Behavioral and Electrophysiological Observations of Attentional Control in Children Who Stutter

Behavioral and electrophysiological observations of attentional control in children who stutter

Chou, Fang-Chi https://iro.uiowa.edu/discovery/delivery/01IOWA_INST:ResearchRepository/12730655650002771?l#13730800740002771

Chou, F.-C. (2014). Behavioral and electrophysiological observations of attentional control in children who stutter [University of Iowa]. https://doi.org/10.17077/etd.hb1r1a1m

BEHAVIORAL AND ELECTROPHYSIOLOGICAL OBSERVATIONS OF ATTENTIONAL CONTROL IN CHILDREN WHO STUTTER

by Fang-Chi Chou

A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Speech and Hearing Science in the Graduate College of The University of Iowa 1 May 2014

Thesis Supervisor: Professor Patricia M. Zebrowski

Graduate College The University of Iowa Iowa City, Iowa

CERTIFICATE OF APPROVAL ______

PH.D. THESIS ______

This is to certify that the Ph.D. thesis of

Fang-Chi Chou has been approved by the Examining Committee for the thesis requirement for the Doctor of Philosophy degree in Speech and Hearing Science at the May 2014 graduation.

Thesis Committee: ______Patricia M. Zebrowski, Thesis Supervisor

______Jerald Moon

______J. Toby Mordkoff

______Melissa Duff

______Richard Hurtig

ACKNOWLEDGMENTS

On completion of this dissertation, I would like to express my heartfelt gratitude to my advisor Dr. Tricia Zebrowski for her support, encouragement and mentorship for the past six years. Without her guidance and persistent help, this dissertation would not have been possible. I would like to acknowledge the members of my Thesis Committee for their guidance and contributions to this project. Particular, I would like to thank Dr. Toby Mordkoff for his knowledge and time in ERP techniques and data analysis. I appreciated the time Toby spent sitting down and going through the ERP data and results. His suggestions and comments on data analysis have greatly benefited me. I would like to thank Dr. Richard Hurtig, Dr. Melissa Duff and Dr. Jerald Moon for their knowledge and thought for the revision of this thesis. Their feedback and suggestions for my thesis and the questions they raised in my defense have directed me to adopt new methods to analyze my data and explore the questions I raised in my thesis more deeply. Another big thanks to Toni Cilek for her help in subject recruitment. Most importantly I would like to thank my family—my grandma, parents and sisters—for their support and unconditional love. 2

ABSTRACT

Both theoretical models and empirical evidence implicate the combined influences of high emotional reactivity and low emotional regulation to exacerbation in children’s stuttering behavior (e.g., Conture, Walden, Arnold, Graham, Hartfiled, Karrass, 2006; Conture & Walden, 2012; Karrass et al., 2006). Attentional control is a key factor in both the development and implementation of emotional regulation (Bell & Calkins, 2012; Rueda, Posner, & Rothbart, 2004). The purpose of this study was to investigate attentional control along the distraction process in children who stutter using two event-related potential (ERP) experimental tasks: auditory-auditory distraction and visual search. Eight school-age children who stutter (CWS) and eight school-age children who do not stutter (CWNS) were recruited in this study. Using a Go/No Go paradigm, children in this study were asked to discriminate tone duration in the auditory-auditory distraction task and detect specific visual targets in the visual search task in both the auditory and visual tasks. Behavioral measures included reaction time (RT), hit rate (HR, accuracy) and false alarm (FA), while electrophysiological measures included the peak latency and mean amplitude of the mismatch negativity (MMN), P3a, N2pc and reorientation negativity (RON), and N2pc. Each ERP component reflects a specific stage along the distraction process:

automatic scanning and change detection (MMN), involuntary orientation to deviants 3

(P3a), attentional filtering (N2pc) and voluntary attentional reorientation (RON). The first three components are involved in the sensory/perceptual processing, while the last component is involved in the goal-directed processing (cognitive control for distraction compensation). These behavioral and ERP results were correlated with temperament data obtained from parent-report questionnaires. There were three main findings. First, CWS, but no CWNS, exhibited a P600 and increased peak latency of the late phase of RON (lRON). The P600 is elicited by

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violations in rule-governed sequences or the effect of encountering unexpected stimuli, while the lRON reflects evaluation of task-relevant information and motor preparation. The existence of P600 suggests that CWS return and re-evaluate deviants, perhaps due to reduced inhibitory control. As a result, CWS are delayed to start the attentional process reflected by lRON. Second, CWS exhibited a higher rate of false alarms in the auditory- auditory distraction task; this finding confirmed the notion of less efficiency in inhibitory control for CWS. Third, similar to previous research findings, our temperament data also revealed that CWS tended to exhibit relatively high negative affect in combination with relatively low effortful and attentional control, compared to their fluent peers. Taken together, present findings corroborate previous observations of relatively high emotional reactivity and relatively low efficiency in emotional regulation for CWS, including attentional and inhibitory control. Further, our results reveal that the low attentional control in CWS may result from less efficiency in the goal-directed processing for distraction compensation. 4

TABLE OF CONTENTS

LIST OF TABLES ...... viii LIST OF FIGURES ...... xi INTRODUCTION ...... 1 CHAPTER 1: LITERATURE REVIEW ...... 7

1.1 The Relation between Temperament, Emotional Variables, and Attentional Control in Regulating Emotions ...... 7 1.1.1 Temperament: Definitation and three broad dimensions ...... 7 1.1.2 Emotional variables ...... 9 1.1.3 Attentional control and its role in emotional regulation ...... 11 1.2 Attentional Control in the Development of Stuttering ...... 14 1.2.1 Emotion reactivity and regualtion in the development of stuttering ...... 15 1.2.1.1 Theoretical models ...... 15 1.2.1.2 Empirical findings ...... 18 1.2.1.3 Summary ...... 22 1.2.2 Attentional control: Direct evidence from behavioral and electrophysiological studies ...... 24 1.3 The Processing of Distraction and ERP Tasks ...... 30 1.3.1 The processing of distraction ...... 30 1.3.2 The auditory-auditory distraction task ...... 32 1.3.3 The visual search task ...... 35 1.4 Statement of Purpose and Research Questions ...... 37 1.4.1 Statement of purpose ...... 37 1.4.2 Research questoins ...... 39

CHAPTER 2: METHODS ...... 40

2.1 Participants ...... 40 2.2 Subject Recruitment Criteria and Screening Tests ...... 40

2.2.1 Subject recruitment criteria ...... 40 5 2.2.2 Screening tests ...... 41 2.2.2.1 Stuttering ...... 41 2.2.2.2 Language screening ...... 41 2.2.2.3 Hearing screening ...... 42 2.2.2.4 Handedness ...... 42 2.3 Procedure ...... 42 2.4 Data Collection ...... 43 2.4.1 Temperament questionnaires ...... 43 2.4.2 ERP data acquisition ...... 45 2.4.3 ERP stimuli and experiment procedure: The auditory- auditory distraction task ...... 45 2.4.4 ERP stimuli and experiment procedure: The visual search task ...... 46 2.5 Data Analysis ...... 48 2.5.1 The auditory-auditory distraction task ...... 48

2.5.1.1 Behavioral data: RT, HR, and FA ...... 48 2.5.1.2 ERP measures: MMN, P3a and RON ...... 49 2.5.2 The visual search task ...... 51 2.5.2.1 Behavioral data: RT, HR, and FA ...... 51 2.5.2.2 ERP measures: N2pc ...... 51 2.5.3 Temperament ...... 53

CHAPTER 3: RESLUTS ...... 54

3.1 The Auditory-Auditory Distraction Task ...... 54 3.1.1 Behavioral data ...... 54 3.1.1.1 RT ...... 54 3.1.1.2 HR ...... 55 3.1.1.3 FA ...... 56 3.1.2 ERP data ...... 56 3.1.2.1 MMN ...... 57 3.1.2.2 P3a ...... 57 3.1.2.3 eRON ...... 58 3.1.2.4 lRON ...... 58 3.1.2.5 Occurrence of P600 ...... 59 3.1.3 Summary ...... 59 3.2 The Visual Search Task ...... 60 3.2.1 Behavioral data ...... 60 3.2.1.1 RT ...... 60 3.2.1.2 HR ...... 61 3.2.1.3 FA ...... 62 3.2.2 ERP data ...... 62 3.2.2.1 The peak latency of N2pc ...... 62 3.2.2.2 The mean amplitude of N2pc ...... 62 3.2.3 Summary ...... 63 3.3 Temperament Data ...... 63 3.3.1 Descriptive data and group comparisons ...... 63 3.3.2 Correlations between temperament scores and behavioral data ...... 64 3.3.2.1 The auditory-auditory distraction task ...... 64 3.3.2.2 The visual search task ...... 65 3.3.3 Correlations between temperament and ERP data ...... 65

3.3.3.1 CWS ...... 66 6 3.3.3.2 CWNS ...... 66 3.3.4 Summary ...... 66

CHAPTER 4: DISCUSSION ...... 68

4.1 Temperament of CWS and CWNS ...... 69 4.2 Auditory-Auditory Distraction ...... 70 4.2.1 Distraction effect on behavioral and ERP data ...... 70 4.2.2 RT, HR, and FA rates in CWS ...... 75 4.2.3 Exploring the role of P600 in the process of distraction in CWS ...... 77 4.2.4 Relationships between temperament traits and behavioral and ERP data in the auditory-auditory distraction task ...... 80 4.3 Visual Search ...... 81

4.3.1 Behavioral and ERP data, and their relationships to temperament ...... 81 4.3.2 Comparison of performance on the autidory-auditory distraction and visual search tasks in CWS ...... 82 4.4 Conclusion ...... 84 4.4.1 Attentional control in the process of distraction ...... 84 4.4.2 Temperament, attentional and inhibitory control and stuttering ...... 85 4.5 Limitations and Directions for Future Research ...... 86

APPENDIX A: TABLES ...... 88

APPENDIX B: FIGURES ...... 114

REFERENCES ...... 127

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LIST OF TABLES

Table A1. Descriptive statistics for behavioral data: The auditory-auditory distraction task ...... 89 A2. Major statistical evaluation of effects of stimulus type (standard vs. deviant) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs 2 separately for RT, HR, and FA. F values, p values and ηp are summarized...... 90 A3. Descriptive statistics for the peak latency and mean amplitude of MMN, P3a, RON, and P600 for long tones ...... 91 A4. Descriptive statistics for the peak latency and mean amplitude of MMN, P3a, RON, and P600 for short tones ...... 92

A5. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the 2 peak latency and mean amplitude of MMN. F values, p values and ηp are summarized………………………………………………………………………...93

A6. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the 2 peak latency and mean amplitude of P3a. F values, p values and ηp are summarized………………………………………………………………………...94

A7. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for 2 the peak latency and mean amplitude of eRON. F values, p values and ηp are summarized ...... 95 A8. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated measures ANOVAs separately for 2 8 the peak latency and mean amplitude of lRON. F values, p values and ηp are summarized...... 96 A9. Descriptive statistics for behavioral data: The visual search ...... 97

A10. Major statistical evaluation of effects of target condition (Target 1 vs. Target 2 vs. Target 3) and group (CWS vs. CWNS) by means of repeated-measures 2 ANOVAs separately for RT, HR, and FA. F values, p values andηp are summarized………………………………………………………………………...98

A11. Descriptive statistics for the peak latency and the mean amplitude of N2pc ...... 99

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A12. Major statistical evaluation of effects of target condition (Target 1 vs. Target 2 vs. Target 3) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the peak latency and mean amplitude of N2pc. F values, 2 p values and ηp are summarized…..…………………………………………....100

A13. Means, standards deviations, and between-group analysis for temperament dimension scores for CWS and CWNS………………………………………...101

A14. Spearman’s rank correlations between the temperament dimension scores and behavioral data in the auditory-auditory distraction task for CWS and CWNS………………………………………………………………………..…102

A15. Spearman’s rank correlations between temperament dimension scores and distraction effects on HR, RT, and FA in the auditory-auditory distraction task for CWS and CWNS...... 103

A16. Spearman’s rank correlations between temperament dimension scores and the peak latency of MMN for CWS and CWNS...... 104

A17. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of MMN for CWS and CWNS...... 105

A18. Spearman’s rank correlations between temperament dimension scores and the peak latency of P3a for CWS and CWNS…………………………………...…106

A19. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of P3a for CWS and CWNS………………………………107

