Working memory capacity and rumination in adults who stutter.

Working Memory Capacity and Rumination in Adults Who Stutter - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Fluency Disord . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: J Fluency Disord. 2025 Apr 24;84:106119. doi: 10.1016/j.jfludis.2025.106119 Working Memory Capacity and Rumination in Adults Who Stutter Geoffrey A Coalson 1 , Robyn Croft 1 , Courtney T Byrd 1 Author information Article notes Copyright and License information 1 Arthur M. Blank Center for Stuttering Education and Research, The University of Texas at Austin ✉ Correspondence concerning this article should be addressed to Geoffrey A. Coalson, Speech, Language, and Hearing Sciences, The University of Texas at Austin, CMB 3.102, Austin, TX 78712, USA. geoffrey.coalson@austin.utexas.edu Issue date 2025 Jun. PMC Copyright notice PMCID: PMC12186184  NIHMSID: NIHMS2079145  PMID: 40334435 The publisher's version of this article is available at J Fluency Disord Abstract Purpose: Difficulty discarding irrelevant negative stimuli activated in working memory has been linked to increased rumination. Adults who stutter (AWS) report (a) rumination about communication events, and (b) difficulties during tasks that require manipulation of information held in working memory compared to non-stuttering adults (AWNS). Given the link between rumination and working memory, this study examined whether AWS discard negative stimuli faster or slower compared to AWNS, and whether speed of discarding negative information is linked to generalized rumination. Method: To assess the relationship between working memory and rumination, 26 participants (14 AWS, 12 AWNS) completed a version of the Modified Sternberg Task. Participants first memorized wordlists with positive or negative emotional valence. Participants were then cued to recall a specific wordlist, followed by a prompt word that either matched or conflicted with the wordlist. Manual response latency was compared between groups. Results: AWS and AWNS exhibit comparable speed when discarding irrelevant stimuli, with both groups slower to discard negative rather than positive irrelevant stimuli. Latency of discarding negative information was not associated with self-reported rumination in AWS. Conclusions: Preliminary data indicate no differences in the rate at which AWS and AWNS regulate negative stimuli. Additionally, rumination does not appear to be uniquely associated with the speed at which negative information is discarded. Further research is warranted to determine what role, if any, working memory has in the repetitive negative thinking reported by some, but not all, individuals who stutter. Keywords: stuttering, adults, rumination, working memory, emotion Clinical psychology research suggests that weaker working memory capacity may prevent healthy adults ( Paul et al., 2013 ; Pe et al., 2015 ; Schmeichel et al., 2008 ) and adults with psychopathology (e.g., Moran, 2016 ; Yoon et al., 2014 ; Schweizer & Dalgeish, 2011 ) from effectively regulating negative responses to adverse stimuli. A meta-analysis of 94 studies by Zetsche et al. (2018) identified one distinct aspect of cognitive control strongly linked to the tendency to engage in recurrent negative thinking, or rumination. Specifically, rumination was linked to greater difficulty discarding and replacing intrusive and/or irrelevant information activated in working memory. Adults who stutter (AWS) have reported increased rumination ( Croft & Byrd, 2023 ; Tichenor & Yaruss, 2020 ; Tudor et al., 2013 ) compared to adults who do not stutter (AWNS). AWS have also exhibited significant weaknesses during tasks of working memory (e.g., Byrd et al., 2012 ; Coalson & Byrd, 2017 ; Coalson & Byrd, 2018 ; Eichorn et al., 2023 ; Sasisekaran & Weisberg, 2018; Tichenor et al., 2022 ). To date, however, rumination in AWS has often been examined without consideration of working memory abilities, and limited only to emotionally-neutral stimuli. Based on the available data, it is possible that regulation of recurrent negative material may be more challenging for AWS, as a group, due to weaknesses in working memory capacity. Therefore, the purpose of the present study was to examine how efficiently AWS discard emotionally-valenced information from working memory capacity relative to AWNS. Working Memory Capacity and Rumination Rumination is defined as repetitive, intrusive, and self-critical negative thoughts. Although ruminative behavior is reported by neurotypical populations, elevated rumination is thought to be a hallmark symptom of depressive episodes and anxiety ( Nolen-Hoeksema, 2000 ; Roberts et al., 1998 ). Zetsche et al. (2018) examined the relationship between rumination and a variety of aspects of cognitive control (e.g., shifting, updating, response inhibition, interference control, resistance to proactive interference) in both clinical and non-clinical adult populations. Findings indicated that only proactive resistance to interference – discarding irrelevant stimuli from working memory – was significantly associated with rumination relative to all other cognitive control functions. That is, greater latencies in discarding negative and irrelevant information was linked to increased rumination in both neurotypical adults ( Paul et al., 2013 ; Pe et al., 2015 ; Schmeichel et al., 2008 ) and clinical populations (anxiety disorders: Moran, 2016 ; post-traumatic stress disorder: Schweizer & Dalgeish, 2011 ; major depressive disorder [MDD]: Yoon et al., 2014 ). Joormann and Gotlib (2008) examined the relationship between discarding irrelevant information in working memory and rumination in MDD adults. To do so, the authors administered a variation of the Modified Sternberg Task (MST; Obrerauer, 2001) to adults with and without MDD. During the MST, participants were first required to memorize positively valenced or negatively valenced wordlists, and then to quickly discard prompt words that did not match the wordlist held in memory. Findings indicated that MDD adults were significantly slower than neurotypical adults to discard negative prompt words as irrelevant. Further, the slower latency of discarding irrelevant negative material was significantly and uniquely associated with greater rumination (as measured by the Ruminative Response Scale [RRS; Treynor et al., 2003 ]), indicating that greater difficulty discarding negative words from working memory may underlie rumination. If this is the case, one may argue that individuals with unique difficulties storing and manipulating information in working memory may be particularly susceptible to increased rumination. Stuttering and Working Memory Across the lifespan, individuals who stutter exhibit unique difficulties during tasks of phonological working memory compared to non-stuttering peers. Children who stutter exhibit particular difficulty during nonword repetition tasks, wherein neutral, non-semantic words of increasing length must be accurately reproduced (e.g., Anderson & Wagovich, 2010 ; Anderson et al., 2006 ; Hakim & Bernstein Ratner, 2004 ; Pelzcarski & Yaruss, 2016; Spencer & Weber-Fox, 2014 ; Weber-Fox et al., 2008 ; see Ofoe et al., 2018 and Ofoe et al., 2025 for meta-analyses). Adults who stutter also exhibit greater variation in accuracy during nonword repetition compared to AWNS as demands on working memory increase (e.g., Byrd et al., 2012 ; Byrd et al., 2015 ; Coalson & Byrd, 2017 ; Coalson & Byrd, 2018 ; Ludlow et al., 1997 ; Sasisekaran & Weisberg, 2014 ). Specifically, working memory in AWS becomes less accurate when the participant must actively rehearse verbal information stored in working memory for a short period of time (i.e., subvocal rehearsal). Working memory abilities in AWS also appear susceptible to breakdown during tasks that require managing information that interferes with stored