Abstract

Developmental prosopagnosia (DP) is a condition that indicates the inability to recognize individuals by their faces from birth, without any history of brain damage. The assessment of face recognition ability and diagnosis of DP involve the use of face tests such as the Cambridge Face Memory Test (CFMT) and the Cambridge Face Perception Test, along with self-reported measures like the 20-Item Prosopagnosia Index (PI20). Face recognition accuracy is affected by anxiety. However, previous studies on the relationship between face recognition ability and anxiety have not used the PI20 measure. This study aimed to investigate the relationship between self-reported measures of face recognition ability and anxiety tendencies among healthy young individuals for DP diagnosis and its implications. We used a face recognition test, involving the PI20, CFMT, Visual Perception Test for Agnosia–Famous Face Test (VPTA–FFT), and State-Trait Anxiety Inventory (STAI). We assessed the performance of 116 Japanese young adults (75 females, median age of 20.7 years, with a standard deviation of 1.2). Subsequently, we conducted a statistical analysis to examine the relationship between the outcomes of the face recognition tests and STAI scores using Pearson correlation analysis and single correlation coefficients. The results showed a positive correlation between state anxiety and PI20 (r = 0.308, p = 0.007), and a weak positive correlation was also observed between trait anxiety and PI20 (r = 0.268, p = 0.04). In contrast, there was no correlation between CFMT and VPTA–FFT with respect to STAI. The results of the hierarchical multiple regression analysis also suggested that the correlation between the performance on the PI20 (self-report) and objective measures of face recognition performance (the CFMT and the VPTA–FFT) are driven by differences in anxiety. This study is the first to explore the relationship between face recognition abilities and anxiety using the PI20 self-report measure. There are implications for future research on the diagnosis of DP and the relationship between anxiety and face recognition.

Introduction

Face recognition ability enables individuals to differentiate and identify faces, playing a crucial role in interpersonal recognition and everyday communication. Clinical and functional imaging studies have highlighted that brain lesions, particularly in regions such as the fusiform gyrus, can lead to face recognition impairment. In recent years, there has been a growing interest in the field of face recognition research regarding developmental prosopagnosia (DP). DP refers to a condition in which individuals experience lifelong impairments in face recognition despite possessing normal visual and intellectual capabilities since birth. Clinically, there is no indication of overt central nervous system disorders, and neuroimaging studies also reveal the absence of organic brain lesions (Bate & Tree, 2017; Behrmann & Avidan, 2005; Cook & Biotti, 2016; Duchaine & Nakayama, 2006a). Epidemiological studies in Europe and Asia have reported a prevalence rate of approximately 1.9 %–2.9 % of the population (Bowles et al., 2009; Kennerknecht et al., 2006; Kennerknecht et al., 2008). People with DP encounter challenges and psychosocial difficulties in their interpersonal interactions due to impaired face recognition (Yardley et al., 2008). Early diagnosis and support are crucial, especially before adulthood and entering the workforce.

Currently, diagnosing DP presents challenges due to the absence of established strict objective criteria or biological markers (Barton & Corrow, 2016; Susilo & Duchaine, 2013). Previous studies have revealed ambiguous criteria for diagnosing DP and differentiating it from conditions other than DP in face recognition tests (Bate & Tree, 2017; Dalrymple & Palermo, 2016). Dalrymple and Palermo (2016) provided guidelines that define DP as objectively poor performance on face memory tests and a subjective feeling of repeated face recognition failures in daily life. The authors emphasized the importance of using questionnaires and interviews to assess face recognition difficulties in daily life and highlighted the limitations of relying solely on questionnaires for DP diagnosis, as individuals with DP may lack accurate insight into their own face recognition abilities.