A20. Spearman’s rank correlations between temperament dimension scores and the peak latency of eRON for CWS and CWNS...... 108

A21. Spearman’s rank correlations between temperament dimension scores and 9 the mean amplitude of eRON for CWS and CWNS...... 109

A22. Spearman’s rank correlations between temperament dimension scores and the peak latency of lRON for CWS and CWNS………………………………..110

A23. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of lRON for CWS and CWNS……………………………111

A24. Spearman’s rank correlations between temperament dimension scores and behavioral data in the visual search task for CWS and CWNS...... 112

A25. Spearman’s rank correlations between temperament dimension scores and the ERP data obtained from the visual search task for CWS and CWNS……...113

LIST OF FIGURES

Figure

B1. Relative placement of 30 scalp electrodes on the electrode cap used in this Study……………………………………………………………………………115

B2. Stimulus type and sequence for the auditory-auditory distraction paradigm. (A) There were two types of stimuli: Standard and deviant tones. Standard tones (90% of all trials) were presented with a frequency of 1,000 Hz. Deviant tones (10% of all trials) were either higher or lower in pitch by 10% (either 900 or 1,100 Hz) from the standard. (B) Stimuli were presented every 1800 ms. The sequence of stimulus presentation was pseudo-randomized; a deviant tone was always followed by three standard tones...... 116

B3. Examples of the stimulus arrays. The upper left is the homogeneous stimulus array of eight small, blue, vertical bars. The other three are pop-out stimulus arrays, which consist of seven small, blue, vertical bars and one of three pop-out bars. The bars were placed at random locations within an imaginary rectangle which subtended 9.2 × 6.9 degrees of visual angle that was centered around a fixation point. Four types of stimulus arrays were presented randomly and equally: 25% probability for each type…………………………117

B4. Grand average ERPs for standard (black line) and deviant (red line) stimuli and the difference waveforms (deviant-standard; blue line) for long tones at selected electrodes, separately for CWS and CWNS…………………………..118

B5. Grand average ERPs for standard (black line) and deviant (red line) stimuli and the difference waveforms (deviant-standard; blue line) for short tones at selected electrodes, separately for CWS and CWNS…………………………...119

B6. Comparisons of the difference waveforms between CWS (red line) and CWNS (black line) at selected electrodes, separately for long and short tones………..120 11

B7. Grand average ERP waves elicited by the target arrays at posterior electrodes ipsilateral (black line) and contralateral (red line) to the target location in Target 1 condition. Difference waves (contralateral – ipsilateral; blue line) were computed to isolate the N2pc component………………………………...121

B8. Grand average ERP waves elicited by the target arrays at posterior electrodes ipsilateral (black line) and contralateral (red line) to the target location in Target 2 condition. Difference waves (contralateral – ipsilateral; blue line) were computed to isolate the N2pc component………………………………...122

B9. Grand average ERP waves elicited by the target arrays at posterior electrodes ipsilateral (black line) and contralateral (red line) to the target location in Target 3 condition. Difference waves (contralateral – ipsilateral; blue line) were computed to isolate the N2pc component………………………………...123

B10. A scatter plot of the percentage of FA and RT (N = 16). Different symbols are used to represent two subject groups: triangle for CWS and circle for CWNS. Three triangles in the dashed ellipse are CWS with low FAs in combination with slow RTs…………………...……………………………………………...124

B11. Difference waveforms for three CWS with low FAs and slow RTs in the long (black line) and short (red line) tone conditions at selected electrodes………...125

B12. Comparisons of the difference waveforms between correct (ERPs for long and short tones) and erroneous (FAs) trials in CWS at selected electrodes………...126

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INTRODUCTION

Stuttering is a developmental speech disorder characterized by involuntary repetitions and (in)audible prolongations of sounds and syllables. The disorder emerges between the ages of two and five; observations of development from longitudinal studies have shown that approximately 75% of children experience unassisted recovery (i.e., without treatment) 6 to 36 months post-onset, with 25% persisting into later childhood. Of significant interest to both researchers and speech-language pathologists is the identification of factors that contribute to or predict these developmental paths (e.g., Howell & Davis, 2011; Mansson, 2000; Yairi & Ambrose, 1999, 2005). Several theoretical accounts of both the onset and development of stuttering and stuttering moments point to a complex interaction of multiple factors as their origin. These include speech-language planning, motor stability, genetic predisposition, temperament, and environmental variables as the most likely contributors to the emergence of stuttering and its developmental path (e.g., Conture & Walden, 2012; Conture et al., 2006; Smith & Kelly, 1997). Recent work examining the role of temperament has focused on two specific constructs, emotional reactivity and regulation, and their role in the emergence and development of stuttering in children. Emotional reactivity refers to the threshold for or degree of arousability for either positive or negative emotions (Eisenberg & Fabes, 1992), while emotional regulation refers to one’s ability to modulate internal emotional reactivity and related behavioral reactions (Ahadi & Rothbart, 1994; Rothbart, Ellis, & Posner, 2004). Recently, Conture and his colleagues have proposed two theoretical models in an attempt to account for the role of emotional variables in the development of stuttering: the communication emotional model (CE; Conture et al., 2006) and the dual diathesis-stressor model (DD-S; Conture & Walden, 2012). In general, both the CE and DD-S models propose that stuttering develops as a result of an interaction between a 2 child’s levels of emotional reactivity and regulation, and his speech-language planning and production abilities. Specifically, the CE model suggests that the processes of speech-language planning and production can be modified or exacerbated by emotional variables, which can impact stuttering behavior in children who stutter (CWS; Conture et al., 2006). According to the CE model, emotional reactivity is associated with detecting or reacting to speech errors, while emotional regulation is associated with correcting or coping with these errors. Instances or moments of stuttering are thought to result primarily from subtle to pronounced difficulties in speech-language planning and production. Consequently, the chronic production of speech disfluencies can result in relatively high levels of emotional reactivity in the child. Over time, and with more experience, the CWS may attempt to regulate these increased negative emotions by modulating or inhibiting the stuttering behavior in some fashion (e.g., stuttering frequency, duration, and disfluency type). CWS with low emotional regulation may not be able to stay with the task (i.e., speaking), leading to more speech disruptions (Johnson, Walden, Conture, & Karrass, 2010; Karrass et al., 2006). In brief, the CE model does not suggest that emotional reactivity and regulation cause stuttering, but it does suggest that emotional regulation may impact stuttering behavior (Conture et al., 2006). The more recent model, the DD-S model, proposes that trait-like diatheses (i.e., constitutional predisposition or tendency) interact with situational stressors, leading to speech disruptions (Conture & Walden, 2012). The DD-S model suggests that there are two predisposing traits that contribute to stuttering: speech-language and emotional diatheses. These two diatheses can be influenced by two situational stressors: speech-language and emotional stressors. The speech-language stressor refers to the linguistic demand from a specific situation, such as the increased linguistic demand in public speaking. The emotional stressor refers to emotional stress that is usually elicited under a specific situation, such as increased anxiety from a situation novel to the speaker. The interaction between the diatheses combined with the influence from emotional and 3 speech-language stressors affect instances of stuttering (i.e., stuttering frequency, duration, and disfluency type), and potentially the developmental course of the problem (Conture & Walden, 2012). Importantly, the DD-S model suggests that increased linguistic demand from a specific situation can impose more challenges on children. In addition, increased emotional stress can tax their limited information processing resources, leaving fewer resources for speech-language planning and production. Both increased linguistic demand and emotional stress can result in more speech disruptions. With regard to the influence from the emotional stressor, the DD-S further suggests that high emotional stress can impose more challenges on CWS with more reactive temperament traits coupled with low emotional regulation (Conture & Walden, 2012; Walden et al., 2012). Consistent with the CE model, the DD-S model also suggests that the proficiency of emotional regulation can influence stuttering behavior. To date, research results have provided equivocal evidence to support the association between emotional reactivity and regulation and stuttering. Most of this work has focused on dispositional aspects (i.e., predisposing reactivity and regulation traits), while relatively few studies have examined emotional reactivity and regulation in different contexts. Results of these studies have shown that CWS, compared to children who do not stutter (CWNS), demonstrate higher emotional reactivity, in that they tend to be more reactive to both environmental changes and their own negative emotions

(Johnson et al., 2010; Karrass et al., 2006; Schwenk, Conture, & Walden, 2007), as well as more physically active and impulsive (Eggers, De Nil, & Van den Bergh, 2009, 2010). In addition, findings have shown that when compared to CWNS, CWS demonstrate slower adaptation to novelty, suggesting a reduced ability to self-regulate and poorer attentional control (Anderson, Pellowski, Conture, & Kelly, 2003). With regard to the latter, recent work has provided evidence that CWS exhibit less effective attentional and inhibitory control (Eggers, De Nil, & Van den Bergh, 2010, 2012; Felsenfeld, Maria, Beijsterveldt, & Boomsma, 2010; Karrass et al., 2006). Further, CWS who use these 4 regulatory strategies less frequently and for shorter durations are more likely to exhibit increased stuttering frequency (Arnold, Conture, Key, & Walden, 2011; Ntourou, Conture, & Walden, 2013; Walden et al., 2012). This latter finding is most intriguing and suggests that a fruitful avenue for exploring the proposed link between reactivity and regulation and stuttering is attentional control. Attentional control involves the process of shifting attention away from a stimulus that elicits high emotional reactivity in order to limit the emotional effects caused by the stimulus (Ahadi & Rothbart, 1994; Buss & Goldsmith, 1998). Evidence has indicated that poor attentional control is related to high levels of emotional and behavioral problems and disorders in children, and can even influence the course of the problem or disorder development and treatment outcome (e.g., Derryberry & Reed, 2002; Muris, de Jong, & Engelen, 2004; Muris & Ollendick, 2005; Purper-Ouakil et al., 2010; Rapee & Jacobs, 2002). Karrass et al. (2006) argued that the proficiency of attentional control plays a role in the development of stuttering. Research results from parent-report temperament questionnaires and attention-related behavior observations have indicated that CWS, compared to CWNS, are easily distracted by environmental changes (Schwenk et al., 2007). Additionally, CWS have been proven less effective in attentional focusing (Schwenk, et al., 2007) and attentional shifting/orienting (Anderson et al., 2003; Eggers et al., 2012; Karrass et al., 2006). Based on their work with CWS, Karrass et al. and others have speculated that CWS with reduced attentional control may be less capable of regulating the emotional reactivity associated with stuttering disruptions. Because of this difficulty in shifting attention, these children may fixate on speech disruptions, resulting in increased arousal (Eggers et al., 2012; Karrass et al., 2006). Then, according to the DD-S model, handling the increased emotional reactivity could tax the information processing resources and leave an insufficient resource for speech-language planning and production, resulting in subsequent speech disruptions (Conture & Walden, 2012; Walden et al., 2012). Conversely, CWS with good attentional control can more easily 5 shift attention toward the continued production of spontaneous fluent speech (Karrass et al., 2006; Eggers, et al., 2012). Attentional control is a rapid dynamic process, and it is implemented by different underlying mechanisms (Hopfinger & Parks, 2012). Although the evidence from parent-report temperament questionnaires and behavior studies lend support to the inefficient attentional control in CWS, the results from these measures cannot reflect the dynamic changes across an attentional process and the efficiency and proficiency of underlying attentional mechanisms. The event-related potential (ERP) method provides a measure of neural activity at a tiny time scale, which is well suited to investigate the rapid dynamics of attention control (Hopfinger & Parks, 2012). Previous stuttering research has used the ERP method to investigate the differences between individuals who do and do not stutter; however, most of these studies have examined domains of language processing in adults with long histories of habituated stuttering (e.g., Cuadrado & Weber-Fox, 2003; Weber-Fox & Hampton, 2008a, 2008b; Weber-Fox, Spencer, Spruil III, Smith, 2004). Only one study, conducted by Kaganovich, Wray, and Weber-Fox (2010), focused on attentional control in CWS. Their results indicate that CWS do not have difficulty in the change-detection system (no difference in MMN, save for the mismatch negativity), but they do have difficulty allocating attention and updating working memory efficiently (a reduced P3b). These results corroborate the evidence from the aforementioned temperament questionnaires and behavioral studies that CWS tend to be less efficient in attentional control. To sum, CWS, compared to CWNS, tend to be more distractible and less efficient in attentional control (attentional focusing and shifting), based on the aforementioned research results. The high distractibility and low efficiency in attentional focusing and shifting may indicate that CWS have difficulty in attentional control in the process of distraction. Specifically, high distractibility and low efficacy in attentional focusing may suggest difficulties in sensory/perceptual-level processing, including the change-detection 6 system (i.e., more reactive to changes in the environment) and the attentional filtering system, such as attenuating unwanted or task-irrelevant information. Low efficiency in attentional shifting suggests difficulties in the goal-directed processing (the cognitive control for compensating distraction), such as disengaging from unwanted or task-irrelevant information based on the goal of a current task and reorienting focus to task-relevant information. With these two deficits, exogenous stimuli can easily catch their attention, making goal-directed behaviors less effective (Escera, Alho, Schroger, & Winkler, 2000; Hopfinger & Parks, 2012). Kaganovich et al. (2010) reported no difference in the change-detection system (reflected by MMN) between CWS and CWNS. However, it remains unknown whether CWS differ from CWNS at other stages or systems in the distraction process. The focus of this study was on the attentional control involved in the process of distraction. Four stages in the process of distraction, indexed by different ERP components, were examined: 1) automatically scanning and detecting changes from the environment, which is reflected by MMN; 2) the involuntary orienting of attention to a distractor, which is reflected by P3a; 3) attenuating or filtering unwanted information or stimuli, which is reflected by N2pc (short for N2-posterior-contralateral); and 4) reorienting attention to a current task, which is reflected by RON (Escera et al., 2000; Hopfinger & Parks, 2012; Luck & Hillyard, 1994a, 1994b; Schröger & Wolff, 1998). To obtain the four ERP components, two ERP tasks were adopted: the auditory-auditory distraction task (Schröger & Wolff, 1998), which can elicit MMN, P3a, and RON, and the visual search task (Luck & Hillyard, 1994b), which can elicit N2pc. Subjects were asked to perform a tone duration discrimination task in the auditory-auditory distraction task, and a target detection task in the visual search task. Both behavioral data (reaction time, hit rate, and false alarm), and ERP data (the amplitudes and latencies of MMN, P3a, RON, and N2pc) were collected and analyzed. These results were then correlated with temperamental data obtained from parent-report questionnaires, and discussed. 7