verbal information ( Bosshardt, 1990 ; Bosshardt, 1993 ). For example, AWS exhibit decreased accuracy during Operation Span tasks, wherein participants are required to remember a random letter sequence while also completing an unrelated secondary task on intervening trials (i.e., math problems; Tichenor et al., 2022 ; cf. Eichorn et al., 2016 ; Eichorn et al., 2023 ). These findings suggest that AWS may continue to have difficulties regulating neutral verbal stimuli. Given the findings of Zetsche et al. (2018) , it is possible that these differences in working memory may be exacerbated if stimuli are emotionally valenced rather than neutral (i.e., nonword stimuli, letter sequence with intervening math problems). Further, given the relationship documented between working memory abilities and rumination, one might expect that working memory differences in AWS, if observed, would underlie and perhaps exacerbate rumination patterns. Stuttering and Rumination Individuals who stutter often report greater rumination than non-stuttering peers, particularly in response to social cues or communicative scenarios. Croft and Byrd (2023) found that, after giving an impromptu presentation, AWS within a large cohort ( n = 96) who had reported more negative experiences of stuttering (as measured by the Overall Assessment of the Speaker’s Experience of Stuttering [OASES]; Yaruss et al., 2016 ) were significantly more likely to engage in post-event processing (i.e., negative rumination). During structured interviews, Tudor et al. (2013 ; n = 21) found that AWS reported more intrusive negative imagery on a weekly basis than AWNS related to speaking situations. However, these outcomes were typically mediated by the individuals’ experiences with stuttering and overall resiliency, and outcomes were specific to communication and social evaluation. There are data to suggest adults who stutter may also exhibit a more generalized proclivity to attend to generalized negative emotional stimuli, rather than stimuli exclusively associated with communication, although results have been mixed. In a survey study by Tichenor and Yaruss (2020 , n = 313), AWS reported an increased tendency to engage in recurrent negative thinking, yet this pattern was not exclusive to communication-related rumination. In experimental studies, Bauerly (2022 , n = 12) found AWS were more likely to attend to negative than positive facial stimuli even before social threat conditions were introduced to participants. Although Lowe et al. (2012 , n = 16) found AWS were more likely to attend to negative faces than positive faces during a presentation to a simulated audience, Lowe et al. (2016, n = 23) also reported that AWS exhibited greater recall of everyday objects when presented alongside images of negative facial affect as opposed to positive faces. Additionally, in a study by van Lieshout et al. (2014 , n = 20), AWS and AWNS exhibited differential speech motor response patterns to threatening content words; however, the authors argue that this difference was driven by state anxiety rather than stuttering experience. Among children and adolescents who stutter, Usler and Weber (2021 , n = 38) found that children ages 5-8 years old exhibit similar emotional reactivity to threatening facial expressions as same-aged children who do not stutter. In contrast, Rodgers et al. (2020 , n = 86) suggested that adolescents who stutter demonstrated an increased attentional bias towards negative facial expressions when compared to fluent controls. Taken together, findings suggest that AWS may exhibit greater bias towards negatively charged stimuli regardless of whether or not emotional stimuli are related to communication. If this is the case, it is possible that this bias would be reflected in greater difficulty discarding emotionally and negatively charged information in working memory which, in turn, would result in greater rumination. To date, the relationship between discarding negative information in working memory and rumination in AWS has not been experimentally examined. Clarifying this relationship is critical for improving interventions that reduce rumination among AWS. Rationale and Purpose of the Present Study Recent studies suggest that AWS may be prone to ruminate ( Croft & Byrd, 2023 ) and may do so at an increased rate compared to AWNS ( Tichenor & Yaruss, 2020 ; Tudor et al., 2013 ). Rumination has also been found to be associated with difficulties discarding negative stimuli from working memory ( Joormann & Gotlib, 2008 ; Zetsche et al., 2018 ). Given the social and often evaluative nature of communication, it would not be surprising to find that negative external social cues would contribute to rumination behaviors in AWS. However, given that AWS exhibit greater difficulty during tasks of phonological working memory with neutral, non-emotional stimuli compared to AWNS, it is possible, yet unexpected, that AWS would be particularly susceptible to greater rumination about generalized negative emotional stimuli, even in the absence of social threat. That is, these underlying differences in working memory capacity in AWS, specifically the ability to update and disregard irrelevant information, may be exacerbated by the increased emotional valence of the intrusive material – a pattern linked to high levels of rumination. The purpose of the present study, therefore, was to examine regulation of positive and negative content in working memory between AWS and AWNS, and to determine whether differences in working memory were associated with greater rumination in AWS. Specifically, we examined the following research questions: Research Question 1 [RQ1]: Do AWS exhibit greater difficulty discarding irrelevant positive or negative stimuli, as measured by an adapted Modified Sternberg Task ( Joormann & Gotlib, 2008 ) than AWNS? Hypothesis : AWS will demonstrate greater difficulty discarding previously irrelevant, negative information held in working memory than AWNS; that is, AWS will be slower to determine whether negative intrusion prompts were included in a cued wordlist of positive valence. Research Question 2 [RQ2]: Are intrusion effects associated with generalized rumination, as measured by the Ruminative Response Scale ( Treynor et al., 2003 ), within AWS? Hypothesis: Longer latencies discarding negative material held in working memory will be associated with greater self-reported rumination within AWS. Method Participant and Inclusion Criteria Twenty-six participants (14 AWNS, 12 AWS) were included in this study (see Tables 1 and 2 for participant demographics). All participants provided consent prior to participation. The study was approved by the institutional review board of The University of Texas at Austin (IRB: 2015-05-0044). Participants were included if they were (a) 18 years or older, (b) reported no significant medical history, and (c) demonstrated proficiency in English (LEAP-Q, Marian et al., 2007 ). Participants were classified as AWS if (a) the participant self-identified as a person who stutters, and (b) prior diagnosis of developmental stuttering was reported. Table 1. Participant Description P Age (y) Gender ID Race ID Ethnici ty ID Multiling ual Neighb Aff AWS ID Prev Dx Prev Tx OASES Sev AWS-1 19 F W H/L N .52 Y Y CARE 2.99 9 AWS-2 24 M W Non-H/L N .61 Y Y CARE 1.77 9 AWS-3 23 M W H/L N .64 Y Y FF,CARE 1.81 6 AWS-4 39 M W H/L N .49 Y Y CARE 1.94 2 AWS-5 19 F W H/L N .36 Y Y FF,CARE 3.01 3 AWS-6 22 F AIAN H/L N .46 Y Y FF,CARE 3.55 5 AWS-7 22 M NHOPI Non-H/L Y E,R .27 Y Y None 2.53 7 AWS-8 35 M A Non-H/L Y E,H .75 Y Y FF,CARE 1.78 3 AWS-9 24 M W Non-H/L N .49 Y Y FF 3.67 4 AWS-10 23 M B/AA H/L N .75 Y Y CARE 2.90 4 AWS-11 29 M B/AA Non-H/L N .53 Y Y FF 2.69 3 AWS-12 26 M W H/L N .53 Y Y FF 2.72 2 AWNS-1 20 M W H/L N .52 N N - - - AWNS-2 25 F B/AA Non-H/L N .43 N N - - - AWNS-3 20 M W H/L N .52 N N - - - AWNS-4 20 M