Neurocognitive Model). CI users showed significantly poorer performance on measures of high-variability sentence recognition than NH peers, even after simple sentence recognition was controlled. Nonword repetition scores showed almost no overlap between CI and NH samples, and correlations between nonword repetition scores and high-variability sentence recognition were consistent with greater reliance on engagement of fast-automatic phonological coding for high-variability sentence recognition in the CI sample than in the NH sample. Further investigation of the verbal WM-sentence recognition relationship in CI users is recommended. Assessment of fast-automatic phonological processing and slow-effortful EF skills may provide a better understanding of speech perception outcomes in CI users in the clinical setting.