CHAPTER 1 LITERATURE REVIEW

The following literature review consists of several sections. The first section describes the relationships between temperament, emotional variables, and attentional control. Also, the role of attentional control in regulating emotions will be discussed (Section 1.1). The second section presents a review of the literature on how attentional control may influence the development of stuttering (Section 1.2). The third section introduces the concept of distraction processing as well as the two ERP tasks that will be adopted in this study: the auditory-auditory distraction and the visual search tasks (Section 1.3). The last section provides a statement of the problem and research questions (Section 1.4).

1.1 The Relations between Temperament, Emotional Variables, and Attentional Control in Regulating Emotions

1.1.1 Temperament: Definition and three broad dimensions Temperament, as defined by Rothbart and Bates (1998), can be described as biologically rooted individual differences in reactivity and self-regulation that can be observed in different domains, such as emotion, motor, and attention. Reactivity and self-regulation are influenced over time by genes, environment, and experience (Rothbart & Bates, 1998). Reactivity refers to sensitivity and reactions to incoming information, and can be conceptualized in terms of the onset, duration, and intensity of the response, as well as its offset and recovery time (Rothbart, Derryberry, & Hershey, 2000; Rothbart et al., 2004). Temperamental reactivity is seen in general behavioral tendencies, such as negative emotion or distress proneness, or specific physiological reactions, such as heart rate (Rothbart et al., 2004). Self-regulation refers to processes of adaptive control that 8 serve to modulate these reactive tendencies (Rothbart et al., 2004; Rothbart & Rudua, 2005). Basically, three broad/high-level dimensions of temperament have been identified in children across different ages: surgency, negative affect and effortful control (Putnam, Ellis, & Rothbart, 2001). Two dimensions are involved in reactivity: surgency and negative affect (Evans & Rothbart, 2009; Rothbart, Ahadi, Hershey, & Fisher, 2001). Surgency, also known as positive emotionality or extraversion, refers to the tendency to actively approach new experiences with positive emotional states (Derryberry & Reed, 2003; Rothbart et al., 2001; Tellegen, 1985; Watson, Wiese, Vaidya, & Tellegen, 1999). Negative affect, also known as negative emotionality or neuroticism, refers to the tendency to experience feelings of sadness, anxiety, fear, and frustration when confronted with novel or threatening stimuli or situations (Muris & Ollendick, 2005; Rothbart et al., 2001, Tellegen, 1985; Watson et al., 1999). The third dimension is effortful control, which refers to self-regulation processes, and is defined as “the ability to inhibit a dominant response to perform a subdominant response” (Rothbart & Bates, 1998). Specifically, effortful control pertains to the abilities to voluntarily inhibit, activate, or modulate attention, emotion, and behavior (Eisenberg, Smith, Sadovsky, & Spinrad, 2004). Measures of effortful control usually include two indices: attentional control (e.g., the ability to focus or shift attention as needed) and inhibitory control (e.g., the capacity to plan and suppress inappropriate approach behavior; Rothbart et al., 2004). The temperament dimension of effortful control has been considered to be associated with the executive attention system, which includes the abilities of inhibitory control, error detection, conflict monitoring, and action planning (Rothbart & Bates, 1998; Rothbart et al., 2004). Beyond identifying these broad temperamental dimensions, of central importance are the interactions between children’s reactive tendencies and their effort to control them, namely, the interactions among surgency, negative affect and effortful control 9

(Rothbart, 2007). Research results have shown that a low level of effortful control has been associated with a high level of emotional and behavioral problems, including symptoms of anxiety, depression, and aggression, and other disorders (e.g., Muris, 2006; Muris et al., 2004; Muris, Meesters, & Rompelberg, 2007; Muris & Ollendick, 2005). Further, some researchers have indicated that a low level of attentional control is associated with a high level of anxiety and depression, whereas a low level of inhibitory control is associated with a high level of aggression (Dennis & Brotman, 2003; Muris, 2006; Muris et al., 2004; Muris et al., 2007). For example, anxious individuals with poor attentional control may have difficulty constraining their negative emotionality, leading to immersing in negative emotions and having more avoidant responses. Extraverted individuals with poor inhibitory control may not appropriately constrain their positive emotionality, resulting in more aggressive and impulsive responses in behavior and emotion (Eisenberg et al., 2004).

1.1.2 Emotional variables Emotion is basic to temperament. Emotion and temperament correspond to each other and can be viewed as “part of the same whole” (Bates, Goodnight, & Fite, 2008). Inherited temperamental traits can influence the way that an individual expresses and regulates his or her emotions (Rothbart et al., 2004).

Emotion can be defined as “the processes of registering the significance of physical or mental events, as the individual construes that significance” (Campos, Frankel, & Camras, 2004, p. 379). Two processes are involved: emotional reactivity and emotional regulation, which interplay with each other (Cathy, Horesh, Apter, & Gross, 2010). These two processes are dynamic and governed by an interconnected network of neurological structures, including the prefrontal cortex, cingulate cortex, thalamus, hypothalamus, hippocampus, and amygdala (Kolb & Whishaw, 2003). 10

People vary significantly in their emotional responses to the same stimulus or situation. The concept of emotional reactivity refers to the characteristics of the emotional responses to internal and external stimuli. These responses are reflected in threshold, intensity, and reaction rate (Davidson, 1998). The process of emotional reactivity emphasized in temperament is often considered an interface between the perceptual/cognitive stages (inputs) and response tendencies (outputs). In general, this process begins when an external or internal stimulus signals to an individual that something may be urgent and important. After the stimulus is attended to and evaluated, two types of information will be sent into the emotion-generative system: perceptual information (e.g., appraisals of the physical characteristics of the stimulus, the difference between the stimulus and existing perceptual representations), and conceptual information (e.g., appraisals of the importance of the stimulus and the capacity to cope with the important event). These two types of information will be integrated and then trigger a coordinated set of response tendencies that involve experiential (e.g., being anxious), behavioral (e.g., crying), central (e.g., activation in the amygdala and prefrontal cortex), and peripheral physiological systems (e.g., increased heart rate; Derryberry & Reed, 2003; Gross, Richards, & John, 2006). As a coordinated set of emotional response tendencies is generated, the process of emotional regulation is often activated to modulate these emotional response tendencies.

Emotional regulation refers to the attempts that individuals make to influence “which emotions we have, when we have them, and how we experience and express them” (Gross, 2002, p. 282), and the purpose of emotional regulation is to accomplish emotion-related biological or social adaptation, or to achieve personal goals (Eisenberg & Spinrad, 2004). Emotional regulation may be conscious or unconscious, automatic or controlled, and strategies of emotional regulation can be applied to change negative and positive emotional states (i.e., increasing or decreasing; Parrot, 1993). One of the critical 11 strategies in emotional regulation is attentional control (Bell & Calkins, 2012; Rueda, Posner, & Rothbart, 2004).

1.1.3 Attentional control and its role in emotional regulation Attentional control involves the facilitation and inhibition of information; it can refer to a process for selectively facilitating the most important information and inhibiting others (Derryberry & Reed, 2003). The abilities of attentional control progressively increase until adolescence (Rothbart & Bates, 1998). The measures often include attentional focusing (e.g., focusing on the task-relevant information and attenuating the task-irrelevant information) and attentional shifting (e.g., flexibly shifting attention among different stimuli; Rothbart et al., 2004). Results from neuroimaging studies have indicated that there are three distinct neural network systems involved in attentional control: alerting, orienting, and executive attention systems (Fan, McCandliss, Fossella, Flombaum, & Posner, 2005; Posner & Peterson, 1990; Posner & Rothbart, 2007). The alerting attention is involuntary and defined as achieving and maintaining a high-alert state to incoming stimuli with a high priority, such as task-relevant information or a threatening stimulus (Rueda et al., 2004). The alerting system has been associated with the right frontal and parietal cortexes as well as the thalamus (Fan et al., 2005), and can be modulated by the neurotransmitter norepinephrine (Marrocco & Davidson, 1998). The orienting attention involves both involuntary attention, such as when a novel stimulus automatically captures an individual’s attention, and voluntary attention, such as when an individual voluntarily directs attention to a specific stimulus (Rueda et al., 2004; White, Helfinstein, Reed-Sutherland, Degnan, & Fox, 2009). The act of orienting includes three steps: disengaging attention from a location, directing attention to a new location, and engaging attention to the new location (Posner & Petersen, 1990). Orienting typically involves the process of selective attention, which is defined as focusing one’s 12 attention on a specific location rather than other locations in the environment (White et al., 2009). Research has shown that the orienting attention system for visual events is associated with posterior brain areas, including the superior parietal lobe, the temporal parietal junction, and the frontal eye fields (Corbetta & Shulman, 2002), and can be modulated by the neurotransmitter acetylcholine (Rueda et al., 2004). The executive attention involves monitoring and resolving conflict among thoughts, feelings, and responses, for example, the filtering out of irrelevant information, inhibiting unwanted responses, switching attention between different tasks, detecting errors, and making plans (Bell & Calkins, 2012; Posner & Fan, 2004; Rueda et al., 2004). The executive attention is thought to be involved in the control of voluntary attention and the regulation of involuntary attention processes (i.e., orienting; Posner & Fan, 2004). Research has indicated that the executive attention system is associated with the anterior cingulate, lateral ventral prefrontal, and basal ganglia (Fan et al., 2005; Posner & Rothbart, 2007), and can be modulated by the neurotransmitter dopamine (Rueda et al., 2004). These three systems are tightly cooperative, and dysfunctions or less efficiency in any of these systems can create maladaptive attention biases, such as being preferentially allocated to irrelevant negative stimuli, leading to a cascade of problems; for example, less efficiency in regulating negative emotions (Derryberry & Reed, 2002; White et al.,