W H/L N .52 N N - - - AWNS-5 20 F W Non-H/L Y E,S .52 N N - - - AWNS-6 21 F W H/L N .52 N N - - - AWNS-7 19 F W Non-H/L N .52 N N - - - AWNS-8 22 M W Non-H/L Y E,S .52 N N - - - AWNS-9 21 M W H/L N .68 N N - - - AWNS-10 20 M W H/L N .52 N N - - - AWNS-11 28 F W H/L Y E,S .42 N N - - - AWNS-12 21 M W Non-H/L N .52 N N - - - AWNS-13 19 F W H/L N .52 N N - - - AWNS-14 19 F W H/L N .67 N N - - - Open in a new tab Note. P = Participant; Y = yes; N = no; NR = not reported; AWS = adult who stutters; ID = self-identification; AIAN = American Indian or Alaskan Native; A = Asian; B/AA = Black or African American; NHOPI = Native Hawaiian or Other Pacific Islander; W = White; H/L = Hispanic or Latino; Prev Dx received a previous diagnosis of stuttering from licensed speech-language pathologists; Prev Tx = previously received speech-language pathology services for stuttering [sequence of treatment left-to-right]; DN R = did not report; FF = fluency-focused treatment; CARE = Blank Center CARE ™ Model treatment ( Byrd et al., 2024 ); Neighb Aff = neighborhood affluence rating (scale 0 to 1, National Neighborhood Data Archive; Melendez et al., 2020 ); OASES = Overall Assessment of the Speaker’s Experience of Stuttering (Total Impact Rating, Yaruss et al., 2016 ); Sev = stuttering severity based on O’Brian et al. (2004) nine-point scale. Languages in superscript (primary language listed first, then additional languages; E = English, H = Hindi, R = Russian, S = Spanish). Table 2. Summary of Demographic Factors and Measures of Cognition and Emotion AWNS AWS t / χ 2 p N 14 12 Age 21.0 (2.32) 25.4 (6.11) 2.36 .03 Race ID 0/0/1/0/13 1/1/2/1/7 5.00 .29 Ethnicity ID 5/9 5/7 .10 .76 Gender ID 7/7 9/3 1.71 .19 Language Status 11/3 10/2 .09 .78 Neighborhood Affluence .53 (.07) .53 (.14) .04 .97 RRS 38.14 (10.48) 40.83 (14.49) .55 .59 BDI-2 10.36 (12.14) 7.33 (7.46) .75 .46 STAI-State 37.86 (12.04) 39.58 (8.21) .42 .68 STAI-Trait 60.57 (29.67) 68.00 (26.70) .67 .51 NIHTB-CB 61.61 (9.69) 61.45 (10.35) .04 .97 BAARS-IV 29.00 (9.08) 28.75 (9.25) .07 .95 ERQ 4.51 (.60) 4.23 (1.02) .86 .40 Reappraisal 4.44 (.58) 3.99 (1.02) 1.43 .17 Suppression 4.63 (.92) 4.60 (1.29) .05 .96 OSPAN 72.2 (13.4) 57.1 (17.5) 2.39 .03 SSPAN 54.3 (2.6) 49.7 (2.0) .47 .64 Open in a new tab Note. Mean and standard deviations (in parentheses). ID = self-identification, AWNS = adults who do not stutter; AWS = adults who stutter; race (sequenced as follows) = American Indian or Alaskan Native, Asian, Black/African American, Native Hawaiian or Other Pacific Islander, White, ethnicity = non-Hispanic or Latino/Hispanic or Latino, gende r = self-identified female/male; Language Status = monolingual/multilingual; BAARS-IV = Barkley Adult ADHD Rating Scale – Fourth Edition (BAARS-IV, Barkley, 2011 ); NIHTB-CB = NIH Toolbox © - Cognitive Battery Total Composite Score ( Weintraub et al., 2013 ); STAI = State-Trait Anxiety Inventory ( Spielberger et al., 1983 ); BDI-2 = Beck Depression Inventory – Second Edition ( Beck et al., 1996 ); RRS = Ruminative Response Scale ( Treynor et al., 2003 ); ERQ = Emotional Regulation Questionnaire ( Gross & John, 2003 ); working memory tasks by Draheim et al. (2018) : OSPAN = operation span (% accuracy); SSPAN = symmetry span (% accuracy). Procedures Participant data was collected during two separate sessions, each lasting approximately 1.5 hours. The first session included informed consent, demographic questionnaire, standardized measures of speech measures, a series standardized measures of executive function and cognition, emotional health, handedness, and language history, as well as self-reported history with rumination and emotional regulation. The second session included experimental working memory tasks (e.g., operation span [OSPAN], symmetry span [SSPAN]), and the Modified Sternberg Task - a customized task designed to assess working memory and recurrent negative thinking used in Joormann and Gotlib (2008) . Stuttering Measures Stuttering severity was determined based on speech samples collected from all participants. Each videotaped sample was rated for stuttering severity using the nine-point severity scale developed by O’Brian et al. (2004 ; 1 = no stuttering , 9 = extremely severe stuttering ). To determine inter-rater reliability, a licensed certified speech-language pathologist and a research assistant trained in severity rating protocol coded video samples from 100% of the AWS. To determine intra-rater reliability, the same video samples were coded two months later. Inter- and intra-rater reliability were found to be sufficiently high (inter-rater: r = .86, inter-item correlation coefficient [ICC] = .89, p < .001; intra-rater: r = .84, ICC = .89, p < .001; Koo & Li, 2016 ). To assess the adverse impact of stuttering, participants completed the OASES ( Yaruss et al., 2016 ). Self-Administered Measures A summary of self-administered and experimental measures of cognition and emotion is provided in Table 2 . Executive Functioning. The purpose of completing standardized measures of executive functioning and overall cognition was to account for variability known to impact reaction time latency and performance during tasks of executive functioning, specifically Attention Deficit-Hyperactivity Disorder (ADHD) and overall cognition. To assess ADHD, participants completed the Barkley Adult ADHD Rating Scale – Fourth Edition (BAARS-IV, Barkley, 2011 ). The BAARS-IV is a self-report measure designed to assess frequency of symptoms as an adult that reflect ADHD symptoms. Scores > the 7th percentile are consistent with ADHD. To assess Cognition, the NIH Toolbox – Cognitive Battery (NIHTB-CB; Weintraub et al., 2013 ). The NIHTB-CB is a self-administered, tablet-based test battery comprised of eight subtests (Picture Vocabulary, Flanker Inhibitory Control and Attention, List Sorting Working Memory, Dimensional Change Card Sort, Pattern Comparison Processing Speed, Picture Sequence Memory, Oral Reading Recognition). The NIHTB-CB generated a cognitive function composite score adjusted based on a large, diverse national database of neurotypical adults ( Heaton et al., 2014 ; standard score of 50 indicates performance that was at the national average matched on age, gender, race, ethnicity, and educational attainment; scores of 40 or 60 indicates scores reflects −/+ 1 SD below or above average). Emotion and Regulation. The purpose of assessing the psychosocial profiles of participants was to account for co-occurring factors known to influence response time latencies during tasks of executive function, particularly the Modified Sternberg Task ( Joormann & Gotlib, 2008 ). To assess the potential contribution of clinical anxiety and depression, participants completed the State-Trait Anxiety Inventory (STAI; Spielberger et al., 1983 ) as well as the Beck Depression Inventory – Second Edition (BDI-2; Beck et al., 1996 ). Participants rate 40 items of the STAI based on a 4-point Likert scale (1 = almost never , 4 = almost always ). STAI scores in the 75th percentile or higher on the STAI-Trait and/or STAI-State scaled reflect elevated trait or state anxiety. Participants rate 21 items of the BDI based on a 4-point scale, with higher scores reflecting greater depressive symptoms. Scores of 11 or higher on the BDI reflect elevated risk for clinical depression. In either case, the participant was informed of their scores and provided with a list of contact information for local allied health resources. To assess self-perceived emotional regulation, participants completed the 10-item Emotion Regulation Questionnaire (ERQ; Gross & John, 2003 ). Higher overall scores on the ERQ, as well as its subscales (Reappraisal, Suppression), are based on a 7-point Likert scale (1 = strongly disagree , 7 = strongly agree ) and reflect greater use of cognitive reappraisal and expressive suppression strategies. Rumination. A primary measure