Objectives: 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. Design: 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 and Perceptually Robust English Sentence Test Open-set test-Foreign Accented English test). Measures of EF included verbal working memory (WM), spatial WM, controlled cognitive fluency, and inhibition concentration. Results: 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. Conclusions: 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. ACKNOWLEDGMENTS: The authors thank Shirley Henning and Bethany Colson for administering the speech, language, and neurocognitive tests and Allison Ditmars for coordinating the study. The authors also thank Luis Hernandez for his help and assistance with data analysis connected with the speech production measures. This research was funded by National Institutes of Health-National Institute on Deafness and Other Communication Disorders (NIH-NIDCD) R01DC015257 (to W.G.K. and D.B.P.) and NIH-NIDCD R01DC009581 (to D.B.P.). Portions of this article were presented at the American Cochlear Implant Alliance CI2018 Emerging Issues Symposium on March 8, 2018, in Washington DC, (by W.G.K.) and the Cognitive Neuroscience Society 25th Annual Meeting on March 27, 2018, in Boston, MA, (by G.N.L.S.). G.N.L.S. analyzed the data, wrote the first draft of the paper, and provided critical revisions to subsequent drafts. D.B.P. conceptualized the study, designed the experiments, and provided critical revisions to all drafts of the article. W.G.K. conceptualized the study, designed the experiments, analyzed the data, wrote sections of the article, and provided critical revisions to the article. G.N.L.S., D.B.P., and W.G.K. gave final approval of the paper before submission. W.G.K. is a paid consultant for Shire Pharmaceuticals and the Indiana Hemophilia and Thrombosis Center. Address for correspondence: Gretchen N.L. Smith, Cognitive Neuroscience Laboratory, Shirley Ryan AbilityLab, 355 East Erie Street, Chicago, IL 60611, USA. E-mail: gsmith02@sralab.org Received May 14, 2018; accepted November 6, 2018. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
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