Conclusions

The present study is the first to investigate the relationship between self-reported face recognition abilities and anxiety in young, healthy Japanese individuals using the self-perception face recognition test PI20. The findings indicated that unlike the objective face recognition tests CFMT and VPTA–FFT that showed no association with anxiety, the self-perception test scores may be influenced by state anxiety. Self-reporting, at least in its current form, provides limited insights into predicting face recognition performance in young individuals and diagnosing DP. Therefore, self-reporting is insufficient to evaluate face recognition abilities in young individuals, and DP cannot be diagnosed solely based on self-perception tests. It is necessary to combine self-perceived indicators of face recognition impairment with other perception-based face recognition function tests and consider the presence and extent of anxiety tendencies. Our findings have significant psychological implications for understanding face recognition impairments, DP diagnosis, and clinical practices. In the future, expanding research to investigate the interaction between facial and expression recognition abilities and anxiety is expected to enhance the accurate diagnosis and support for young individuals with facial recognition impairments.

Abstract

     Stuttering is a neurodevelopmental disorder affecting 5–8 % of preschool-age children, continuing into adulthood in 1 % of the population. The neural mechanisms underlying persistence and recovery from stuttering remain unclear and little information exists on neurodevelopmental anomalies in children who stutter (CWS) during preschool age, when stuttering symptoms typically first emerge. Here we present findings from the largest longitudinal study of childhood stuttering to date, comparing children with persistent stuttering (pCWS) and those who later recovered from stuttering (rCWS) with age-matched fluent peers, to examine the developmental trajectories of both gray matter volume (GMV) and white matter volume (WMV) using voxel-based morphometry. A total of 470 MRI scans were analyzed from 95 CWS (72 pCWS and 23 rCWS) and 95 fluent peers between 3 and 12 years of age. We examined overall group and group by age interactions in GMV and WMV in preschool age (3–5 years old) and school age (6–12 years old) CWS and controls, controlling for sex, IQ, intracranial volume, and socioeconomic status. The results provide broad support for a possible basal ganglia-thalamocortical (BGTC) network deficit starting in the earliest phases of the disorder and point to normalization or compensation of earlier occurring structural changes associated with stuttering recovery.

Introduction

     Developmental stuttering is a complex neurodevelopmental disorder (Smith and Weber, 2017) that disrupts the fluent flow of speech production. It is characterized by frequently occurring involuntary repetitions and prolongations in speech sounds, in addition to prolonged articulatory posture, and/or avoidance and struggle behaviors (Van Riper, 1971). Stuttering affects 5–8 % of preschool-age children (Yairi and Ambrose, 2013) and remains as a chronic speech disorder in 1 % of the general population. Typical onset of stuttering is reported to occur at 30–48 months (Bloodstein and Ratner, 2008, Reilly et al., 2013, Yairi and Ambrose, 2005) with approximately 80 % of children naturally recovering 24–36 months after the onset of stuttering (Yairi and Ambrose, 2013, Yairi and Ambrose, 2005, Yairi and Ambrose, 1999). Around this same developmental period, the neural systems supporting executive function, language, and speech-motor control undergo rapid and vigorous development (Smith and Weber, 2017, Almli et al., 2007, Chang et al., 2019, Friederici, 2006, Gilmore et al., 2018).

     In the past 20 years, an increasing number of neuroimaging studies have been conducted to understand the possible neural bases of stuttering. Convergent findings from systematic reviews and meta-analyses have highlighted several neuroanatomical differences in children and adults who stutter (AWS) compared to fluent speakers (Smith and Weber, 2017, Chang et al., 2019, Belyk et al., 2015, Budde, 2014, Ingham et al., 2005, Neef et al., 2015). Structural and functional anomalies in the left speech motor neural system, including the inferior frontal gyrus (IFG), posterior superior temporal gyrus (STG), basal ganglia-thalamo-cortical (BGTC) loop and the cerebellum have been associated with stuttering (see Chang et al., 2019 for review). In addition to gray matter anomalies, children who stutter (CWS) were found to have less white matter volume (WMV) bilaterally in the forceps minor of the corpus callosum (Beal et al., 2013) while another study found no significant differences (Choo et al., 2012).