of interest in the present study was the participants overall tendency to engage in recurrent negative thinking, or rumination. To assess rumination, similar to Joormann and Gotlib (2008) , participants completed the Ruminative Response Scale (RRS; Treynor et al., 2003 ). Rumination, as measured by the RRS, has significantly linked to the efficiency in discarding irrelevant material from working memory and has been administered in >70 of the 94 studies examined by Zetsche et al. (2018) . Participants rate the 22-items on the RRS on a scale from 1 ( almost never ) to 4 ( almost always ), with higher scores associated with greater self-reported rumination. Experimental Measures Working Memory. Complex span tasks required participants to complete an additional processing task between each to-be-remembered item held in memory, including mental calculation (Operation Span, or OSPAN), and judging the symmetry visual graphic (Symmetry Span, or SSPAN; see Unsworth et al., 2005 and Draheim et al., 2018 ). During the OSPAN task, participants store random letter sequences in memory (3- to 9-item list length) while completing an intervening secondary task (i.e., solving math equations). During the SSPAN task, participants store the position of a series of colored boxes in a 4-by-4 grid (2- to 7-item list lengths) while completing an intervening secondary task (i.e., symmetry judgment). The OSPAN tasks included six trials with list lengths of four, five and six letters. The SSPAN task includes six trials of list lengths three, four, and five squares. Accuracy of to-be-remembered items were measured (partial credit scoring, see Conway et al., 2005 ), as well as accuracy of response during the intervening processing trials (≥85% accuracy). Modified Sternberg Task. The measure used to assess working memory and recurrent negative thinking was derived from the Modified Sternberg Task (MST; Oberauer, 2001 ) and further described by Joormann and colleagues ( Joormann & Gotlib, 2008 ; Yoon et al., 2014 ). As depicted in Figure 1 , each trial consisted of three phases: memorization, cue, and prompt. First, participants were instructed to memorize lists of three positive cue words (e.g., gift, friendly, laughter) and a list of three negative cue words (e.g., evil, neglect, stench) presented in either red or blue font (5 sec). Following memorization, a cross appeared in the center of the screen (1 ISI) followed by a red or blue colored frame to cue the participant to recall either the positive or negative wordlist (1 sec). Finally, a single prompt word was displayed in the center of the colored rectangle frame that was (a) included in the cued word list (relevant prompt), (b) included in the non-cued word list (irrelevant intrusion prompt), or (c) an intruder word not included in any list (new prompt; see Table 3 ). Participants were instructed to quickly respond yes or no, via button-press, if the word was part of the relevant list. Response latencies were measured from presentation of prompt word to yes/no button press. Figure 1. Open in a new tab Sequence of a trial in the Modified Sternberg Task Table 3. Conditions, Latencies, and Accuracy for Adults Who Do and Do Not Stutter Condition Relevant Cue Irrelevant Intrusion Probe AWNS M (SD) % AWS M (SD) % 1 Negative New-Neutral 956.87 (350.79) 97 923.67 (320.48) 94 2 Negative Positive 1074.52 (462.24) 83 1088.00 (422.96) 92 3 Positive New-Neutral 993.92 (483.29) 95 955.90 (349.59) 97 4 Positive Negative 1089.37 (504.03) 95 1159.17 (358.57) 86 Open in a new tab Note. AWNS = adults who do not stutter; AWS = adults who stutter. All latencies measured in milliseconds (ms). Each trial represented one of four different critical conditions (see Table 3 ) that varied in cue valence (positive, negative) and irrelevant intrusion probe valence (positive, negative, new-neutral). Each condition was presented three times per session. In addition to these four critical trials, four additional relevant conditions were included on a similar number of trials, wherein cue and probe matched in valence, to avoid response habituation. Similar to Joormann and Gotlib (2008) , relevant condition trials were not included in analyses. All trials were preceded by five practice trials with written instructions and written feedback about response accuracy, and participants were instructed to ask questions during the practice period. Positive, negative, and neutral valenced stimuli were based on the Affective Norms of English Words ( Bradley & Lang, 1999 ). Appendix A provides a complete list of stimuli along with valence ratings (positive vs. negative: t (1, 18) = 77.88, p < .001; positive vs. neutral: t (1, 18) = 20.69, p < .001; negative vs. neutral: t (1, 18) = 22.54, p < .001) and word frequency values (positive vs. negative: t (1, 18) = .59, p = .282; positive vs. neutral: t (1, 18) = 1.60, p = .141; negative vs. neutral: t (1, 18) = 1.02, p = .160). Analyses RQ1: Do AWS exhibit greater difficulty discarding irrelevant positive or negative stimuli than AWNS? To examine RQ1, a 2 x 4 repeated measures mixed model ANOVA was conducted to assess the relationship between Talker Group (AWNS, AWS) and Condition (Positive Intrusion, Negative Intrusion, New-Positive, New-Negative). Talker Group served as the categorical independent variable, and Condition (Positive, Negative, New-Positive, New-Negative) served as the within-group factor. Planned pairwise comparisons were conducted for all main effects and interactions. All planned comparisons used Bonferroni’s adjusted p values, and Greenhouse–Geisser adjusted F values were reported for all ANOVAs that did not meet assumptions of sphericity. Partial eta squared (ηρ 2 ) was used to estimate effect size (.01 = small, .06 = medium, .14 = large; Cohen, 1988). RQ2: Are intrusion effects associated with generalized rumination in AWS? To examine RQ2, and similar to Joormann and Gotlib (2008) , two separate analyses were conducted. First, a correlational analysis was conducted between rumination, as measured by RRS, and negative intrusion effects. Similar to Obrerauer (2001) and Joormann and Gotlib (2008) , negative intrusion effects were calculated by subtracting latencies for new prompts (Condition 3) from latencies for negative intrusion prompts (Condition 4). Additional factors known or suspected to influence rumination were included in the model, including measures of cognition (NIHTB-CB, BAARS-IV, OSPAN, SSPAN) and measures of emotion (BDI, STAI-S, STAI-T, ERQ) and for the purposes of the present study, measures of stuttering (Severity, OASES-Total Impact Rating). Second, a linear regression analysis was conducted to examine the effects of negative intrusion effects in working memory on rumination, as measured by RRS. All factors identified as significant during correlational analyses were included into the linear regression analysis. Excluded tokens Prior to latency analysis, individual responses were excluded if (a) associated response latency exceeded >2500 ms, (b) identified as statistical outliers (+/− 2 SDs from the mean RT under each condition), or (c) the response reflected an accuracy error (i.e., yes’ was selected instead of ‘no’, or vice versa). Of a total of 312 tokens (26 participants x 12 trials), a total of 12 (3.8%) were excluded from accuracy analyses based on > 2500 ms response latency. An additional 10 (3.2%) were identified as statistical outliers (+/− 2 SDs from the mean latency under each condition). Finally, a total of 23 (7.4%) were excluded due to manual response accuracy error (AWNS, n = 12; AWS, n = 11). The remaining 267 (85.6% of the original corpus) were included in final latency analyses. See Appendix B for breakdown of excluded tokens by group. Results RQ1: Do AWS exhibit greater difficulty discarding irrelevant negative or positive stimuli than AWNS? A significant main effect for Condition was found F (3, 22) = 5.44, p = .006, ηρ 2 = .43. As depicted in Figure 2 , planned comparisons found that negative intrusive stimuli were discarded more slowly than new prompt stimuli ( p = .043), whereas positive intrusive stimuli were discarded more slowly but did not reach significance ( p = .109). No significant main effects for Talker Group F (1, 24) = .01, p = .984, nor was a significant interaction detected F (3, 22) = .46, p = .716. Findings indicate that AWS and AWNS exhibit comparable ability to discard irrelevant negative material from working memory. 