2009). Most evidence relating attentional control to emotional regulation comes from research investigating the regulation of fear or anxious behaviors, and findings have indicated that anxious individuals may have dysfunctions or less efficiency in their attentional control (e.g., Derryberry & Reed, 2002; Muris et al., 2007; White et al., 2009). Researchers have indicated that, compared to non-anxious individuals, anxious individuals are: 1) more reactive to environmental changes, especially threat- or fear-relevant stimuli (alerting); 2) less capable of disengaging attention from a threat- or 13 fear-relevant stimulus (orienting); and 3) less capable to inhibit task-irrelevant emotional stimuli as well as voluntarily and flexibly switch attention from one stimulus to anther (executive attention; e.g., Carthy et al., 2010; Derryberry & Reed, 1998, 2002; Muris, et al., 2004; Muris et al., 2007; White et al., 2009; Williams, Mathews, & MacLeod, 1996). Further, to consider individual differences in attentional control, Derryberry and Reed (2002) compared the performances of trait-anxious participants with either good or poor attentional control in a spatial orienting task. In this task, participants were engaged in a motivated game in which they could gain or lose points depending on their speed in detecting targets. Before each trial, a peripheral cue was presented that directed attention to a threatening location (where points would be lost) or a safe location (where points would not be lost). Results indicated that anxious participants with poor attentional control exhibited an attentional bias in favor of threatening locations, and were delayed in shifting attention from a threatening to a safe location. In contrast, anxious participants with good attentional control were more capable of shifting their attention from a threatening to a safe location. Based on the results, Derryberry and Reed suggest that an ability to disengage attention from threat and engage in safety helps individuals to constrain anxiety and regulate their negative emotions more effectively. Taken together, the above findings indicate the importance of attentional control in the emotional processes. However, attention cannot function alone; it should cooperate with other systems, such as the cognitive system. Derryberry and Reed (2003) propose that the role of attentional control in the emotional processes may be such that attentional control can influence cognitive processing (e.g., appraisals of emotions and coping strategies) and then impact the generation of emotional responses and behaviors. As described by Bower (1981), an activated emotion node can spread excitation to related conceptual nodes. For example, a feeling of pain can activate the concepts of diseases, hospitals, doctors, and so on. As mentioned previously, anxious individuals are prone to have involuntary attention biases toward threat-related information in the 14 environment, leading to activation of the emotion of fear and fear-related concepts. Anxious individuals with good attentional control can easily disengage from fear and fear-related concepts and focus on other soothing situations, effectively decreasing the intensity of the fear. In contrast, anxious individuals with poor attentional control will have difficulty disengaging from the emotion and related concepts of fear. In turn, these fear-related concepts can feed back to deepen the fear, and then activate more fear-related concepts. Once the vicious cycle is formed, people may persist in the negative emotion. This negative experience can feed back to influence individuals’ emotional systems; that is, the experience can bias individuals’ cognitive appraisals, including overestimating the threat and underestimating self-coping potential (Derryberry & Reed, 2003). When confronted with the same stimulus again, the vicious cycle will be activated quickly and more negative emotional responses will be generated, leading to worse emotional problems. This perspective of the way that attentional control influences the emotional processes in anxious individuals can be applied to explain the relations of emotion, attention, and stuttering.

1.2 Attentional Control in the Development of Stuttering Recently, researchers have focused on examining the role of emotional variables—emotional reactivity and regulation—and their role in the emergence and development of stuttering in children. Both theoretical models and empirical findings have indicated that emotional regulation plays a crucial role in changes of stuttering behavior. Thus, some researchers have focused on investigating specific strategies in emotional regulation and their influence in stuttering, such as attentional control and inhibitory control. The focus of the present study was attentional control. This section is divided into two small sections: First, we discuss recent empirical evidence and theoretical models regarding the relationships between the two emotional variables and 15 stuttering. Second, empirical findings regarding attentional control and its role in stuttering are discussed.

1.2.1 Emotional reactivity and regulation in the development of stuttering 1.2.1.1 Theoretical models Two theoretical models that link emotional variables to stuttering have been proposed by Conture and his colleagues: the communication emotional model (CE; Conture, et al., 2006), and the dual diathesis-stressor model (DD-S; Conture & Walden, 2012). Both models propose that emotional variables interact with speech-language processing, affecting stuttering behaviors. In addition, the CE and DD-S models also indicate that emotional regulation may play an important role in affecting changes in stuttering instances for CWS. The CE model. In the framework of this model, stuttering instances are considered to be influenced by three types of contributors: distal, proximal, and exacerbating contributors. Distal contributors are antecedent variables, events, or conditions, such as genes and environment, which predispose a person to stuttering. Proximal contributors are processes that underlie speech-language planning and production. That is, the processes occur between a person’s thought and overt speech behavior, such as lexical selection and phonological encoding (Levelt, 1989). Less efficiency in these processes may result in more speech disruptions. The CE model suggests that the two distal contributors—genetic and environmental factors—interact with each other and cause difficulties in speech-language planning and production. That is, the distal contributors set up the foundation for the proximal contributors that trigger instances of stuttering. In addition, the processes of speech-language planning and production, as well as the end product of stuttering instances, can be influenced by emotional reactivity and regulation, which are defined as exacerbating contributors. In terms of the CE model, emotional reactivity is associated with detecting and reacting to covert (i.e., errors occur 16 in the processes of speech-language planning and production) and overt speech errors (i.e., stuttering instances), whereas emotional regulation is associated with correcting and coping with these covert or overt speech errors. Emotional reactivity and regulation are thought to contribute to changing stuttering instances quantitatively (i.e., stuttering frequency) and/or qualitatively (i.e., disfluency types and stuttering duration). Another variable of importance is experience, which is thought to impact the connections between speech-language planning and production and emotional variables. Specifically, experience can strengthen the connections between emotional reactions and disruptions in speech-language planning and production. In the early stages of stuttering, instances of stuttering are thought to mainly result from internal difficulties in the speech-language planning and production, and have less or no influence from emotional reactivity and regulation. The internal difficulties and disruptions in speech fluency can produce an emotional reaction in children, such as increased negative emotions. Over time, and with more experience with stuttering, children may try to cope with or regulate these negative emotions. Such emotional regulation might contribute to quantitative (e.g., stuttering frequency) and/or qualitative (e.g., stuttering duration and disfluency type) changes in stuttering. Children with high emotional regulation are capable of regulating their feelings and focusing on maintaining fluent speech. However, children with low emotional regulation may tend to focus on the increased negative emotions and be less able to allocate attention to maintaining fluent speaking, resulting in changes in the quantity and/or quality of stuttering instances (Conture et al., 2006; Karrass et al., 2006). The DD-S model. Conture and Walden (2012) adopted the same elements (i.e., speech-language planning and production, and emotional reactivity and regulation) from the CE model and proposed a new model based on a diathesis-stress theory in which “stress activates a diathesis, transforming the potential of predisposition into the presence 17 of psychopathology” (Monroe & Simons, 1991, p 406). The DD-S suggests that diatheses interact with stressors, leading to speech disruptions. Diatheses refer to predisposing traits, which are relatively stable. Stressors refer to environmental challenges, which are variable and different across situations. Two diatheses are included in this model: speech-language and emotional diatheses. The speech-language diathesis consists of processes in speech-language planning and production. The emotional diathesis consists of proclivities or tendencies for emotional reactivity and regulation. Likewise, there are two types of stressors in the DD-S model, speech-language and emotional stressors. The speech-language stressors refer to linguistic requirement under a specific situation for the purpose of effective communication. Increased linguistic requirement (i.e., need to achieve rapid conversation) can impose more challenges on children’s speech-language processing system, leading to more speech disruptions. The emotional stressors refer to “variable features of situations that elicit emotion” (Walden et al., 2012, p. 634), namely, emotional stress that is usually elicited under a specific situation. Increased emotional stress (e.g., a situation novel to the speaker) can cause high emotional reactivity. Reducing the increased emotional reactivity can tax our limited resources for information processing (i.e., more processing resources are needed for reducing the increased emotional reactivity), leaving fewer resources for speech-language processing and resulting in more speech disruptions.

Importantly, the DD-S suggests that children with different trait-like diatheses may differ in their performance or reactions to the same environmental challenge, leading to differences in their stuttering behaviors. For example, CWS with poor speech-language abilities are less capable of handling increased linguistic demand, leading to more speech disruption. However, the increased linguistic demand may impose less or no influence on CWS with good speech-language abilities; thus, fewer or no changes may be found in their stuttering behaviors. 18

With regard to the influence of increased emotional stress, the DD-S emphasized the role of emotional regulation in affecting changes of stuttering instances. Increased emotional stress can elicit high emotional reactivity, especially for some CWS with more reactive temperament traits. However, CWS with good emotional regulation can easily reduce the increased emotional reactivity without taxing the resource for speech-language processing, maintaining fluent speech. In contrast, CWS with low emotional regulation may tax more resources in order to reduce the increased emotional reactivity, resulting in more speech disruptions.

1.2.1.2 Empirical findings Empirical findings have indicated that CWS may possess certain temperamental characteristics (such as the tendency to be sensitive or reactive, easily frustrated and anxious, and poor at regulating negative emotions) that can contribute to influencing the development of stuttering (e.g., Anderson et al., 2003; Eggers et al., 2010; Karrass et al., 2006). Most of the evidence comes from parent-report questionnaires, such as the children’s behavior questionnaire (CBQ; Rothbart et al., 2001) and the behavior style questionnaire (BSQ; McDevitt & Carey, 1978). Eggers, Nil, and Van den Bergh (2010) assessed temperamental characteristics of CWS and CWNS using CBQ. Of the three high-level temperamental dimensions assessed by CBQ (Surgency, Negative Affect, and Effortful Control), they reported that there were significant differences between CWS and CWNS in the dimensions of Negative Affect and Effortful Control; compared to CWNS, CWS scored significantly higher on Negative Affect and lower on Effortful Control. Moreover, by comparing the scales under these three high-level temperamental dimensions, they found that in comparison with CWNS, CWS had significantly higher scores on the scales of Approach and Motor Activation under Surgency, and on the scale of Anger/Frustration under Negative Affectivity, and 19 that they had significantly lower scores on the scales of Inhibitory Control and Attentional Shifting under Effortful Control. Given that high negative emotional reactivity and low effortful control are considered to predispose the onset, maintenance, and development of emotional and behavioral problems or disorders (Muris & Ollendick, 2005), Eggers et al. (2010) proposed that these specific temperamental characteristics, high negative affectivity and low effortful control, also played a role in the development of stuttering. Similar results were found in the study of Eggers, Nil, and Van den Bergh (2009). Further, based on the comparisons of the temperamental dimensions and scales, they concluded that CWS tend to have heightened reactivity (i.e., higher on the dimension of Negative Affect and on the scales of Approach, Motor Activation, and Anger/Frustration) and limited abilities of self-regulation (i.e., lower on the dimension of Effortful Control and the scales of Inhibitory Control and Attentional Shifting). This finding is in line with Embrechts et al. (2000, cited in Eggers et al., 2009). In addition, two explanations were provided. First, given the association between inhibitory control and aggressive behavior constraining (e.g., Dennis & Brotman, 2003; Muris, 2006; Muris et al., 2004), they suggested that CWS scored higher on Approach and Motor Activation because of their difficulty in inhibiting their aggressive behaviors (i.e., approach) and motor movement (i.e., eye blinking and increased muscle tension). Second, given that attentional and inhibitory control are important functions in the executive attention network (as discussed in Section 1.1.3), Eggers et al. (2010) indicated that the low scores on Attentional Shifting and Inhibitory Control suggested that CWS had more difficulty in executive attention control, including the abilities of inhibiting unwanted information, flexibly shifting attention between two tasks, detecting errors, and making plans. Other results regarding the temperamental characteristics of CWS have been reported in studies using the BSQ, which assesses nine temperamental dimensions, including Activity Level, Rhythmicity, Approach or Withdrawal, Adaptability, Threshold 20 of Responsiveness, Intensity of Reaction, Quality of Mood, Distractibility, and Attention Span/Persistence (McDevitt & Carey, 1978). Anderson, Pellowski, Conture, and Kelly (2003) reported that CWS, compared to CWNS, scored lower on Adaptability and Distractibility. They suggested that low adaptability in combination with low distractibility might suggest that CWS, compared to CWNS, are less able to adapt to novel or different situations, and less flexible in switching their attention when concentrating on a task. Thus, when confronting a speech disruption, CWS may tend to stay longer at the stuttering moment, resulting in more struggle and physical tension. Karrass et al. (2006) selected and clustered different BSQ items into three new temperament dimensions: Emotional Reactivity, Emotional Regulation, and Attentional Regulation/Control. They reported that CWS, compared to their fluent peers, tended to exhibit higher emotional reactivity and lower emotional and attentional regulation. Further, to determine if the new temperament dimensions were valid measures, correlations between these three new temperament dimensions and dimensions from the CBQ were conducted. Karrass et al. reported that BSQ emotional reactivity correlated with the CBQ dimension of negative affectivity (r = .42; p < .05), BSQ emotional regulation correlated with the CBQ scale of falling reactivity/soothability (r = .37; p < .05), and BSQ attentional regulation/control correlated with the CBQ scale of attention shifting (r = .67; p < .001; in Karrass et al., 2006). The results indicated that these three new dimensions were valid measures. In addition, given the correlation data, the findings from the CBQ and BSQ can be integrated. The results from both the CBQ and BSQ indicate that CWS as a group, compared to CWNS as a group, tend to possess high reactivity (i.e., high scores on Negative Affect and Surgency) and low self-regulation (i.e., slow adaptability, inefficient inhibitory control, and emotional and attentional regulation), which may influence the development of stuttering. Results from behavioral experiments also provide evidence of the relation between emotional variables and stuttering. Johnson, Walden, Conture, and Karrass 21

