Search

When listening to speech under adverse conditions, listeners compensate using neurocognitive resources. A clinically relevant form of adverse listening is listening through a cochlear implant (CI), which provides a spectrally degraded signal. CI listening is often simulated through noise-vocoding. This study investigated the neurocognitive mechanisms supporting recognition of spectrally degraded speech in adult CI users and normal-hearing (NH) peers listening to noise-vocoded speech, with the hypothesis that an overlapping set of neurocognitive functions would contribute to speech recognition in both groups. Ninety-seven adults with either a CI (54 CI individuals, mean age 66.6 years, range 45–87 years) or age-normal hearing (43 NH individuals, mean age 66.8 years, range 50–81 years) participated. Listeners heard materials varying in linguistic complexity consisting of isolated words, meaningful sentences, anomalous sentences, high-variability sentences, and audiovisually (AV) presented sentences. Participants were also tested for vocabulary knowledge, nonverbal reasoning, working memory capacity, inhibition-concentration, and speed of lexical and phonological access. Linear regression analyses with robust standard errors were performed for speech recognition tasks on neurocognitive functions. Nonverbal reasoning contributed to meaningful sentence recognition in NH peers and anomalous sentence recognition in CI users. Speed of lexical access contributed to performance on most speech tasks for CI users but not for NH peers. Finally, inhibition-concentration and vocabulary knowledge contributed to AV sentence recognition in NH listeners alone. Findings suggest that the complexity of speech materials may determine the particular contributions of neurocognitive skills, and that NH processing of noise-vocoded speech may not represent how CI listeners process speech. [ABSTRACT FROM AUTHOR]

Purpose: Although the existing literature has explored the link between cognitive functioning and speech recognition in noise, the specific role of fluid intelligence still needs to be studied. Given the established association between working memory capacity (WMC) and fluid intelligence and the predictive power of WMC for speech recognition in noise, we aimed to elucidate the mediating role of fluid intelligence. Method: We used data from the n200 study, a longitudinal investigation into aging, hearing ability, and cognitive functioning. We analyzed two age-matched samples: participants with hearing aids and a group with normal hearing. WMC was assessed using the Reading Span task, and fluid intelligence was measured with Raven's Progressive Matrices. Speech recognition in noise was evaluated using Hagerman sentences presented to target 80% speech-reception thresholds in four-talker babble. Data were analyzed using mediation analysis to examine fluid intelligence as a mediator between WMC and speech recognition in noise. Results: We found a partial mediating effect of fluid intelligence on the relationship between WMC and speech recognition in noise, and that hearing status did not moderate this effect. In other words, WMC and fluid intelligence were related, and fluid intelligence partially explained the influence of WMC on speech recognition in noise. Conclusions: This study shows the importance of fluid intelligence in speech recognition in noise, regardless of hearing status. Future research should use other advanced statistical techniques and explore various speech recognition tests and background maskers to deepen our understanding of the interplay between WMC and fluid intelligence in speech recognition. [ABSTRACT FROM AUTHOR]