1 Figure 2. Open in a new tab Mean decision latencies for negative intrusion probes, positive intrusion probes, and neutral intrusion probes for adults who do and do not stutter Note. ms = milliseconds RQ2: Are intrusion effects associated with generalized rumination in AWS? The second research question examined whether the ability to discard irrelevant negative material would be related to individual differences in rumination for AWS, even in the absence of group differences in intrusion effects. That is, we predicted that intrusion effects for negative stimuli would significantly predict individual differences in rumination for AWS. To examine this, we first conducted correlations between intrusion effects for positive and negative material in the Modified Sternberg Task and rumination (as measured by the RRS) within AWS. A series of Pearson’s correlation analyses were conducted to assess the relationship between negative intrusion effects, measures of cognition (BAARS-IV, NIHTB-CB), measures of emotion (BDI-2, STAI, ERQ) and measures of stuttering (Severity, OASES). Significant associations identified during the correlational analyses were then entered into a regression analysis along with the primary variable of interest - negative intrusion effects. Table 4 presents the results of the within-group correlational and regression analyses. A significant correlation between intrusion effects and rumination was not found for AWS ( r = .24, p = .46). That is, although some AWS with higher self-reported rumination also took longer to discard negatively-valenced stimuli from working memory, this pattern was inconsistently observed across the cohort. Significant correlations were detected, however, between rumination and BDI-2 ( p < .001), STAI-S ( p = .014), STAI-T ( p = .010), and OASES ( p = .002). These factors were included, along with negative intrusion effect, into a hierarchical linear regression analysis in which we predicted individual differences in rumination. No significant correlation was found for BAARS ( r = .30, p = .341), NIHTB-CB ( r = −.38, p = .225), ERQ ( r = −.18, p = .581), SSPAN ( r = −.51, p = .110), or Severity ( r = .22, p = .491) and therefore excluded from regression analysis. Although a significant difference in age and OSPAN was detected for AWS relative to AWNS (see Table 2 ), no significant correlation was detected between rumination and OSPAN ( r = −.17, p = .617), or rumination and age ( r = −.44, p = .150) for AWS. These factors, along with BAARS, NIHTB-CB, ERQ, SSPAN, and Severity, were therefore excluded from the linear regression analysis. Table 4. Correlational and Regression Analyses of Negative Intrusion Effect, as measured by the adapted Modified Sternberg Task ( Joormann & Gotlib, 2008 ), and Rumination in Adults who Stutter, as measured by Ruminative Response Scale ( Treynor et al., 2003 ) Factors Correlation Regression r β R 2 Negative Intrusion .24 −.09 .056 BDI-2 .94 ** 2.00 ** .885 STAI-S .68 * −.09 .001 STAI-T .71 * −.07 .005 OASES .78 ** 3.09 .007 Open in a new tab Note. Values reflect correlations (r) and unstandardized coefficients (β). BDI-2 = Beck Depression Inventory – Second Edition ( Beck et al., 1996 ); STAI = State-Trait Anxiety Inventory ( Spielberger et al., 1983 ); OASES = Overall Assessment of the Speaker’s Experience of Stuttering (Total Impact Rating; Yaruss et al., 2016 ). ** p < .01 * p < .05 The regression model accounted for 91.9% of the variance ( R 2 = .919; F (5, 11) = 13.61, p = .003; Durbin-Watson = 2.19). In this regression model, only BDI-2 scores were a significant predictor of individual differences in rumination ( p < .001) explaining 88.5% of the variance in rumination ( R 2 = .885) within the AWS group. Negative intrusion effects were not a significant predictor of rumination for AWS ( p = .298, R 2 = .056; 5.6% of variance in rumination scores explained within the regression model). Discussion The purpose of this study was to investigate whether AWS exhibited greater difficulty discarding irrelevant information from working memory than AWNS, and to determine whether differences in discarding were related to self-reported rumination in AWS. Findings indicated no differences between AWS and AWNS with respect to discarding irrelevant items in working memory, with both groups showing slower discarding of negative irrelevant stimuli. Findings further suggest, even in the absence of group differences, no significant association with rumination in AWS and their ability to discard irrelevant negative stimuli in working memory. Together, results indicate that working memory abilities in AWS are comparable to AWNS with respect to regulating negative intrusive information, and the ability to discard such material from working memory does not contribute to self-reported rumination in AWS. RQ1: Do AWS exhibit greater difficulty discarding irrelevant positive or negative stimuli than AWNS? Similar to neurotypical adults in Joormann and Gotlib (2008) , AWS and AWNS exhibited greater latencies discarding irrelevant negative material compared to neutral stimuli. Unlike adults with MDD in Joormann and Gotlib (2008) , however, the tendency to perseverate longer on intrusive, negative stimuli was comparable to the control group, in this case non-stuttering adults. That is, although a significant tendency to discard irrelevant negative information more slowly was detected, it was shared by both AWS and AWNS. It is notable that the significant main effect of Condition was limited to negative intrusion effects rather than positive intrusion effects. Both groups exhibited slower discarding of irrelevant positive stimuli relative to neutral stimuli when cued to hold negatively-valenced information in working memory, but unlike Joormann and Gotlib (2008) , this latency difference did not reach significance ( p = .109). One interpretation of this difference is that working memory regulation for participants in this study was more disrupted by the presence of irrelevant negative stimuli than irrelevant positive stimuli. However, mean latencies for negative and positive intrusion were statistically identical (positive: M = 1081.26, SE = 87.4; negative: M = 1124.4, SE = 87.2, p = .99). It is possible that the valence of the positive stimuli ( M valence rating = 5.17) was more similar to the neutral stimuli ( M valence rating = 5.17), and therefore less emotionally distinct from the neutral stimuli, than the negative stimuli ( M valence rating = 1.99). It is also possible that distinction between groups was muted due to differences in the design of new intrusion stimuli. For example, Joormann and Gotlib (2008) used new stimuli that were either positive or negatively valenced, as opposed to the present study, wherein new stimuli were neutral valence. Although this change did not appear to affect negative intrusion effects in the present study, the disparity between latencies for both negative and positive intrusion effects were less pronounced than those reported in Joormann and Gotlib (2008) . Nevertheless, although significance for positive intrusion effects was not achieved, a non-significant pattern of slower discarding of positive irrelevant stimuli was observed and, at minimum, suggest that these stimuli followed a similar trajectory to Joormann and Gotlib (2008) . It was predicted