Conclusion

     Regional gray and white matter volume differences across the whole brain were examined in preschool- and school-age CWS (both persistent and recovered) compared to age matched peers in the largest MRI dataset collected for this clinical population to date. Longitudinally collected scans that spanned up to 4 years per participant allowed tracking of developmental trajectories that differentiated among the groups. The results provide broad support for a possible BGTC network deficit starting at the early phase (preschool-age) of the disorder, involving the putamen, nucleus accumbens, left IFG/vPMC, and corticostriatal tracts. The deficits in input regions of the BGTC network appear to affect the output regions of the network including the thalamus in later phases of stuttering (school-age). The current data also provide insights into neural bases of natural recovery from stuttering during childhood. Children who recover from stuttering showed increased WMV in motor projection fibers, left AF, corpus callosum, and cerebellar peduncle around the dentate nuclei, suggesting that normalization or successful compensation of stuttering-linked neural deficits. Similar volume increases in corpus callosum, and cerebellar peduncle were also observed in pCWS, indicating an incomplete compensation. These results provide substantial new insights into possible neural bases of stuttering onset, persistence, and recovery during childhood.

Abstract

     In this paper, we examine the relationships between school suspensions and/or ADHD diagnoses and delinquency across different school disciplinary and special education climates. Given how these childhood interventions are intended to improve schoolchildren’s behavior and create a safe and predictable learning environment, it is critical for scholars to compare how these diverse yet connected responses to child misbehavior influence delinquent behavior. We use data from the Fragile Families and Child Well-Being Study (n = 2,267). Results from binomial regression models show that delinquency scores are higher among suspended children and children receiving both suspension and ADHD treatment, compared to young people who experience neither. Further, school context has a direct association with delinquency scores, as children attending schools with higher rates of school suspensions and special education enrollment have lower delinquency scores. Moreover, the relationship between individual childhood experiences with school suspension and/or ADHD treatment and delinquency is moderated by school context, especially regarding special education enrollment rates.

Introductin

     Each year, approximately 2.5 million U.S. students are suspended from school (Civil Rights Data Collection (CRDC), 2021). At the same time, 6.1 million school-aged children are diagnosed with attention deficit hyperactivity disorder (ADHD), with nearly 2 in 3 receiving therapy or taking prescription medication for their diagnoses (Danielson et al., 2018) and roughly 2 million children are enrolled in in special education behavior plans for ADHD and similar conduct problems (Civil Rights Data Collection (CRDC), 2021). The use of suspension or medical diagnoses and special education for perceived childhood behavior problems reflects different philosophies regarding misbehavior and involve different institutions, leading to different approaches (Hinshaw & Scheffler, 2014; Kern et al., 2019; Lamont, 2013). School suspensions are punishments imposed by schools that rely on negative consequences (e.g., exclusion) for misconduct to encourage good behavior (Bandura, 1977; Bear, 2012). ADHD diagnoses entail medical decisions made between doctors and caregivers, utilizing therapy and/or medication, and often involve schools implementing special education plans to promote appropriate behavior.

Results

     Table 1 presents descriptive statistics for all variables in the study for the total sample and for each category of individual suspension and ADHD treatment (i.e., neither suspended nor received ADHD treatment, received only ADHD treatment, only suspended, and both suspended and received ADHD treatment). Notably, almost half of the respondents had experiences with suspension and/or ADHD treatment. The proportion of respondents who experienced neither exclusion nor ADHD diagnoses is 0.506, 0.075 were diagnosed with ADHD without experiencing exclusionary discipline, 0.307 experienced exclusion without ADHD diagnoses, and 0.111 experienced both exclusion and ADHD diagnoses. The average delinquency score in the sample is 1.1 delinquent behaviors and varies by suspension/ADHD experience. Among those who receive neither ADHD treatment nor suspension, the average delinquency score is 0.6, compared to an average delinquency score of 0.7 for those who receive only ADHD treatment, 1.7 for those who experience suspension only, and 2.1 for those who receive both ADHD treatment and suspension. School punishment and special education rates also vary by suspension/ADHD category. Overall, the average school special education rate is 15.5. For children who are neither suspended nor treated for ADHD, the average school special education rate is 14.5, compared to an average rate of 14.7 for those only treated for ADHD, 16.6 for those only suspended, and 17.7 for those who are both suspended and treated for ADHD. The average school punishment rate is 12.5 for the total sample, compared to 9.8 for those who are nether treated for ADHD nor suspended, 8.6 for those only treated for ADHD, 16.6 for those only suspended, and 15.6 for those who are both suspended and treated for ADHD.