(2010) assessed children’s emotional nonverbal expressions (positive or negative) after the children received a desirable gift and a disappointing gift. Results indicated that CWS and CWNS did not differ in the amount of positive emotional expressions after receiving a desirable gift. However, they were significantly different in their amount of negative emotional expressions after receiving a disappointing gift: CWS exhibited more negative emotional expressions compared to CWNS. Such results again support the notion of high negative emotional reactivity and poor emotional regulation. Johnson et al. also suggest that CWS may have low attentional control, given that it is primarily a strategy of emotional regulation. Further, the high negative emotional reactivity may be due to decreased use of regulatory strategies, resulting in more stuttering instances (Arnold et al., 2011). The study by Arnold, Conture, Key, & Walden (2011) assessed the frequency of using regulatory strategies (i.e., voluntary shifting of attention away from a stimulus to another in the environment) when subjects listened to brief background conversations that conveyed happy, angry, and neutral emotions. In addition, electroencephalograms (EEG) were recorded to examine cortical correlations with emotional reactivity and regulation. A narrative was collected immediately after each background conversation, and speech disfluencies were measured. Arnold et al. reported that no significant differences were found in EEG measurements; however, the behavioral results showed that the stuttering frequency was negatively correlated with the duration and frequency of the use of regulatory strategies, suggesting that CWS who use fewer regulatory strategies may tend to exhibit more speech disfluencies. Based on the results, they also suggested that CWS’s emotional regulation, such as attentional control, may be the primary contributor to their stuttering development. For example, when confronted with speech disruptions, CWS with good attentional control can easily shift their attention away from the speech disruptions and continue to produce relatively smooth speech. However, CWS with poor attentional control may tend to fixate on the speech disruptions, leading to increased 22 negative emotional reactivity and further instances of stuttering (Conture et al., 2006; Karass et al., 2006). Furthermore, researchers also speculate that the cascade effect between increased negative emotional reactivity and more stuttering may be due to the inefficiency of attentional allocation (Arnold et al., 2011; Johnson et al., 2010). Decreased emotional regulation can result in increased negative emotional reactivity. Constraining the increased negative emotional reactivity may become a demanding task that can occupy more attentional resources, which are limited. Diverting attentional resources that are required for speech-language planning and production will be harmful to speech fluency, especially for individuals with stuttering (Arnold et al., 2011). Research findings have indicated that individuals who stutter may have vulnerable speech-language planning and production systems (i.e., less efficiency in phonological processing; Anderson, Wagovich, & Hall, 2006; Paden, Yairi, & Ambrose, 1999); therefore, they may need more attentional resources to efficiently plan and produce fluent speech. In addition, Bosshardt (2006) reported that adults who stutter tend to produce more speech disfluencies when performing concurrent, demanding cognitive tasks, indicating they have more difficulty in the allocation of attentional resources. Planning and producing fluent speech and constraining increased negative emotional reactivity can be considered two demanding cognitive tasks; therefore, CWS may have difficulty doing these two tasks simultaneously, leading to more stuttering instances (Arnold et al., 2011;

Johnson et al., 2010).

1.2.1.3 Summary To conclude, the theoretical models and empirical findings from the parent-report questionnaires and behavioral studies suggest that CWS possess specific temperament traits—high emotional reactivity in combination with low emotional regulation—that may influence their stuttering behaviors. In addition, these findings also indicate that CWS, compared to CWNS, tend to exhibit lower attentional control, which is an 23 important strategy in emotional regulation. The proficiency in attentional control may play a crucial role in changing stuttering behaviors. For CWS, the findings and speculations of attentional control in stuttering development—together with this perspective of the way that attentional control influences the emotional processes in anxious individuals (as discussed in Section 1.1.3)—suggest that a stuttering instance may initially act like a distractor, which can easily catch their attention and activate more negative emotional reactivity due to their temperamental traits. The negative emotion also activates many underlying negative concepts, given the effect of spreading activation. CWS with good attentional control can easily disengage their attention from the stuttering instance, which can effectively decrease their negative emotional reactivity. Thus, they can focus on the task of speaking, maintaining relatively fluent speech. However, CWS with poor attentional control may have difficulty disengaging from the stuttering instance. The initial activation of the negative emotion and concepts feed back to deepen the negative emotion, which can in turn feed back to activate more negative concepts related to this negative emotion. Once this vicious cycle is formed, the stuttering instance is no longer a distractor, and constraining the negative emotion elicited by the stuttering instance becomes a cognitive and demanding task. That is, children need to allocate their attention to two tasks: speaking and decreasing negative emotion. A dual task is difficult for people with poor attention allocation, such as individuals who stutter, leading to more stuttering instances. This negative experience—the connections between a stimulus (a stuttering instance), a negative emotion, negative concepts, and the resultant behavior (producing more stuttering)—can also feed back to influence a person’s cognitive appraisals, exacerbating the stuttering problem. However, the evidence is indirect; the abilities of attentional control were not directly examined in the studies of temperament questionnaires, which rely on parents’ beliefs and perceptions, and the aforementioned behavioral measurements, which focused 24 on the large dimension of emotional regulation rather than the specific strategy of attentional control. Based on these findings, it is uncertain whether or not CWS possess deficits in attentional control. Thus, more evidence from studies that directly examine the abilities of attentional control is required. The following section will specifically focus on studies that directly examine CWS’s abilities in attentional control.

1.2.2 Attentional control: Direct evidence from behavioral and electrophysiological studies Recently, researchers have adopted different behavioral tasks to directly measure and compare the performance of CWS and CWNS in attentional control. For example, Schwenk, Conture, and Walden (2007) compared the behavioral performances of CWS and CWNS in attention maintenance and reaction to background stimuli. They measured the frequency (the total number of attention shifts), duration (the latency of each shift), and reaction time (the speed of attention shifts) when children shifted their attention from a conversation with parents to the movement of a video camera (distractor). Results showed that CWS, compared to CWNS, performed significantly more attention shifts, although there were no significant differences between the groups in duration and reaction time of the shifts. Based on the results, Schwenk et al. indicated that CWS are more reactive to, more distractible to, and slow in adapting to changes in their environment. In addition, given their high distractibility, Schwenk et al. suggested that CWS differ from CWNS in their attentional control, especially attentional focusing. This finding of high distractibility in CWS is inconsistent with the study result from the BSQ conducted by Anderson et al. (2003). Eggers, De Nil, and Van Den Bergh (2012) assessed the efficiency of attentional control in CWS (age range: 4 to 9 years of age) using the attention network test (ANT; Fan, McCandliss, Sommer, Raz, & Posner, 2002), which measures three attentional networks, including alerting, orienting, and executive control networks (as discussed in 25

Section 1.1.3). The ANT is a target detection task that uses differences in RT between conditions to measure the efficiency of each network. Stimuli consist of a row of five horizontal black lines (one central arrow plus four flankers), with arrowheads pointing leftward or rightward. The target is the central arrow, which may point to the left or right side. The other four arrows, the flankers, point in the same direction. The direction of the four flankers may be the same (congruent condition) or different (incongruent condition) to that of the target arrow. Each trial begins either with a cue (cue condition) or a blank interval (no-cue condition). The cue may be in the center, to inform participants that a target will occur soon (the center cue), or may be above or below fixation, to inform participants where the target will occur (the spatial cue). The participants’ task is to identify the direction of the target by pressing a button with the index finger of the left hand for the left direction, and a button with the index finger of the right hand for the right direction. The efficiencies of three attention networks are computed by comparing the reaction time (RT) of one condition to an appropriate reference condition. Subtracting RTs in the no-cue condition from RTs in the central cue condition gives a measure of alerting because the center cue functions as a warning signal. Subtracting RTs to trials with a spatial cue condition from trials with a central cue provides a measure of orienting, given that a spatial cue provides valid information about where a target will appear, and people will move direct attention to the location of the spatial cue. Subtracting RTs for congruent target trials from incongruent target trials provides a measure of conflict resolution, which is the function of the executive attention network. Eggers et al. (2012) reported that CWS were less efficient in the orienting network compared to CWNS, whereas no differences were found in the alerting or executive control network. Such results again lend support for less efficiency of attentional control in CWS; in addition, these results indicate where the possible deficit lies: orienting. Orienting attention away from a stress-evoking situation can effectively reduce increased emotional arousal (Buss & Goldsmith, 1998). Given this, Eggers et al. (2012) suggest that CWS with low 26 efficiency in attentional orientation may have difficulty disengaging attention from distressing situations, leading to an increase in the already high emotional arousal. Inconsistent with the results of the study by Eggers et al. (2012), Johnson, Conture, and Walden (2012) reported no significant difference in attentional control between CWS and CWNS. Johnson et al. (2012) examined the speed of attentional orientation using the Traditional cueing task (e.g., Perez-Edgar & Fox, 2005; Perez-Edgar, Fox, Cohn, & Kovacs, 2006; Posner & Cohen, 1984) and the Affect cueing task (Perez-Edgar & Fox, 2005; Perez-Edgar et al., 2006). In the Traditional cueing task, either a valid or an invalid cue was presented prior to a stimulus. Responding to a stimulus preceded by a valid cue (i.e., the cue and target are in the same location) can result in faster RTs. In contrast, responding to a target stimulus preceded by an invalid cue (i.e., the cue and target are in different locations) results in slower RTs, given that more steps are required to respond (i.e., disengaging from the cued location, shifting attention from the cued location to another location, engaging attention to the new uncued location; Posner & Cohen, 1984; Posner, Inhoff, Friedrich, & Cohen, 1987). Children were instructed to press either the right or left button corresponding to the target location as quickly as possible. The procedure in the Affect cueing task was identical to the Traditional cueing task, except for the instructions. An emotion-influenced instruction was given to children, which specified that they had to do well to receive the prize they identified previously. The purpose of inclusion of this emotion-influenced instruction was to affect the children’s emotions prior to performance of the task, and investigate whether they performed differently in RTs under such an emotion-laden situation. It has been reported that young children tend to produce shorter RTs, more errors (omission and incorrect responses), and an increased difference in the RTs between trials with valid and invalid cues (validity difference; Perez-Edgar & Fox, 2005; Perez-Edgar et al., 2006). Johnson et al. (2012) reported that children in both groups exhibited shorter RTs for trials with valid cues than those with invalid cues. For both subject groups, emotion-influenced 27 instructions had no significant impact on RTs, but had a significant influence on the frequency of omission, in that an increased RT was positively correlated with an increased rate of omission. Based on the results, Johnson et al. (2012) suggested that preschool-aged CWS did not differ from their fluent peers in the speed of attentional disengaging, shifting, and re-engaging. They further proposed that the lack of significant difference between the groups might be due to the small sample size and methodology, for example, the task was easy for children at the age range in their study and/or the emotion-influenced instruction did not create sufficient emotional arousal. Thus, CWS experienced little or no challenge to their attentional control abilities in the emotion-laden situation (Johnson, Conture, & Walden, 2012). Attentional processes are sophisticated, and many stages may be involved in an attentional process; for example, there are three stages involved in attentional orienting, including disengaging attention from a location, shifting attention to a new location, and engaging attention to the new location (Posner & Petersen, 1990). Unfortunately, behavioral data are not capable of providing detailed information about the underlying steps with an attentional process. In addition, researchers have indicated that the data of behavioral RTs may not always be sensitive to effects at earlier stages of processing that precede motor preparation and execution; thus, an absence of differences in behavioral RTs does not mean that there is no difference in neural processes (Hopfinger & Parks,