that AWS may demonstrate greater difficulty regulating verbal information in working memory compared to AWNS. This prediction was based on the tendency for AWS to exhibit greater difficulty relative to AWNS storing and manipulating information with no emotional valence in phonological working memory (e.g., nonword repetition tasks, OSPAN tasks) observed in previous studies (e.g., Byrd et al., 2012 , Byrd et al., 2015 , Coalson & Byrd, 2017 ; Coalson & Byrd, 2018 ; Tichenor et al., 2022 ). One interpretation of comparable outcomes during MST is that, contrary to group differences in previous works, the baseline phonological working memory abilities of AWS and AWNS in the present study did not differ. This interpretation is tempered by significantly poorer accuracy of AWS when compared to AWNS during OSPAN tasks prior to completion of MST ( p = .03) - a finding that is consistent with some previous investigations ( Tichenor et al., 2022 ) but not all ( Eichorn et al., 2016 ; Eichorn et al., 2023 ). As noted by Eichorn et al. (2023) , set sizes during the OSPAN task in this study and in Tichenor et al. (2022 ; 3- to 9-items) were larger than Eichorn et al. (2023 ; 3- to 7-items), suggesting that level of demand may play a critical role in differentiating the working memory capacity skills of AWS from AWNS. Similarly, the demand placed on phonological working memory in AWS during the MST may have been insufficient to exceed the abilities of AWS in the present study. During the MST, participants were required to remember three words (1- to 3-syllables in length), and store these semantically-related words in working memory for a relatively short period of time (3 seconds) before making a binary judgment about the relevancy of the intruding prompt word. In contrast, the OSPAN task (and nonword repetition tasks in previous studies) require AWS to store longer phonological sequences (i.e., 7 or more items/syllables) and to complete more challenging intervening tasks (e.g., solving a math problem between each item) prior to accurate reproduction. Accuracy requirements during OSPAN and nonword repetition are also more stringent, in that both require sequence accuracy and/or phonological accuracy during reproduction, whereas the current study required a single, nonverbal button press. Thus, the present findings may instead be taken to suggest that the level of demand placed on working memory during the adapted MST was insufficient to compromise a vulnerable phonological working memory in AWS (as evidenced by group differences on OSPAN, see Table 2 ). A straightforward interpretation is that, unlike adults with MDD in Joormann and Gotlib (2008) , regulating generalized emotional content in working memory is not uniquely challenging for AWS. In the present study, AWS processed and discarded negative material as quickly and efficiently as AWNS - a finding that is inconsistent with previous findings that indicate that individuals who stutter have an attentional bias towards negative stimuli, particularly negative facial expressions (e.g., Usler & Weber, 2021 ; Rodgers et al., 2020 ). If this were the case, it would be expected that AWS exhibit greater difficulty discarding intrusive, negative words from working memory. It should be noted that the stimuli in the present study, while negatively valenced, were not necessarily a ‘threat’ as defined in these previous studies. That is, although generalized negative stimuli (based on the ratings by Bradley & Lang, 1999 ) were sufficient to interfere with working memory processes in adults with MDD in Joormann and Gotlib (2008) , these same stimuli pose no unique threat to AWS. This interpretation is consistent with findings that, unlike adults with MDD, AWS may demonstrate greater bias if stimuli are associated with communication or social evaluation ( Usler & Weber, 2021 ; Rodgers et al., 2020 ; cf. Lowe et al., 2016; Tichenor & Yaruss, 2020 ; van Lieshout et al., 2014 ). In sum, slower negative intrusion effects were observed for both groups and not specific to AWS. However, even in the absence of group differences, it is possible that the underlying working memory regulation abilities in AWS, although comparable to AWNS, was nevertheless linked to rumination. Said another way, self-reported rumination in AWS may be attributable, at least in part, to the individual differences in discarding speed. RQ2: Are intrusion effects associated with generalized rumination in AWS? Similar to the neurotypical adults in Joormann and Gotlib (2008) , negative intrusion effects observed for AWS did not significantly correlate or significantly predict self-reported rumination. The lack of significant association between negative intrusion effects and rumination is distinct, however, from adults with MDD. That is, Joormann and Gotlib (2008) found that, along with BDI scores, negative intrusion effects were uniquely and significantly predictive of rumination in adults with MDD. Findings from the present study suggest that significant predictors of rumination patterns AWS are restricted to the BDI scores (85.0% variance explained, see Table 4 ). This finding also aligns with Croft and Byrd (2023) , in that post-event processing in AWS was linked to secondary psychosocial symptoms (in their study, trait social anxiety) but not stuttering severity, suggesting that ruminative tendencies in AWS, if observed, may be linked to negative psychosocial profiles (e.g., depressive symptoms, social or generalized anxiety) rather than the presence or severity of stuttering behaviors. Moreover, findings from the present study suggest that the link between working memory and rumination found by Joormann and Gotlib (2008) may be unique to adults with MDD, or more specifically adults with depressive symptoms. Unlike Joormann and Gotlib (2008) , AWS in the present study did not present with significantly lower rumination behaviors than control participants, nor did RRS scores for AWS exceed the expected cutoff scores for neurotypical adults ( M = 40.83, SD = 14.49; cutoff scores: 35 to 61, Mdn = 47.00; M = 47.98; Stelmach-Lask et al., 2024). One could argue that the lack of significant differences in rumination patterns in AWS, or that average rumination scores AWS occupied the lower end of the RRS (i.e., less rumination) limited the ability to observe the potential link with working memory observed for populations with more pronounced rumination behaviors. RRS scores for AWS in the present study ( M = 40.83, SD = 14.49) were lower than MDD participants in Joormann and Gotlib (2008 ; M = 51.63, SD = 13.23), whose rumination behaviors were linked to intrusion effects, yet higher than Joormann and Gotlib’s control participants ( M = 31.54, SD = 6.76), who exhibited no such association. It is possible, therefore, that investigation of an AWS cohort with greater ruminative experiences would yield different outcomes. It is also possible that the type of rumination captured by the RRS was not specific to the type of rumination reported by AWS. For example, Croft and Byrd (2023) measured rumination with the Post-Event Processing Inventory ( Blackie & Kocovski, 2017 ), which characterizes rumination in relation to (a) a specific, stressful communication event, or (b) social events. This type of rumination rests on the theory that experiencing state anxiety during a social interaction is necessary to trigger rumination ( Abbot & Rapee, 2004 ; Gök & Yalçınkaya-Alkar, 2022; Kiko et al., 2012 ; Lyubomirsky & Nolen-Hoeksema, 1993 ). The RRS used in the present study asks more generalized questions about sadness and negative self-perception unrelated to communication, and participants did not engage in a social stressor. Thus, the lack of a communication task, as well as the general nature of the questions on the RRS may not have captured the nature of ruminative patterns that AWS typically