Abstract

Objectives
to investigate the effect of auditory temporal processing training on the alleviation of stuttering severity in children who stutter (CWS) diagnosed with auditory temporal processing (ATP) disorders.

Materials & Methods
Thirty-one (31) CWS diagnosed with ATP disorders participated in this study (intervention group: 17 participants between 7 to 12 years old; control group: 14 participants between 8 to 12 years old). The ATP test and SSI-3 were examined before, after 12 sessions (about 540 minutes) training, and three months following the conclusion of the intervention.

Result
According to the results, ATP improved significantly in the intervention group after auditory temporal training and the differences between the intervention and control group were significant (p<0.05). The improvement of ATP skills remained stable in the post-training evaluation after three months (p>0.05). Although the SSI-3 score was further improved in the intervention group, but there was no significant difference between two groups (p=0.984).

Conclusions
The findings revealed that ATP training acted as a complementary therapy alleviating stuttering severity of CWS with ATP disorders to some extent.

Introduction 

Stuttering is a well-known speech fluency disorder with primary symptoms such as involuntary and abnormal repetitions and prolongations of sounds, syllables, words, phrases, and silent pauses that disrupt the rhythmic flow of speech(1). With a prevalence rate of 2-5%, this disorder emerges mainly at 3-6 years of age nearby 1% of this population will continue to have persistent stuttering until adulthood (2). Stuttering is neurodevelopmentally (3), associated with several factors(4). Numerous studies and theories point to defective neural centers for speech control, auditory processing disorders, genetic and language impairments, and other cognitive, emotional, and social processing as the underlying causes of stuttering (5).

Currently, the basic neurogenic knowledge of stuttering is not directly linked with clinical programs(6). More promising and effective management and treatment of stuttering appear to be more logically rooted in its potential neural mechanisms (6, 7). Several studies and theories highlight the auditory system, its associated processing, and its roles, which might be the structures linked with this disorder (8).

Results

Demographic data for each group are shown in (Table 1). Children in both groups had a Stuttering Severity Instrument-3 (SSI-3) range of very mild to moderate (score 8-27).

3.1. Analyzing the Effect of Auditory Temporal Training on ATP Test Scores

In this section, data related to ATP tests before, immediately, and three months after auditory temporal training are compared between the intervention and control groups.

3.1.1. Duration pattern test

Table 2 shows a significant difference between the three interventions Duration pattern tests measurement levels (Table 2). The pairwise comparison test, by considering the Bonferroni adjustment indicated that the post-training (session 13) scores of the intervention group improved remarkably (right and left ear: P<0.001). An improvement was also observed in the control group, albeit non-significant (right ear: P=1.0, left ear: P=0.468). The analysis of the resulting stability revealed persistent improvement in the intervention group based on the post-training evaluation after three months (right ear: P=0.005, left ear: P=0.012). No significant difference was observed in the control group’s scores after three months (right ear: P=0.396, left ear: P=1.0). The between-subject primary effect test, found a significant difference between the intervention and control groups across the three measurement levels (P<0.001). The interaction of time and group was substantial (P<0.001) (Table 2).

Highlights

Abstract

Keywords

What this paper adds?

1. Introduction

2. Methods

3. Results

4. Discussion

Author’s contributions

Declaration of Competing Interest

References

Abstract

Background
The lockdown due to the COVID-19 pandemic has been a difficult period for children with Autism Spectrum Disorder (ASD), and their families.

