Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter Purpose Methods from network science have examined various aspects of language processing. Clinical populations may also benefit from these novel analyses. Phonological and lexical factors have been examined in adults who stutter (AWS) as potential contributing factors to stuttering, although differences reported are often subtle. We reexamined the performance of ... Research Note
Newly Published
Research Note  |   June 15, 2017
Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter
 
Author Affiliations & Notes
  • Nichol Castro
    University of Kansas, Lawrence
  • Kristin M. Pelczarski
    Kansas State University, Manhattan
  • Michael S. Vitevitch
    University of Kansas, Lawrence
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Michael S. Vitevitch: mvitevit@ku.edu
  • Editor: Julie Liss
    Editor: Julie Liss×
  • Associate Editor: Ayoub Daliri
    Associate Editor: Ayoub Daliri×
Article Information
Speech, Voice & Prosodic Disorders / Fluency Disorders / Normal Language Processing / Speech, Voice & Prosody / Newly Published / Research Note
Research Note   |   June 15, 2017
Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter
Journal of Speech, Language, and Hearing Research, Newly Published. doi:10.1044/2017_JSLHR-S-16-0298
History: Received July 21, 2016 , Revised November 15, 2016 , Accepted January 14, 2017
 
Journal of Speech, Language, and Hearing Research, Newly Published. doi:10.1044/2017_JSLHR-S-16-0298
History: Received July 21, 2016; Revised November 15, 2016; Accepted January 14, 2017

Purpose Methods from network science have examined various aspects of language processing. Clinical populations may also benefit from these novel analyses. Phonological and lexical factors have been examined in adults who stutter (AWS) as potential contributing factors to stuttering, although differences reported are often subtle. We reexamined the performance of AWS and adults who do not stutter (AWNS) from a previously conducted lexical decision task in an attempt to determine if network science measures would provide additional insight into the phonological network of AWS beyond traditional psycholinguistic measures.

Method Multiple regression was used to examine the influence of several traditional psycholinguistic measures as well as several new measures from network science on response times.

Results AWS responded to low-frequency words more slowly than AWNS; responses for both groups were equivalent for high-frequency words. AWS responded to shorter words more slowly than AWNS, producing a reverse word-length effect. For the network measures, degree/neighborhood density and closeness centrality, but not whether a word was inside or outside the giant component, influenced response times similarly between groups.

Conclusions Network analyses suggest that multiple levels of the phonological network might influence phonological processing, not just the micro-level traditionally considered by mainstream psycholinguistics.

Acknowledgments
This work was made possible in part by a K-State Advancement of Women in Science and Engineering (KAWSE) ADVANCE award to Kristin M. Pelczarski.
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