experience. Future studies would benefit from contrasting whether the specific prompts regarding rumination, similar to the specificity of the negatively-valenced stimuli to communicative events, would yield different outcomes. Limitations Several limitations to present study should be noted. First, we acknowledge the potential influence of using stimuli with affective ratings based on English when assessing a cohort that included multilingual participants. Although all participants in the present study reported English as their primary language, 5 of the 24 participants (21%; 3 of 14 AWNS, 2 of 12 AWS) reported multilingual knowledge (see Table 1 ). Multilingual speakers may exhibit unique linguistic, behavioral, and autonomic reactions to emotional words depending on proficiency and experiences in each language (e.g., Harris, 2004 ; Marian & Kaushanskaya, 2008 ; Optiz & Degner, 2012 ). Thus, although the majority of the speakers reported monolingualism, future studies may find unique interactions between cognitive-linguistic processing and affective stimuli in multilingual and multicultural AWS and AWNS. Second, our small sample size warrants caution during interpretation of the preliminary data. A priori power analyses were based on Joormann and Gotlib (2008 ; N = 23), which yielded large moderate-to-large effect sizes for analyses similar to RQ1 (MST, d = .78; Cohen, 1988) and moderate-to-large effect sizes analyses similar to RQ2 (correlational analysis: Negative Intrusion, r = .46; RRS, r = .49; regression analysis: Negative Intrusion, r = .34; BDI-2, r = .62). The sample used in RQ1 included both AWS and AWNS, and exceeded the minimum sample required to detect large effect sizes within the 2x4 repeated measure ANOVA ( N = 24, β = .80, α = .05). However, the sample size for RQ2 was restricted to AWS ( N = 12). Restriction to AWS was necessary to include stuttering-related factors (Severity, OASES) within regression analysis. Although the correlation and regression analyses were underpowered, we would argue that despite small sample size, the outcomes and their associated effect sizes were relatively unambiguous. For correlational analysis, the four factors significantly correlated with RRS were all identified with large effect sizes (STAI-S: r = .68; STAI-T: r = .71; OASES, r = .78), with BDI-2 exhibiting an effect size ( r = .94) nearly two times higher than Cohen’s (1988) cutoff criteria for large effect sizes ( r = .50). Further, post-hoc power analysis of the correlational analysis shows that, based on the detected effect sizes, the samples size across these four factors were more than sufficiently powered (power = .80; minimum sample sizes: N < 12). For the regression analysis, BDI-2 accounted for 88% of the variance ( R 2 = .88) with an effect size that was three times higher than Cohen’s (1988) cutoff for large effect sizes ( R 2 = .26). Further, post-hoc power analysis of the regression analysis in RQ2 shows that, while also controlling for the other variables, sample size for the detected R 2 of the single significant predictor of BDI-2 was sufficiently powered (power = .92, minimum sample size, N = 11) 2 . Third, the role of attention with respect to working memory processing should be considered, particularly the attentional biases towards threatening stimuli for people who stutter reported by Rodgers et al. (2020) . Although the precise interplay between attention and working memory has yet to be confirmed (see Adams et al., 2018 ), it is plausible that differences in attentional regulation may have impacted performance during the MST based on the demands of the stimuli-response task. Although the attentional bias found in Rodgers et al. (2020) immediately after visual stimuli presentation may have also been present in AWS during the MST paradigm, the non-significant group differences in the present study suggest that the role of attention bias, if present, was less influential when monitoring and manipulating internal material in working memory. That is, if attentional bias was present during silent monitoring, such atypical (dis)engagement would have disproportionately delayed negative intrusive stimuli effects for AWS. This was not observed. Ad-hoc correlational analyses provide additional support for this interpretation. Attention-related subtests from the NIHTB-CB (i.e., the Flanker subtest [ r = .04, p = .90] and the Dimensional Change Card Sort subtest [DCCS; r = .31, p = .32]) did not significantly correlate with negative intrusion effects in AWS, nor did self-reported scores on the measure of ADHD (BAARS-IV: r = .21, p = .52). Further, these measures did not significantly correlate with self-reported rumination (Flanker: r = −.26, p = .42; DCCS: r = −.07, p = .84; BAARS-IV: r = .30, p = .34), suggesting that attentional regulation was not strongly associated with participants’ behavioral or self-reported ability to discard intrusive negative material. Nevertheless, differences between paradigms across studies warrant acknowledgement. For example, Rodgers et al. (2020) used threating visual stimuli (i.e., faces; see also Usler & Weber, 2021 ) rather than orthographic stimuli. The selection of orthographic stimuli in the present study was purposeful given the challenges AWS have demonstrated with phonological rather than spatial working memory tasks but perhaps were less salient or evocative than visual stimuli linked to long-term memories of listeners’ nonverbal judgement. Differences in stimuli material, along with a number of other distinctions such as stimuli modality (i.e., visual, auditory), stimuli type (i.e., generalized negative words versus negative social evaluation), and stimuli-to-response interval (500 msec verses 8 sec), make cross-study comparisons tentative pending further investigation. Additional Considerations There are several additional considerations to the data presented in this study. First, as noted, although the negatively-valenced stimuli used in the present study were taken from Joormann and Gotlib (2008) , it is acknowledged based on the findings that generalized negative ratings of prompt stimuli may not be considered a threat to the participant and, therefore, not sufficient to disrupt the working memory processes. Future studies should incorporate stimuli linked more directly to the participants' negative experiences with stuttering and/or communication stressors. Additionally, the use of manual, button-press responses was included to replicate the design of Joormann and Gotlib (2008) . Previous researchers have found more reliable differences in AWS are observed when vocal rather than manual responses are provided ( Hennessey et al., 2014 ; van Lieshout et al., 2014 ; cf. Treleaven & Coalson, 2021 ). Finally, treatment history would likely impact the extent to which AWS are able to (a) effectively discard negative intrusive material from working memory, and as a result, (b) ruminate. Preliminary data suggest successful treatment history may play a role. Specifically, when entered as a predictor in the linear regression in RQ2, participation in the Blank Center CARE ™ Model treatment ( Byrd et al., 2024 ) yielded a significant change in rumination ( R 2 = .048, β = .37, p = .045; n = 8) when compared to prior participation in fluency-focused or no treatment ( n = 4). Future studies should consider previous treatment history, and the nature of the treatment, when examining the link between working memory and rumination in AWS. Conclusion The purpose of this study was to examine the ability to discard irrelevant negative information from working memory in AWS relative to AWNS, and examine whether the ability to discard such information in AWS was uniquely associated with self-reported rumination. Findings indicate that AWS were comparable to AWNS in their ability to discard irrelevant information from working memory; that is, both