Aims
The aim of this study was to investigate the predictors of the quality of life (QoL) of children with ASD and their parents throughout the first lockdown, providing a snapshot of the impact of the pandemic on these families life.

Method and procedures
A cohort of 243 parents of children with ASD (2–15 years old) completed an original online survey regarding the modification of ASD cores symptoms during lockdown, the type of interventions they had done before and during lockdown and the activities performed by the child. Respondents filled the PedsQL for themselves and their children.

Outcome and results
The data obtained show a worsening of specific ASD core symptoms during lockdown and their role in predicting parents and children’s QoL. Furthermore, protective factors for a better children’s QoL as the Telehealth intervention, and some activities done at home during the lockdown as physical activity and play with parents are identified.

Conclusions
This study identifies the QoL’s risk and protective factors for children with ASD and their families. Furthermore, reveals the fundamental role of the parents as children’s QoL protective factor, suggesting a higher collaboration between families and health care providers, whilst potentially improving families and children’s QoL.

1. Introduction

Coronavirus disease 2019 (COVID-19) developed into a global pandemic, and to prevent the spread of the virus, authorities imposed restrictive measures in many countries. Italy was the first European country to impose such measures (Government, 2020). Everyone was banned from leaving home except for approved reasons. This measure forced children to follow distance learning guidelines and forced parents to work remotely. Moreover, since most businesses were closed, many people lost their jobs or suffered a severe loss of income (Spinelli, Lionetti, Pastore, & Fasolo, 2020). In the family environment, the educational role of parents became much more crucial than it was before. All educational services were closed, babysitters and grandparents were not allowed to provide support, and contacts with peers were not allowed. Children could rely only on their parents, who then served as teachers, educators and playmates. Many parents also had to start working from home, and balance time and spaces while having to work with children nearby can be very challenging. Indeed, due to the absence of other educative and supportive figures, parents inherited the role of promoting positive development and new learning experiences for their children (Wang, 2020).

This plight was even more challenging for families and children with developmental disabilities (Di Renzo et al., 2020; Hume et al., 2020). One specific and broad subgroup of these families and children are children with autism spectrum disorder (ASD). The latest revision of DSM American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, 5th edition, (American Psychiatric Association, 2013) adopted the umbrella term “autism spectrum disorder” with two features: difficulties in social communication and social interaction and restricted and repetitive behaviour, interests or activities. Together with the core symptoms, cooccurring psychiatric or neurological disorders and intellectual disabilities are common in children with autism. These two sets of symptoms have a wide range of severity levels, which may be different for each child with ASD (Lord et al., 2020).

Abstract

     Online peer victimized adolescents are at an increased risk of several behavioral and emotional problems. Most cyberbullying interventions have focused on reducing the frequency of online peer aggressions. Meanwhile, less attention has been given to building resilience in victims to reduce the impact of victimization on their mental health. This study tested the effects of an online growth mindset intervention aimed at building resilience in victims. Eight hundred and fifty-six adolescents (47.10% female) were randomly assigned to the resilience vs. educational control intervention. The adolescents completed measures of online peer victimization, behavioral and emotional problems (online peer aggressions, depression, social anxiety, eating problems, and non-suicidal self-injury), entity theory of personality beliefs, and attitudes toward defending the victims of online peer aggressions at pretest and at three and six months. The resilience intervention reduced the predictive association between online peer victimization and online peer aggression and social anxiety, and it increased the association between online peer victimization and attitude towards defending the victims. The resilience intervention also reduced the entity theory of personality beliefs in all adolescents. These results are promising given that the intervention lasted only approximately 40–45 min.

