Microaggression and the adult stuttering experience.

BACKGROUND: Microaggressions are subtle insults, invalidations, or slights that target people due to their association with a marginalized group. Microaggressive experiences have been shown to degrade quality of life and corroborate negative stereotypes towards persons with disabilities. To date, minimal research has been dedicated to exploring microaggressions within adults who stutter. METHODS: Seven adults who stutter participated in semi-structured focus group interviews similar to Keller and Galgay's (2010) qualitative investigation of microaggressions experienced by adults with a disability. Group interviews were transcribed and analyzed using QSR NVivo software to develop themes and subthemes. RESULTS: Eight major themes were identified within two supraordinate themes: Microaggressive Behavior (patronization, second-class status, perceived helplessness, workplace microaggression, clinical microaggression, denial of privacy) and Perception of Microaggressive Behavior (exoneration of listener, no or minimal microaggressive experience). Although patronization, second-class status, and helplessness were mentioned frequently by multiple participants, exoneration of the listener was the most frequently recurring theme. DISCUSSION: Based on these preliminary focus group interviews, stuttering-based microaggressions broadly resemble ableist microaggressions reported by Keller and Galgay (2010). Interviewees also expressed a reluctance to identify slights related to stuttering as microaggression and often characterized these incidents as unavoidable.

Verbal Response Inhibition in Adults Who Stutter.

Purpose Adults who stutter (AWS) often attempt, with varying degrees of success, to suppress their stuttered speech. The ability to effectively suppress motoric behavior after initiation relies on executive functions such as nonselective inhibition. Although previous studies found that AWS were slower to inhibit manual, button-press response than adults who do not stutter (AWNS), research has yet to confirm a consistent relationship between manual and verbal inhibition. No study has examined verbal inhibition ability in AWS. The purpose of this study, therefore, is to compare verbal response inhibition between AWS and AWNS, and compare verbal response inhibition to both the overt stuttering and the lived experience of stuttering. Method Thirty-four adults (17 AWNS, 17 AWS) completed one manual and three verbal stop-signal tasks. AWS were assessed for stuttering severity (Stuttering Severity Instrument-Fourth Edition: SSI-4) and experience with stuttering (Overall Assessment of the Speaker's Experience With Stuttering [OASES]). Results Results indicate no correlation between manual and verbal inhibition for either group. Generalized linear mixed-model analyses suggested no significant group differences in manual or verbal inhibition. Manual and verbal inhibition did not predict SSI-4 in AWS. However, verbal inhibition was uniquely associated with OASES scores. Conclusion Although underlying manual and verbal inhibition was comparable between AWS and AWNS, verbal inhibition may be linked to the adverse experience of stuttering rather than the overt symptoms of stuttering severity. Supplemental Material https://doi.org/10.23641/asha.15145185.

Manual response inhibition and quality of life in adults who stutter.

BACKGROUND: A considerable amount of research has identified inhibition differences, including slower inhibition of manual responses, in people who stutter. Recent investigations have failed to link slowed motor inhibition with overt stuttering severity. This study investigated the potential relationship between slowed manual response inhibition and the negative impact of stuttering upon individual lives of adults who stutter (AWS). METHODS: Thirty-four adults (AWS, n = 17; AWNS, n = 17) matched by nonverbal IQ completed a manual stop-signal task and provided a conversational speech sample. Motor inhibition latency for AWS and AWNS were compared. For AWS, motor inhibition latency was compared to the four subsections of the Overall Assessment of the Speaker's Experience with Stuttering (OASES; Yaruss & Quesal, 2006; General Information, Reactions to Stuttering, Communication in Daily Situations, Quality of Life). RESULTS: Similar to previous studies, AWS were significantly slower to inhibit inaccurate manual responses than AWNS. Quality of Life subtest of the OASES was found to significantly predict inhibition latency. CONCLUSION: These data replicate findings that indicate that AWS exhibit slower manual inhibition latency, and suggest that these inhibition differences may be associated with an individual's negative experience with stuttering rather than stuttering severity.

Segmental and metrical complexity during non-word repetition in adults who stutter.

Non-word repetition is weaker for adults who stutter (AWS) compared to adults who do not stutter (AWNS) as phonological demands increase. However, non-word stimuli used in previous studies varied by length, but did not vary with regard to segmental or metrical complexity. The purpose of the present study was to examine the unique influence of these two distinct types of complexity on non-word repetition in AWS and AWNS via administration of the Test of Phonological Structure (TOPhS). Twenty-four adults (12 AWNS, 12 AWS) repeated 96 non-words within a soundproof booth immediately after auditory presentation. All 96 non-word targets included on the TOPhS were one to four syllables in length and ranked based on segmental complexity (simple, moderate and complex) and metrical complexity (simple, moderate and complex). No main effect of metrical complexity was detected between groups, and no differences in accuracy were observed for non-words with simple or moderate segmental complexity. However, AWS were significantly more likely to produce a phonemic error when repeating words with complex segmental structure than AWNS, irrespective of metrical complexity. Segmental complexity may contribute to the differences in phonological working memory in AWS when controlling for metrical complexity and length.