2012). Sometimes, the effects on neural processes may be obscured or masked in behavioral measures. Therefore, it may be beneficial to adopt electrophysiological measures, such as the ERP method, which track moment-to-moment changes in neural activity, in order to fully understand the nature of attentional deficits in CWS. The ERP method provides a measure of neural activity that is elicited by a specific stimulus or cognitive event. Voltage deflections of an ERP recording reflect postsynaptic potentials, and the temporal order of these deflections is considered to reflect “the flow of information through the brain” (Luck, 2005). That is, these voltage 28 deflections reflect different steps within a cognitive process, such as a specific type of attentional process; this is helpful for us to understand the different steps underlying this cognitive process. Given this advantage, the ERP method has been used intensively to investigate the mechanisms of different types of attentional processes, such as selective attention and involuntary attentional control (e.g., Luck & Hillyard, 1994a, 1994b; Schröger & Wolff, 1998). Previous stuttering research has used the ERP method to investigate the differences between individuals who do and do not stutter in language processing and most of these studies focused on the population of adults who stutter (e.g., Cuadrado & Weber-Fox, 2003; Weber-Fox & Hampton, 2008a, 2008b; Weber-Fox et al., 2004). Only one study, conducted by Kaganovich et al. (2010), has adopted the ERP method to measure attentional control and working memory update in CWS. Kaganovich et al. (2010) examined working memory update and attention control in CWS and CWNS by using a particular ERP paradigm, called the auditory oddball paradigm. They focused on two ERP components: MMN, which is the index of the sensory/perceptual scanning, and P3, which refers to P3b and is the index of allocating attentional resources and updating working memory (Luck, 2005; Polich, 2007). They reported that: 1) no difference was found in the MMN between CWS and CWNS, and 2) only CWNS exhibited an increased P3b component to deviants. They suggested that

CWS may not have difficulty automatically comparing incoming stimuli to a sensory memory trace of preceding stimuli, given no MMN difference. That is, CWS do not have difficulty detecting and evaluating auditory changes. However, they may be less able to allocate attention and update working memory efficiently because of not exhibiting an increased P3b. The difference in P3 amplitude can also be found in adults who do and do not stutter (Weber-Fox & Hampton, 2008a). Therefore, the authors indicated that the absence of increased P3b to deviants in preschoolers and adults may reflect an inherent 29 weakness of the brain function in stuttering instead of an acquired change, which is attributable to years of compensation for this disorder. In conclusion, the findings of the aforementioned behavioral studies have indicated that CWS may differ from CWNS in attentional control. The differences can be in: 1) attentional focusing, which was reflected by a high frequency in attentional shifting, indicating high distractibility in CWS (Schwenk et al., 2007); and 2) attentional orienting, which was reflected by increased reaction time when shifting attention from the fixation to the spatial cue (Eggers et al., 2012). The results from Kaganovich et al. (2010) also lend support to less efficiency in attentional control in CWS, compared to CWNS. However, as mentioned previously, attentional control is a complicated process and many stages may be involved in the process of attentional control (Posner & Petersen, 1990). To understand how CWS differ from their fluent peers during the temporal process of attentional control, it would be necessary to include more ERP components that reflect different stages along the entire process of attentional control. The present study specifically focused on attentional control during the process of distraction, given that many research findings have indicated that CWS are more reactive, less adaptable to, and easily distracted by environmental changes; as well as the speculation that CWS may tend to fixate on and have difficulty disengaging attention from a stuttering instance. The specific aim of this study was to investigate how CWS respond when confronting a task-irrelevant distractor. In the following sections, the process of distraction will be discussed, and then two ERP tasks, the auditory-auditory distraction task and the visual search task, will be introduced. These two ERP tasks have been used to examine the different mechanisms and stages underlying the situation—confronting with a distractor.

1.3 The Processing of Distraction and ERP Tasks

1.3.1 The processing of distraction Distraction is defined as the process of involuntarily directing one’s attention away from a current task to exogenous stimuli from the environment, which is followed by a cognitive-level control of voluntarily directing attention back to the current task (Escera et al., 2000; Hopfinger & Parks, 2012). In other words, two mechanisms are involved in distraction. The first mechanism is sensory-driven and focused on sensory/perceptual processing, whereas the second mechanism is goal-directed and contingent upon a top-down task requirement (cognitive control after distraction; Hopfinger & Ries, 2005). In an evolutionary sense, orienting attention towards unexpected task-irrelevant stimuli from the environment is advantageous because these stimuli may carry information crucial for survival. However, distracting one’s attention from a current task to task-irrelevant stimuli usually results in a temporary deterioration of the task performance. Thus, a good balance between the involuntary orientation and maintenance of goal-directed behavior is essential for ensuring adequate behavior in daily situations (Escera et al., 2000; Hopfinger & Parks, 2012). The processing of distraction includes three stages: 1) automatically detecting and evaluating extraneous stimuli, 2) involuntary orienting of attention to the extraneous stimuli, and 3) voluntary reorienting of attention to the current task (Näätänen, 1992; Escera et al., 2000). Each stage is reflected by a specific ERP component, which will be discussed in Section 1.3.2. Despite the three stages of attentional control along the distraction processing that are mentioned above, some researchers also focus on a different stage, a filtering stage that weakens distractor input during visual search, which is reflected by a specific ERP component: N2pc (e.g., Luck & Hillyard, 1994a, 1994b; Woodman & Luck, 2003). The stage of filtering out distractors, called attentional filtering, lies within the sensory/perceptual processing (Luck & Hillyard, 1994a, 1994b; 31

Luck, Girelli, McDermott, & Ford, 1997; Woodman & Luck, 2003). Its purpose is to attenuate irrelevant information and allow only relevant information to enter the cognitive processing system, given that the capacity of the human cognitive system is limited (Hopfinger & Parks, 2012; Luck & Hillyard, 1994a). These four stages in the processing of distraction are operated by different mechanisms. The first mechanism, sensory/perceptual processing, includes the stages of surveying exogenous stimuli, orienting attention to a distractor, and attentional filtering (Escera et al., 2000; Hopfinger & Parks, 2012). The second mechanism is a goal-directed processing, that is, cognitive control after distraction. This mechanism includes the stage of disengaging attention from the distractor and reorienting attention to a current task (Escera et al., 2000). The degree of distraction varies across individuals. The differences in distraction between more and less distractible individuals can come from two possibilities: 1) having a low threshold of being caught by exogenous stimuli, which indicates a deficit in the first mechanism, and 2) having deficient control in counteracting the initial orienting to distractors, which indicates a deficit in the second mechanism (Hopfinger & Parks, 2012). Kim and Hopfinger (2010) investigated the differences in distractibility in subjects with normal attentional control using functional magnetic resonance imaging (fMRI). Those subjects were classified into two groups: more and less distractible subject groups. The results showed that these two subject groups significantly differed in the magnitude of the neural activity in the intraparietal sulcus (IPS) and the temporoparietal junction (TPJ), which are responsible for registering distractors, disengaging attention from the distractors, and reorienting attention to a current task (Corbera, Kincade, Lewis, Snyder, & Sapir, 2005; Corbetta, Kincade, & Shulman, 2002). That is, the degree of distractibility was more associated with deficient control in counteracting the initial orienting to distractors (operated by the second mechanism of the distraction process) instead of with increased reactivity to distractors (operated by the first mechanism of the distraction process). 32

Some ERP tasks have been adopted to assess the process of distraction, such as the auditory-auditory distraction task (Schröger & Wolff, 1998) and the visual search task (Luck & Hillyard, 1994a, 1994b). These two tasks focus on different ERP components that reflect different stages during the distraction processing. The auditory-auditory distraction task focuses on the stages of automatically detecting and evaluating extraneous stimuli, orienting attention to the extraneous stimuli, and reorienting attention to the current task, whereas the visual search task focuses on the stage of attentional filtering.

1.3.2 The auditory-auditory distraction task The auditory-auditory distraction task, which was developed by Schröger and Wolff in 1998, has been used to investigate the processing of auditory distraction. In this task, subjects are asked to perform a duration discrimination task and are instructed to focus on a particular dimension of the auditory stimuli (e.g., duration), while ignoring any other aspect of the auditory stimuli (e.g., frequency). In this case, the duration is referred to as task relevant, whereas the frequency is referred to as task irrelevant. Subjects are instructed to distinguish between long and short tones by pressing related response buttons. The changes in the task-irrelevant aspect of the auditory stimuli (the frequency change) can yield a behavioral cost in the performance of the duration discrimination task: increased RT, decreased HR, and increased FA (when using a go/no-go method). RT refers to the interval between the presentation of a stimulus and the detection of a response, whereas HR refers to the frequency of the correct identification of long versus short tones. The rate of FA refers to the frequency of erroneous responses to no-go trials. There are three cortical potentials related to the processing of distraction: MMN, P3a, and RON, which are defined as distraction potentials (DPs; Schröger & Wolff, 1998). These components index the different stages of attentional control when 33 distractors are present. The process includes three stages: 1) automatically scanning and detecting changes from the environment, which is reflected by MMN; 2) the involuntary orienting of attention to changes or distractors, which is reflected by P3a; and 3) reorienting attention to task-relevant information, which is reflected by RON (Escera et al., 2000; Schröger & Wolff, 1998). MMN is a negative-going wave that is largest at fronto-central scalp sites. It peaks between 100 and 250 ms after stimulus onset (Näätänen, 1992). MMN is elicited by a mismatch between an incoming stimulus and the memory trace representing the regularity of the preceding stimulus sequence (Escera et al., 2000; Näätänen, 1992). In addition, MMN can be elicited even if individuals do not attend to the stimuli, and it is considered a pre-attentive change detection mechanism (sensory/perceptual processing; Näätänen, 1992). The MMN is composed of overlapping contributions from the auditory and frontal cortices (Deouell, 2007; Giard et al., 1990). The existence of the frontal generator supports the role of MMN in attentional control (Escera et al., 2000; Näätänen et al., 2012). Given that MMN is usually followed by P3a (discussed below), Näätänen (1990) suggested that the process indexed by MMN can initiate attention switching. The MMN have been considered an important index of abnormal involuntary attention; both too weak and strong MMN responses reflect deficits in the automatic scanning and change detecting system (Näätänen et al., 2012). Research has indicated that attenuated MMN can be observed in individuals with decreased vigilance, such as patients with schizophrenia (e.g., Matthews, Todd, Budd, Cooper, & Michie, 2007) and individuals with acute alcohol intoxication (e.g., Jaaskelainen, Pekkonen, Hirvonen, Sillanaukee, & Näätänen,1996; Jaaskelainen, Schröger, & Näätänen, 1999), as well as in individuals with immature ability in their automatic scanning and change detection systems (Wetzel, Widmann, Berti, & Schröger, 2006). On the other hand, increased amplitude for MMN can be found in individuals with high distractibility, such as patients with closed head injury (e.g., Kaipio et al., 1999; Kaipio et al., 2000). 34

The component followed by MMN is the P3a, which is one of the components in the P300 family. There are two components in the P300 family: P3a and P3b. P3a is elicited by task-irrelevant stimuli and generated in the fronto-central cortical areas, whereas P3b (P3) is elicited by task-relevant stimuli and generated in the fronto-parietal cortical areas (Luck, 2005). P3a is a positive-going brain wave and peaks around 300 ms after stimulus onset (Wetzel et al., 2006). It reflects the process of involuntary attention switching towards distractive events (Berti & Schröger, 2003). Increased P3a amplitude has been linked to impairment in the behavioral performance for the task at hand, as evidenced by increased response time and/or decreased accuracy in adults and children (e.g., Gumenyuk et al., 2001; Gumenyuk, Korzyukov, alho, Escera, & Näätänen, 2004; Schröger & Wolff, 1998; Schröger, Giard, & Wolff, 2000; Wetzel et al., 2006). Research has shown that distractible individuals, such as children with ADHD, exhibit increased P3a compared to their normal peers (van Mourik, Oosterlaan, Heslenfeld, Konig, & Sergeant, 2007). Subsequent to P3a, RON will be elicited, which is a frontal negative-going wave and peaks around 400–600 ms after the onset of deviant stimuli (Schröger et al., 2000; Schröger & Wolff, 1998). Although the functional significance of RON is not yet completely understood, researchers have indicated that RON reflects the capability of voluntarily redirecting attention to task-relevant information after distraction. It indexes the mechanism of goal-directed processing for distraction compensation. (Escera et al., 2000; Schröger & Wolff, 1998; Schröger et al., 2000; Wetzel et al., 2006). Schröger, Giard, & Wolff (2000) suggested that the RON response may reflect the activation of the prefrontal cortex controlling the direction of attention to task-relevant information at the working memory level. Working memory is a system for retaining and processing information, and the access to working memory is attention (Oberauer, 2002; Oberauer & Hein, 2012). In other words, bringing information into the focus of attention enables it to be available for processing in the working memory. After distraction by deviant 35 frequency change, reorienting attention back to the information of tone duration is necessary for evaluation of task-relevant information and performing the task at hand (Berti, 2008; Berti & Schröger, 2004). Research has shown that young children exhibit an increased amplitude for RON, which suggests that more effort is required to direct attention back to the task-relevant information after a momentary distraction (Gumenyuk et al., 2001; Gumenyuk et al., 2004). Moreover, Horváth, Czigler, Birkás, Winkler, and Gervai (2009) reported that, compared to young adults, preschool-age children and the elderly exhibit increased latency in RON, suggesting less efficient triggering of the attentional reorientation process.