groups showed greater slowness discarding negative as compared to positive stimuli. Findings further suggest that discarding speed is not associated with rumination. Together, these findings suggest that working memory does not appear to contribute to differences regulating generalized negative information and/or the repetitive negative thinking reported by some individuals who stutter. Highlights. Rumination has been linked to difficulty discarding negative material from working memory Adults who stutter exhibit subtle difficulties in working memory capacity Increased rumination has also been reported by adults who stutter Adults who do and do not stutter, however, did not differ in ability to discard negative thoughts Generalized rumination in adults who stutter was not predicted by working memory difficulties Acknowledgements The authors thank Enjoli Richardson for her tireless leadership during project administration, as well as Sarah Bunyan, Jack Donahoo, Hudson Hanna, Zaniel Florez, Valarie Morales, Grace Morawiec, and Diana Perez for their efforts in data collection and data management. The authors would also like to thank Chris Kincak for technical consultation and Michael Mahometa for statistical consultation. Finally, this study would not have been possible without each person who stutters who shared their time and perspective. Thank you for sharing your voices and stories, and for helping to advance assessment, treatment, and quality of life for people who stutter worldwide. This research was funded in part by the grant R21DC018109-01A1 (Inhibition and Working Memory Capacity in Adults Who Stutter) from the National Institutes of Health (NIH) as well as the grant F31DC020117-01 (Clinical and Psychosocial Predictors of Post-event Processing in Adults who Stutter) from the National Institute on Deafness and Other Communication Disorders of the NIH. Data have been presented as part of a presentation at the 2024 World Congress for Stuttering and Cluttering in Austin, Texas, United States. We have no conflicts of interest to disclose. Biographies Geoffrey A. Coalson, PhD, CCC-SLP is the Associate Director of Grant and Research Development of the Arthur M. Blank Center for Stuttering Education and at the University of Texas at Austin. Robyn L. Croft, PhD, CCC-SLP is the Assistant Director of Global Research at the Arthur M. Blank Center for Stuttering Education and Research at the University of Texas at Austin. Courtney T. Byrd, PhD, CCC-SLP is the Founding and Executive Director of the Arthur M. Blank Center for Stuttering Education and Research at the University of Texas at Austin. Appendix A. Valence and Frequency of Positive, Negative, and Neutral Stimuli Positive Negative Neutral Word Valence Frequency Word Valence Frequency Word Valence Frequency affection 8.39 (0.86) 18 abuse 1.80 (1.23) 18 ankle 5.63 (1.42) 8 beach 8.03 (1.59) 61 afraid 2.03 (1.55) 57 appliance 0.05 (1.62) 5 champion 8.44 (0.90) 23 assault 2.00 (1.28) 15 arm 5.50 (1.79) 94 cheer 8.10 (1.17) 8 betray 1.68 (1.02) 4 avenue 5.15 (1.23) 46 comedy 8.37 (0.94) 39 bomb 2.10 (1.19) 36 barrel 5.30 (1.66) 24 confident 7.98 (1.29) 16 burial 2.05 (1.41) 11 basket 5.60 (1.22) 17 delight 8.26 (1.04) 29 corpse 2.18 (1.48) 7 bench 4.56 (1.34) 35 diploma 8.00 (1.39) 1 cruel 1.97 (1.67) 15 board 4.58 (1.22) 239 ecstasy 7.98 (1.52) 6 dead 1.94 (1.76) 174 bowl 4.80 (0.63) 23 engaged 8.00 (1.38) 47 debt 2.22 (1.17) 13 building 5.56 (4.08) 160 M 8.15 (.156) 1.99 (.16) 5.17 (.41) Open in a new tab Note. Mean values and standard deviations (in parentheses) as reported by Bradley and Lang (1999) . Appendix B. Excluded Tokens During Latency Analyses Negative Wordlist Positive Wordlist Neutral Intrusion Positive Intrusion Neutral Intrusion Negative Intrusion AWNS AWS N AWNS AWS N AWNS AWS N AWNS AWS N Total Total 42 36 78 42 36 78 42 36 78 42 36 78 312 Error 7 3 10 1 2 3 2 5 7 2 1 3 23 Latency ≥ 2500 ms 3 3 6 1 0 1 2 1 3 2 0 2 12 Outlier 0 1 1 0 2 2 1 3 4 1 2 3 10 Usable 32 29 61 40 32 72 37 27 64 37 33 70 267 Open in a new tab Note. ms = milliseconds Appendix C. Correlational and Regression Analyses of Negative Intrusion Effect, as measured by the adapted Modified Sternberg Task ( Joormann & Gotlib, 2008 ), and Rumination, as measured by Ruminative Response Scale (RRS; Treynor et al., 2003 ), in Adults Who Do and Do Not Stutter Factors Correlation Regression r 95% CI β t R 2 1. Negative Intrusion −.15 −.16, .01 <−.01 −.50 <.01 2. BDI-2 .60 ** .05, .92 .48 * 2.37 .52 3. STAI-S .67 ** −.78, .64 −.08 −.23 .13 4. STAI-T .76 ** −.04, .94 −.45 1.93 .07 5. BAARS-IV .45 * −.37, .58 .11 .48 <.01 6. OASES .78 ** −4.58, 11.46 3.09 .91 .02 7. ERQ-suppression −.39 * −.17, .87 −.28 −.17 <.01 8. Talker Group .37, .71 1.45 .71 <.01 Open in a new tab Note. Values reflect correlations ( r ) unstandardized coefficients ( β ), 95% CI = 95% confidence intervals for β , R 2 = proportion of RRS variance explained by each factor. BDI-2 = Beck Depression Inventory – Second Edition ( Beck et al., 1996 ); STAI = State-Trait Anxiety Inventory ( Spielberger et al., 1983 ); BAARS-IV = Barkley Adult ADHD Rating Scale – Fourth Edition ( Barkley, 2011 ); OASES = Overall Assessment of the Speaker’s Experience of Stuttering (Total Impact Rating; Yaruss et al., 2016 ); ERQ-suppression = Emotional Regulation Questionnaire ( Gross & John, 2003 ); Talker Group = adults who stutter, adults who do not stutter. ** p < .01 * p < .05 Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Declaration of interests The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Geoffrey Coalson and Dr. Robyn Croft are salaried employees of the Arthur M. Blank Center for Stuttering Education and Research. Dr. Courtney Byrd is a salaried employee of the University of Texas at Austin. Dr. Byrd also serves in a non-salaried position as the Founding and Executive Director of the Arthur M. Blank Center for Stuttering Education and Research and the President of the World Stuttering and Cluttering Organization. Research was supported by R21DC018109-01A1 (Inhibition and Working Memory Capacity in Adults Who Stutter) from the National Institutes of Health (NIH), awarded to the Dr. Coalson and Dr. Byrd, the Blank Foundation Legacy Grant, awarded to Dr. Byrd, and by F31DC020117-01 (Clinical and Psychosocial Predictors of Post-event Processing in Adults who Stutter) from the NIH awarded to Dr. Croft. 1 Joormann and Gotlib (2008) did not examine differences between discarding of irrelevant, intrusive stimuli relative to identification of matched cue-probe stimuli, nor make specific hypotheses regarding these condition types. Presumably, participants’ ability to identify a probe word that matches externally cued stimuli which are already activated in working memory would be faster than intrusive probe words (Negative Intrusion, Positive Intrusion; Conditions 2 and 4) or entirely new words (Neutral Intrusion, Conditions 1 and 3). An ancillary 2x6 repeated measures mixed model ANOVA, which included two additional matched cue-probe conditions (Positive-Positive, Negative-Negative), was conducted to examine this hypothesis. Findings indicate that this hypothesis was true, at least for negative probe words. Negative probes that matched a cued word set were recognized significantly faster ( p = .017) than intrusive negative probes (Condition 4) and also remained significantly slower to be discarded compared to neutral words ( p = .017; Condition 3). As in the original analysis, no significant differences were identified for new, matched, irrelevant, or matched positive probes ( p -values > .05). 2 To further corroborate these findings, a supplemental analysis ( Appendix C ) was conducted for RQ2, wherein both talker groups were included in the analyses, rather than restricting to AWS, and talker group was included as a categorical predictor variable. 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