HYPOTHESIS: 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. BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
HYPOTHESIS: 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. BACKGROUND: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. METHODS: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. RESULTS: 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). CONCLUSIONS: 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.
Authors: Douglas C Fitzpatrick; Adam P Campbell; Adam T Campbell; Baishakhi Choudhury; Margaret T Dillon; Margaret P Dillon; Mathieu Forgues; Craig A Buchman; Oliver F Adunka Journal: Otol Neurotol Date: 2014-01 Impact factor: 2.311
Authors: Michael S Harris; Natalie R Capretta; Shirley C Henning; Laura Feeney; Mark A Pitt; Aaron C Moberly Journal: Laryngoscope Investig Otolaryngol Date: 2016-06-06
Authors: Alexander T Murr; Michael W Canfarotta; Brendan P O'Connell; Emily Buss; English R King; Andrea L Bucker; Sarah A Dillon; Meredith A Rooth; Matthew M Dedmon; Kevin D Brown; Margaret T Dillon Journal: Laryngoscope Date: 2021-05-27 Impact factor: 2.970