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Effects of stuttering and sound avoidance on reference production and memory
Si On Yoon1, Morgan Schuchard2, Anu Subramanian3
1Department of Communicative Sciences and Disorders, New York University, New York, NY, USA.
Applied Psycholinguistics
|February 25, 2026
Summary
Adults who stutter (AWS) use language monitoring without significant cognitive costs, but stuttering moments can briefly impact memory encoding. This suggests adaptive language processing in AWS.
Area of Science:
- Cognitive Psychology
- Neuroscience of Language
- Speech and Language Pathology
Background:
- Adults who stutter (AWS) often employ linguistic monitoring to manage disfluencies.
- This monitoring may affect cognitive resource allocation, impacting language production and memory.
- The precise cognitive consequences of this monitoring remain unclear.
Purpose of the Study:
- To investigate if increased linguistic monitoring in AWS incurs dual-task costs, limiting memory encoding.
- To determine if this monitoring enhances memory through deeper conceptual processing.
- To compare cognitive performance in AWS versus adults who do not stutter (AWNS) and a simulated monitoring group.
Main Methods:
- Thirty-two AWS and sixty-four AWNS participated in a referential communication task.
- Half of the AWNS performed a sound avoidance task to simulate AWS' monitoring.
- Participants completed a recognition memory test for previously presented referents.
Main Results:
- AWS showed language production and memory performance more similar to AWNS than to the simulated monitoring group (AWNS-SA).
- AWS' memory performance decreased on a trial-by-trial basis during moments of stuttering.
- Linguistic monitoring in AWS did not appear to impose significant overall dual-task costs.
Conclusions:
- Linguistic monitoring in AWS does not impose substantial cognitive dual-task costs.
- Stuttering events can cause transient disruptions in memory encoding for AWS.
- These findings underscore the adaptive nature of linguistic monitoring in supporting language and memory in AWS.
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