Effortful Listening Despite Correct Responses: The Cost of Mental Repair in Sentence Recognition by Listeners With Cochlear Implants

Matthew B. Winn, Katherine H. Teece

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Purpose: Speech recognition percent correct scores fail to capture the effort of mentally repairing the perception of speech that was initially misheard. This study measured the effort of listening to stimuli specifically designed to elicit mental repair in adults who use cochlear implants (CIs). Method: CI listeners heard and repeated sentences in which specific words were distorted or masked by noise but recovered based on later context: a signature of mental repair. Changes in pupil dilation were tracked as an index of effort and time-locked with specific landmarks during perception. Results: Effort significantly increases when a listener needs to repair a misperceived word, even if the verbal response is ultimately correct. Mental repair of words in a sentence was accompanied by greater prevalence of errors elsewhere in the same sentence, suggesting that effort spreads to consume resources across time. The cost of mental repair in CI listeners was essentially the same as that observed in listeners with normal hearing in previous work. Conclusions: Listening effort as tracked by pupil dilation is better explained by the mental repair and reconstruction of words rather than the appearance of correct or incorrect perception. Linguistic coherence drives effort more heavily than the mere presence of mistakes, highlighting the importance of testing materials that do not constrain coherence by design.

Original languageEnglish (US)
Pages (from-to)3966-3980
Number of pages15
JournalJournal of Speech, Language, and Hearing Research
Issue number10
StatePublished - Oct 2022

Bibliographical note

Funding Information:
This research was supported by NIH Grant R01 DC017114 (Matthew Winn). The experiment design was assisted by our late colleague Akira Omaki. Data collection was assisted by Steven Gianakas, Maria Paula Rodriguez, Siuho Gong, Hannah Matthys, Lindsay Williams, Emily Hugo, and Justin Fleming. Valuable input to this project was given by our laboratory participants, as well as by Allison Johnson, Peggy Nelson, and Benjamin Munson. All data and code to run analyses and plotting

Publisher Copyright:
© 2022 The Authors.


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