1.3.3 The visual search task Attention filtering and selection have been extensively studied in visual attention literature, and a specific ERP component, N2pc, has been identified as an indicator of attentional selectivity (e.g., Eimer, 1996; Luck & Hillyard, 1994a). The N2pc component is a negative-going deflection contralateral to an attended stimulus, peaking around 200 and 300 ms post-stimulus (Luck, 2005; Luck & Hillyard, 1994a) and generated in the lateral occipitotemporal cortex (Hopf et al., 2000). In other words, the ERP waveform in the left hemisphere becomes more negative when attention is directed to a stimulus in the right hemisphere (contralateral) rather than one in the left hemisphere (ipsilateral), and vice versa. This component reflects attentional selectivity, which is a covert perceptual attention mechanism that operates before awareness and working memory encoding (early in the course of visual processing; Woodman & Luck, 2003). Specifically, the N2pc indexes the ability of focusing on the target location and suppressing or filtering the surrounding nontarget items (Eimer, 1996; Luck, Fan, & Hillyard, 1993; Luck & Hillyard, 1994a). 36

There are two advantages of using N2pc. First, because the index function of the N2pc component has been extensively studied, the empirical evidence supporting it is very robust (e.g., Eimer, 1996; Luck & Hillyard, 1994a, 1994b; Kiss, van Velzen, & Eimer, 2008; Woodman & Luck, 2003). Second, the contralateral scalp distribution of the N2pc allows it to be isolated from the rest of the ERP waveforms by computing difference waves in which the response at a given electrode for an ipsilateral target is subtracted from the response at that electrode for a contralateral target (Luck, 2005). Therefore, the influence of overlapping with other bilateral ERP components that are unrelated to selective attention will be eliminated, yielding a highly precise means of measuring the deployment of covert perceptual attention (Luck et al., 2006). One of the ERP experimental techniques that has been used extensively to study visual attention and the index function of N2pc is the visual search task (e.g., Eimer, 1996; Kiss et al., 2008; Luck et al., 1993; Luck & Hillyard, 1994a, 1994b; Mazza, Turatto, & Caramazza, 2009). In visual search tasks, an array of stimuli is presented, and participants have to report the presence or absence of a specified target within the array, irrespective of its location (a target detection task). Behavioral data, including RT and HR, and ERP data, including the amplitude and latency of N2pc, are collected. The N2pc component has been observed in response to targets that are embedded within displays containing nontargets, which may need to be filtered in order to correctly determine the target. For example, a target letter T is surrounded by nontarget letters, such as Es and Ls. The N2pc reflects a process of pulling out the signal of T and filtering out the signals arising from the distractors, Es and Ls. The amplitude and latency represent the effectiveness of attentional filtering; decreases in attentional filtering are reflected by small and delayed N2pc components (Kiss et al., 2007; Luck et al., 2006; An, Sun, Wang, Ding, & Song, 2012).

1.4 Statement of Purpose and Research Questions

1.4.1 Statement of purpose The purpose of the present study was to investigate the difference in the distraction process between CWS and CWNS using two ERP tasks: the auditory-auditory distraction task (Schröger and Wolff, 1998) and the visual search task (Luck & Hillyard, 1994b). Both behavioral (RT, HR and false alarm: FA) and electrophysiological data (MMN, P3a, RON, N2pc) were collected, analyzed, and compared to measure the degree of distraction in these two participant groups and determine the possible causes of the differences in distractibility, that is, the deficits in the sensory-level processing (MMN, P3a, N2pc) or in the cognitive control after distraction (RON). In addition, temperament data were collected from parent-report temperament questionnaires and then correlated with behavioral and ERP data in order to investigate whether there were relationships between them. Adopting the ERP method to investigate the distraction processing has some advantages. Evidence from temperament questionnaires and behavioral studies has indicated that the attentional deficits of CWS may be in attentional focusing and/or attentional shifting. However, these findings cannot fully explain the nature of the attentional deficits in CWS, given that attentional control is sophisticated and always involves many neural mechanisms. The ERP method, which directly measures neural activity and tracks moment-to-moment changes at a small-time scale, is well suited to investigate the fast-acting effects of involuntary attentional control. This method has been utilized intensively in attention-related studies to investigate the nature of the attentional process (e.g., Luck & Hillyard, 1994a, 1994b) and attentional deficits in different populations, such as those with ADHD (e.g., van Mourik et al., 2007). Therefore, using the ERP method can help us understand the nature of the inefficiency of attentional control in CWS, and specifically, the possible causes of the high distractibility in CWS. 38

Knowing the nature of the deficits in attentional control during the process of distraction in CWS is important. The skills adopted in controlling distraction, such as attention disengagement and attentional focusing, have been considered to be associated with constraining negative emotions. Less efficiency in constraining negative emotions may exacerbate stuttering behaviors, such as increased stuttering severity (e.g., Arnold et al., 2011; Conture & Walden, 2012; Conture et al., 2006). However, at the present time, most explanations concerning the role of attentional control in the development of stuttering are speculations based on the indirect evidence, such as results from temperament questionnaire studies. To understand the influence of attentional control in stuttering, it is necessary to know the nature of the low efficiency or efficacy in attentional control for CWS. Based on the review of the literature, the working hypotheses in the present study are: 1. Compared to CWNS, CWS will perform increased RTs, decreased HRs and increased FAs in the duration of the discrimination task of the auditory-auditory distraction task, as well as in the target detection task in the visual search task. 2. Compared to CWNS, CWS will perform increased amplitude in MMN, as well as increased amplitude in P3a, given that CWS are more reactive to environmental changes.

3. Compared to CWNS, CWS will perform increased amplitude and/or delayed latency in RON, given that CWS have more difficulty in reorienting attention from a distractor to a current task. 4. Compared to CWNS, CWS will perform decreased amplitude and/or delayed latency in N2pc, given the high distractibility. 5. Children’s behavioral and electrophysiological responses will be associated with their temperament traits.

1.4.2 Research questions The present investigation addressed the following questions: 1. Are there differences in behavioral data in the two subject groups? a) Do CWS differ from CWNS in the RT, HR and/or FA of the duration of the discrimination task of the auditory-auditory distraction paradigm? b) Do CWS differ from CWNS in the RT, HR and/or FA of the target detection task in the visual search task? 2. Are there differences at the four stages of involuntary attentional control (the process of distraction)? a) Do CWS differ from CWNS in the amplitudes and latencies of MMN, P3a, and RON in the auditory-auditory distraction paradigm? b) Do CWS differ from CWNS in the amplitude and latency of N2pc in the visual search task? 3. What are the possible causes of the differences in distractibility from the deficits in the sensory/perceptual processing or the cognitive control after distraction? 4. What are the relationships between temperament traits and behavioral and behavioral and ERP data?

CHAPTER 2 METHODS

2.1 Participants Participants for this study were eight school-age CWS (six boys and two girls) and eight school-age CWNS (six boys and two girls). Participants were between the ages of eight to twelve years (years; months; CWS: M = 10;3, SD = 5.73 mos; CWNS: M = 10;1, SD = 5.29 mos) with no significant between-group difference in age, t (14) = .144, p = .89, d = .212. A matched-subjects design was used; children were matched by age, gender, and handedness. All 16 children were paid volunteers. The CWNS were recruited from the areas surrounding Iowa City, while the CWS were recruited from the University of Iowa’s Wendell Johnson Speech and Hearing Clinic and the community of Iowa City. The protocol for this study was approved by the Institutional Review Board of the University of Iowa, Iowa City, Iowa. The experimental tasks and study procedures were described to the children and parents prior to consent. A verbal assent from each child, and a signed informed consent from the parent, was obtained prior to participation in this study.

2.2 Subject Recruitment Criteria and Screening Tests

2.2.1 Subject recruitment criteria The recruitment criteria for CWS were: (a) they produced at least three within-word or stuttering-like disfluencies, including part-word repetitions, single-syllable word repetitions, sound prolongations and blocks, per 100 words; (b) they were regarded by parents, teachers, or caregivers as having a stuttering problem; and (c) apart from stuttering, they did not exhibit other language and speech problems, or hearing, neurological, or intellectual abnormalities (Conture, 2001; Yairi & Ambrose, 41

1992). The recruitment criteria for CWNS were: (a) they produced two or fewer within-word disfluencies per 100 words; (b) they were not regarded by parents, teachers, or caregivers as having a stuttering problem; and (c) they did not stutter or have other language and speech problems, or hearing, neurological, or intellectual abnormalities (Conture, 2001; Yairi & Ambrose, 1992). To determine eligibility, a hearing screening and a formal language test (Test of Language Development-Intermediate: 3rd edition; TOLD-I:3; Hamill & Newcomer, 1997) were administered to each child. A 300-word conversational sample was also collected and analyzed for frequency of within-word disfluencies. Stuttering severity was measured for CWS using the Stuttering Severity Instrument-IV (SSI-4; Riley, 2009).

2.2.2 Screening tests 2.2.2.1 Stuttering CWS. Children who were classified as CWS exhibited three or more within-word disfluencies per 100 words (M = 8.25, SD = 4.59) and received a severity score of 15 or higher on the SSI-4 (M = 20.75, SD = 4.86). The parents in this group all expressed concern about their child’s stuttering. All of the CWS, besides one child, had received treatment for stuttering in school or a clinic before participating in this study. CWNS. Children who were classified as CWNS exhibited two or fewer within-word disfluencies per 100 words (M = 1.03, SD = .53). None of their parents expressed concern about their speech fluency.

2.2.2.2 Language screening To participate in this study, children had to exhibit language scores within the normal range (a standard score equal to or higher than 85) on the TOLD-3; otherwise they were excluded from this study to ensure that all participants had normal receptive and expressive language skills. No significant difference was found in overall language 42 skills between these two groups, t (14) = -1.79, p = .1, d = .9 (Mean and standard deviation of Spoken Language Quotient; CWS: M = 101.5, SD = 16.84; CWNS: M = 114.63, SD = 12.09).

2.2.2.3 Hearing screening All participants passed a hearing screening at a level of 20 dB HL at 500, 1,000, 2,000, 4,000, and 6,000 HZ and had normal or corrected-to-normal vision per parent report.

2.2.2.4 Handedness An abbreviated handedness inventory (5 tasks adapted from Oldfield, 1971) was used to confirm the hand preferences reported by children and their parents. One participant in the CWS group and one in the CWNS group were left-handed.

2.3 Procedure This study consisted of two visits. During the first visit, the general procedure was introduced to each child and parent prior to consent. All children and their parents were encouraged to ask questions throughout the course of this study. After parents completed consent forms, children received screening tests to determine their eligibility to participate. Once eligibility was determined, parents completed forms concerning the child’s developmental, medical, and stuttering history (for CWS only). In addition, parents or caregivers completed either the Temperament in Middle Childhood Questionnaire (TMCQ; Simonds & Rothbart, 2004) or the Early Adolescence Temperament Questionnaire-Revised (EATQ-R; Ellis & Rothbart, 2001). Parents of children between 8 and 10 years of age completed the TMCQ, whereas parents of children between 11 and 12 years of age completed the EATQ-R. 43

The second visit involved two ERP tasks: an auditory-auditory distraction task and a visual search task. Prior to data collection, an electrode cap with 30 scalp electrodes (Figure B1) was placed on each child’s head by the investigator. After appropriate impedance levels were obtained (below 5 kOhms) for all electrodes, the children were seated comfortably in a quiet room with the experimenter monitoring the experiment’s procedure and children’s behavior from the adjacent room. Instructions for the experimental procedure were introduced to children before each ERP task. A trained research assistant was seated in the room with younger children to direct their attention to the stimuli, as well as to monitor their behavior in order to reduce movement artifacts in the data. Presentation version 15.01 (Neurobehavioral Systems, Inc. NBS) was used to control the experimental procedure and ERP data recording. The auditory-auditory distraction task was conducted first, followed by the visual search task. Detailed descriptions of both tasks, including relevant stimuli, will be presented in Sections 2.4.3 and 2.4.4. All children received two small toys and $20 compensation upon completion of the study.