Introduction

     Online peer aggressions are a major problem among adolescents. They consist of behaviors of threat, harassment, and embarrassment through electronic means or devices (Chun, Lee, Kim, & Lee, 2020). When these behaviors include as properties the vulnerability of the victims and the repetitiveness of the behaviors they are labeled as cyberbullying (Chun et al., 2020; Smith et al., 2008). According to a scoping review that included 159 studies distributed worldwide, the prevalence of online peer victimization in the last year varied from 1.0 to 61.1% (Brochado, Soares, & Fraga, 2017). Adolescents who are victims of online peer aggressions exhibit an increased risk of several emotional and behavioral problems. For example, findings of several meta-analyses reveal significant relationships between online peer victimization and anxiety, depression, low self-esteem, self-harming behaviors, and suicidal ideation (Fisher, Gardella, & Teurbe-Tolon, 2016; John et al., 2018; Katsaras et al., 2018). A recent meta-analysis of longitudinal studies found cyberbullying victimization as a predictor of internalizing problems, such as anxiety and depression (Marciano, Schulz, & Camerini, 2020). Online peer victimization was also associated with other behavioral problems, such as aggressions and social problems with peers and family members (Fisher et al., 2016). Moreover, the meta-analysis of longitudinal studies conducted by Marciano et al. (2020) showed that victimization significantly predicted perpetration of online aggressive behaviors over time, contributing to the perpetuation of these problematic behaviors and to the bully-victim circle. Beyond the emotional and behavioral problems highlighted in previous meta-analyses, some studies have also found a significant relationship between online peer victimization and eating problems as, for example, body dissatisfaction and unhealthy eating behaviors (Calvete, Orue, & Gámez-Guadix, 2016; Marco, Tormo-Irun, Galán-Escalante, & Gonzalez-García, 2018; Salazar, 2021).

Conclusions

     The findings of this study support the benefit of this growth mindset intervention in reducing the typical increase in social anxiety and online peer aggression after being victimized and improving attitudes to defend victims. The high prevalence rates of victimization among adolescents and the adverse effects that this victimization entails call for the implementation of interventions to reduce the effects of these aggressions. This type of short intervention, which can be applied easily and cheaply in schools, can be a useful tool. Future studies should continue to investigate its effects on other problems and in the long term.

Abstract

     In adolescence, smartphone use in general and social media use in particular has often been associated with negative effects, such as higher anxiety levels and body dissatisfaction. Other outcomes – such as fundamental cognitive abilities and skills (e.g., intelligence, information processing, spatial perception) – have rarely been the focus of research. Here, we analysed data from a large sample of adolescents (12–16 years; N > 12,000) who performed a series of psychometric tests ranging from intelligence, spatial perception, and information processing, to practical numeracy, and compared their test results with their social media usage (average active and passive time per day, problematic social media use). We additionally applied a random-forest model approach, useful for designs with many predictors and expected small effect sizes. Almost all associations did not outperform known age- and sex-differences on social media use; that is, effect sizes were small-to-tiny and had low importance in the random-forest analyses compared to dominant demographic effects. Negative effects of social media use may have been overstated in past research, at least in samples with adolescents.

Introduction

     In the early days of the Internet, communication was typically unidirectional and often time-staggered, that is, real-time communication was rarely possible. Since then, however, Internet-mediated communication has become so fast that it is possible to communicate with people worldwide with only very small time delays, so much so that we do not truly see much difference compared with face-to-face communication. This transformation from unidirectional, time-staggered communication to bidirectional, almost real-time communication has often been termed Web 2.0 or social media (Ellison & boyd, 2013). Since then, research has analysed what impact this transformation may have had on people, especially adolescents as frequent users, and how this could be explained.

     One example is the transformation framework (Nesi et al., 2018), which focuses on the impact of social media-based communication on adolescents’ peer experiences (i.e., experiences typically occurring between two or more individuals). It is assumed that this impact is based on seven key differences between face-to-face and online communication via social media. Specifically, social media is mostly asynchronous (i.e., there is time lapse due to the time taken to construct messages, though videoconferencing is an exception), permanent (i.e., texts and other content is stored or can be recorded), public (i.e., usually accessible by large audiences), almost universally available (i.e., can be shared regardless of physical location), lacks certain cues (i.e., physical cues such as gesture may be absent), quantifiable (i.e., use of social metrics, such as likes), and visual (i.e., use of photographs and videos).

