Information Load Predicts Stuttering Events.
Purpose: One potential contributor to the variability of stuttering events is information load, quantified by assessing word predictability based on preceding context. This study investigated whether words with greater information load are more likely to be stuttered using word surprisal as an index...
| Published in: | Journal of Speech, Language & Hearing Research Vol. 69; no. 4; pp. 1554 - 1568 |
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| Main Authors: | , , |
| Format: | Article |
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American Speech-Language-Hearing Association
Apr2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192982177&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192982177 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Apr2026 vid: 69 iid: 4 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 192982177 10.1044/2026_JSLHR-25-00833 ppf: 1554 ppct: 14 formats: fmt: @attributes: type: P size: 886KB tig: atl: Information Load Predicts Stuttering Events. aug: au: Warner, Haley J. Ratner, Nan Bernstein Jackson, Eric S. affil: Department of Communicative Sciences and Disorders, New York University, New York Department of Hearing and Speech Sciences, University of Maryland, College Park su: Interviewing Psycholinguistics Linguistics Semantics Phonetics Language acquisition People with disabilities Speech evaluation Pearson correlation (Statistics) Statistical models Receiver operating characteristic curves Data analysis Questionnaires Logistic regression analysis Stuttering Severity of illness index Descriptive statistics Speech-language pathology Statistics Conceptual structures Comparative studies Data analysis software Evaluation sug: subj: Interviewing Psycholinguistics Linguistics Semantics Phonetics Language acquisition People with disabilities Speech evaluation Pearson correlation (Statistics) Statistical models Receiver operating characteristic curves Data analysis Questionnaires Logistic regression analysis Stuttering Severity of illness index Descriptive statistics Speech-language pathology Statistics Conceptual structures Comparative studies Data analysis software Evaluation ab: Purpose: One potential contributor to the variability of stuttering events is information load, quantified by assessing word predictability based on preceding context. This study investigated whether words with greater information load are more likely to be stuttered using word surprisal as an index while controlling for relevant linguistic variables. We also examined whether this relationship varied with stuttering severity and impact. Method: We analyzed spontaneous speech samples from 35 adults who stutter, drawn from the FluencyBank corpus. Word surprisal values were computed using the Generative Pretrained Transformer 2 large language model and analyses controlled for six linguistic predictors of stuttering. We also tested whether clinical measures of stuttering severity and overall impact predicted individuals' sensitivity to surprisal. Results: Higher word surprisal significantly increased the likelihood of stuttering, even after controlling for linguistic covariates. However, we found no evidence that stuttering severity or overall impact moderated this relationship. Conclusions: These findings suggest that words with greater information load are more likely to elicit stuttering during spontaneous speech. This pattern may reflect increased communicative demands associated with high-surprisal words. The absence of a moderating effect of stuttering severity or impact may indicate that individual differences in coping with communicative pressure, rather than clinical severity alone, shape stuttering variability. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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