Results

     Looking at Table 1 and Fig. 1, the results of the multiple linear regressions and RF models are clear. First, explained variance percentages were low (<7.1%), bearing in mind the number of predictors in the model (k = 18). Second, even the strongest predictors for each social media use indicator (e.g., PSMU, average social media use per day) revealed only low effect sizes, from β = −0.071 to 0.116 (PSMU binary: sex; PSMU likert: practical numeracy; time active per day: sex; time passive per day: practical numeracy; Table 1). Third, depending on the indicator of social media use (PSMU vs. average social media use per day), we see inconsistent patterns of rather tiny effect sizes. This suggests that PSMU might be different from social media use (zero-order correlations between these concepts: r = 0.26–0.28), which is perhaps surprising because usage duration should be one of the strongest indicators of problematic social media use. Fourth, if we compare the predictors for active and passive social media use, we do not see much difference except substantially lower explained variance for passive use (1.6% vs. 7.1% for active use respectively). In past research, passive use (in comparison to active use) was associated with negative aspects (e.g., stronger symptoms of anxiety and depressed mood: e.g., Escobar-Viera et al., 2018; Thorisdottir et al., 2019); therefore, we expected a more differentiated pattern of significant predictors between active and passive use, which is not apparent (see Table 1).

1- Introduction

2- Methods

3- Results

4- Discussion

References

Abstract

Objective: Stuttering is a developmental disorder of speech production with a dynamic and multifactorial nature. Scientific theories mentioned the role of auditory processing disorder in stuttering. Investigating the auditory processing in stuttering would provide insights into the mechanisms of stuttering. The details of basic auditory processing in children with stuttering (CWS) continue to remain uncertain. This study aimed to investigate the auditory temporal processing (ATP) in CWS and also its relationship with the stuttering severity. Methods: The participants of this comparative cross-sectional study were 54 CWS and 63 children without stuttering (CWOS). All children were between 7 and 12 years old. ATP ability of the participants was measured using the Backward Masking (BM), Duration Pattern (DP), and Gap in Noise (GIN) tests. Then, the groups were compared in terms of ATP results. The correlation between the scores of these tests and stuttering severity was assessed. Results: According to the results, CWS showed poorer performance on DP, BM and GIN tests when compared with CWOS. Moreover, the stuttering severity had a significant negative correlation with the DP scores and percentage of correct identification scores in GIN; whereas it had a significant positive correlation with the BM thresholds. Conclusions: The results showed that some CWS have ATP disorder, which could exacerbate their stuttering. These findings highlighted the role of ATP disorder in stuttering.

Introduction

Stuttering is a common childhood developmental disorder, which affects speech fluency [1]. It may recover spontaneously, otherwise requires long-term intensive therapeutic sessions, which typically have adverse impacts on the psychological and social health of children and their family [2,3]. Stuttering is characterized by repetition of a syllable or a part of a syllable, prolongation of sounds, and frequent interruptions in speech [4]. The prevalence of stuttering in childhood is between 4% and 5%; about 1% of this population will continue to have persistent stuttering until adulthood [5]. The underlying cause of stuttering continues to remain unknown. According to current theories, stuttering is defined as a dynamic and multifactorial disorder, which occurs due to the effects of complex interactions between atypical speech motor control system, auditory processing, language and other cognitive processing, genetic, emotions, and social factors [6]. Auditory processing is a neural mechanism, which has been emphasized in numerous theories for its role in stuttering [7]. According to the Direction of Velocities into Articulators (DIVA) model, the auditory system has a role in activating and tuning speech sound maps and forming internal speech control models (feedforward and feedback models) through neural representation of sounds via processing others’ speech, receiving self-produced speech feedback, and interacting with motor cortex [8]. Proposing the “internal model and feedback-biased motor control theory,” Max et al. (2004) assumed that stuttering is due to a mismatch between predicted (feedforward) and actual (feedback) auditory consequences of speech.The researchers attributed this mismatch and speech error to the auditory feedback control pathway [9].