2.4 Data Collection

2.4.1 Temperament questionnaires The TMCQ and EATQ-R were chosen for use in this study. Both temperament questionnaires were developed by Rothbart and her colleagues, and based on the same temperamental constructs (see Section 1.1.1). Both the TMCQ and EATQ-R have two versions: a self-report version and a parent-report version. Only the parent-report version of each questionnaire was used in this study. The TMCQ is designed for children aged seven to ten years. The TMCQ parent-report version consists of 157 items across 16 scales that comprise temperament: 44

Activity Level, High Intensity Pleasure, Impulsivity, Shyness, Attention, Inhibitional Control, Low Intensity Pleasure, Perpetual Sensitivity, Anger/ Frustration, Discomfort, Fear, Sadness, and Soothability. These 16 scales are collapsed into three high-level dimensions or factors of temperament: Surgency (consisting of Activity Level, High Intensity Pleasure, Impulsivity, and Shyness scales), Negative Affectivity (consisting of Anger/Frustration, Discomfort, Fear, Sadness, and Soothability scales), and Effortful Control (consisting of Attention, Inhibitory Control, Low Intensity Pleasure, and Perceptual Sensitivity scales; Simonds & Rothbart, 2004). Internal consistency reliability for the TMCQ has been established with data from 95 parents; the Cronbach’s alpha ranged from .63 to .90, indicating a high degree of reliability (Simonds & Rothbart, 2004). The EATQ-R is a questionnaire designed to assess temperament characteristics of young adolescents between 10 and 15 years of age. It consists of 62 items across 10 scales: Surgency, Fear, Shyness, Attention, Inhibitory Control, Activation Control, Frustration, Depressive Mood, Aggression and Affilliativeness. Exploratory factor analyses have shown that these 10 scales can be collapsed into four high-level dimensions: Surgency (Surgency, Fear, and Shyness scales), Negative Affect (Frustration, Depressive Mood, and Aggression scales), Effortful Control (Attention, Inhibitory Control, and Activation Control scales), and Affiliativeness (Affiliativeness; Ellis, 2002).

The EATQ-R is a reliable tool; the internal consistency for the scales has been established with data from 62 parents, with coefficient alphas ranging from .65 to .86 (Ellis, 2002). Both the TMCQ and the EQTQ-R use a 1 to 5 Likert scale to describe the frequency with which parents observe a certain behavior in their child, with 1 being “almost always untrue” and 5 “almost always true.” In addition, parents have a choice of “does not apply” for each item.

2.4.2 ERP data acquisition Scalp electrical activity during both the auditory-auditory and visual search task was recorded with 36 Ag-Cl electrodes sewn into in an elastic cap (Quick-cap). Thirty scalp electrodes were positioned over homologous locations across the two hemispheres based on the International 10-20 system (American Electroencephalographic Society, 1994; see Figure B1). Locations were as follows: midline sites FZ, FCZ, CZ, CPZ, PZ, and OZ; midlateral sites FP1/FP2, F3/F4, FC3/FC4, C3/C4, CP3/CP4, P3/P4, and O1/O2; and lateral sites F7/F8, FT7/FT8, T7/T8, TP7/TP8, and P7/P8. EEG activity was referenced to physically linked mastoids (M1 and M2). Horizontal eye movements were monitored via electrodes placed over the left and right outer canthi, while vertical eye movements were monitored via electrodes placed over the inferior and superior orbital ridges. All electrode impedances were kept below 5 kOhms. The electrical signals were amplified within a bandpass of 0.1 and 100 Hz, and digitized online (Neuroscan 4.4) at a rate of 500 Hz.

2.4.3 ERP stimuli and procedure: The auditory-auditory distraction task An auditory-auditory distraction task was used in this study. The task consisted of both duration discrimination and task-irrelevant frequency change (e.g., Berti &

Schröger, 2003; Schröger & Wolff, 1998; see Figure B2). The task-relevant variable was duration, and the task-irrelevant variable was frequency. These variables occurred in two types of stimuli used in this study: frequent standard stimuli (90% probability of occurrence) and frequency-changed deviant stimuli (10% probability of occurrence). The frequent standard stimuli consisted of a 1000 Hz pure tone in two durations: 400 ms (long) and 200 ms (short). The deviant stimuli consisted of either a 900 or 1100 Hz pure tone in two durations, 400 ms (long) and 200 ms (short). Half of the stimuli in both conditions were long and half were short. To prevent children from familiarizing 46 themselves with the deviant stimuli and reducing the effect of distraction, the sequence of stimulus presentation was pseudo-randomized; a deviant stimulus was always followed by three standard stimuli. The experiment consisted of 10 blocks. Each block contained 60 tones. As shown in Figure B2, tones were presented every 1800 ms, including a 5 ms rise-and-fall time. Additionally, all tones were presented binaurally at 60 dB SPL through headphones. Children were instructed to look at a fixed point on a computer monitor and to listen to the tones. They were further instructed to press a specific response button when they heard “long sounds” (i.e., 400 ms) for long tones only (i.e., a go/no-go task) with their preferred hand, as fast and as accurately as possible. Every child performed a training session consisting of short and long standard tones in order to practice this duration discrimination task. The percentage of correct responses (pressing the button for long tones and not pressing the button for short tones / the total number of presentations) was calculated, and the training session was stopped when the percentage of correct responses reached 80%. After the training session, participants were also encouraged to ask questions about the task and stimuli. The experiment was started when they had no further questions and indicated that they were ready to begin. A total of 600 tones were presented in 10 blocks. Between every two consecutive blocks, a picture appeared on the monitor signaling a short break (2–3 minutes).

2.4.4 ERP stimuli and procedure: The visual search task A visual target detection task developed by Luck and Hillyard (1994b) was used in the study. Visual stimuli were presented on a computer-controlled video display at a distance of 100 cm from the child. The stimulus configurations are presented in Figure B3 and consisted of either (1) homogeneous arrays of eight small blue vertical bars or (2) seven bars with one of three possible pop-out bars: a) a small blue horizontal bar 47

(orientation pop-out); b) a large blue vertical bar (size pop-out); or (c) a small green vertical bar (color pop-out). Each pop-out bar contained a simple feature that was absent from the small blue vertical bars (distractors); thus, it could be easily distinguished from the homogeneous distractors. Each individual bar subtended 0.3 × 0.9 degrees, except for the large bars, which subtended 0.5 × 1.5 degrees. All arrays were placed within an imaginary rectangle that subtended 9.2 × 6.9 degrees of visual angle. The positions of these bars within each array varied randomly with two constraints: 1) equal numbers of bars were presented on the right and left sides of the screen; and 2) the bars did not overlap. Each stimulus array was displayed for 750 ms, and the interstimulus interval (ISI; the temporal interval between the offset of one stimulus to the onset of another) was varied randomly between 600 and 900 ms. A fixation point was continuously visible in the center of the screen. The four arrays were presented with equal probability (i.e., 25% probability for each type) and in random order. The target detection task consisted of three conditions. At the beginning of each condition, one of the three pop-out bars was designated as the target, and the other two were designated as nontargets. The order of pop-out bar designation was fixed: the small blue horizontal bar (Target 1: orientation pop-out), the large blue vertical bar (Target 2: size pop-out), and the small green vertical bar (Target 3: color pop-out). There were 96 stimulus arrays presented in each of the three conditions. The order of blocks within a section was randomized across participants, and the order of stimulus arrays within a block was randomized across blocks. Children were instructed to press a specific response button with their preferred hand for the target-present stimulus arrays, but not for target-absent stimulus arrays (i.e., a go/no-go task). For example, if the target was the small green vertical bar, participants were instructed to press the specific button for stimulus arrays with a small green vertical bar only, but not for arrays where it was absent (including the stimulus arrays that contained small blue bars only). Prior to the task, every child received a training session 48 with the small blue horizontal bar as the target. The percentage of correct responses (pressing the button for target-present stimulus arrays + not pressing the button for target-absent stimulus arrays / the total number of presentations) was calculated. The training session was stopped when the percentage of correct responses reached 80%. Participants were encouraged to ask questions regarding the task, and the experiment was started when they had no more questions and stated that they were ready to begin. A total of 12 trial blocks were presented in the visual search task. A picture was displayed on the monitor following each block, signaling a short break (2–3 minutes).

2.5 Data Analysis

2.5.1 The auditory-auditory distraction task 2.5.1.1 Behavioral data: RT, HR and FA Mean RT, HR and FA for both standard and deviant tones were computed separately. For standards, the first two standard trials after a deviant trial were excluded from the computation, to exclude responses to standards that were possibly influenced by the preceding deviant (Csépe, Pantev, Hoke, Hampson, & Ross, 1992). HRs and RTs were computed from the responses to long tones only (go trials), and FAs from responses to short tones only (no-go trials). Button-press responses to long tones in the interval from 200 to 1500 ms relative to stimulus onset were counted as correct (hits), given that the earliest time point at which short and long stimuli can be distinguished is 200 ms after stimulus onset (Horváth, Czigler, Birkás, Winkler, & Gervai, 2009; Wetzel et al., 2006). Mean RTs were calculated for hits only. Button-press responses to short tones were counted as erroneous responses (FAs). A statistical analysis for all behavioral data (HR, RT, and FA) was conducted using repeated-measures analyses of variance (ANOVAs) with group (CWS versus CWNS) as the between-subject factor, and stimulus type (standard vs. deviant tones) as the within-subject factor. An alpha level of .05 was used 49

2 for all statistical tests. Effect sizes, indexed by the partial-eta squared statistic, (ηp ), are reported for all significant effects. In addition, deviant-minus-standard differences in RTs, HRs, and FAs provided an index of the distraction effect on behavior (Horváth et al., 2009; Wetzel et al., 2006). Independent sample t-tests were used to compare deviant-minus-standard differences in RTs, HRs, and FAs between the two subject groups. Effect sizes, indexed by Cohen d (d), are reported.

2.5.1.2 ERP measures: MMN, P3a, and RON The EEG recordings were bandpass filtered between 0.1 and 30 Hz, and then segmented into epochs in the interval of -100 ms to +800 ms relative to stimulus onset. The 100 ms prior to recording onset served as a baseline. Like the behavioral data analysis, the first two standard trials immediately following a deviant trial were excluded. In addition, trials with incorrect behavioral responses were removed because they might contain error-related negativity, which was not the focus in this study. Artifact detection was performed prior to data analysis in order to eliminate trials contaminated with blinks, excessive eye movement, and muscular artifacts, using a rejection threshold of 130 µV; epochs with EEG or electro-oculographic (EOG) activity changes exceeding 130 µV were rejected from further analysis. All EEG records were subsequently inspected visually to confirm the accuracy of rejection/inclusion (Luck, 2005; Kaganovich, Wary,

& Weber-Fox, 2010). The remaining trials were averaged separately for standard and deviant long and short tones for each participant, and grand averages were calculated. Difference waveforms for long and short tones were calculated separately by subtracting Standard ERPs from Deviant ERPs to isolate and evaluate the ERP components of interest: MMN, P3a, and RON. Peak latencies and mean amplitudes were obtained from three frontal electrode positions (F3, FZ, F4) because of the frontal distribution of the ERP components of 50 interest. Based on previous research results (see Section 1.3.2), it was assumed that the deviant-minus-standard difference waveform would exhibit a negativity between 100 and 250 ms, followed by a positivity between 300 and 400 ms and a late negativity between 400 and 600 ms. After visual inspection of the grand averages of difference waveforms (Figures B4 to B6), we found that our data were consistent with previous research findings except for the existence of two phases of RON and P600 (CWS). The auditory-auditory distraction task was developed by Schröger and Wolff (1998). They only found a negative waveform subsequent to P3a (Schröger et al., 2000; Schröger & Wolff, 1998); however, intriguingly, recent research has indicated that RON actually has two phases: an early and a late phase (eRON and lRON, respectively; Escera, Yago, & Alho, 2001; Berti, 2008; Munka & Berti, 2006). Based on these results, Escera and others indicated that the eRON peaks in 400-600 time interval and reflects attentional reorienting in working memory, the process of refocusing attention on task-relevant information. The lRON peaks in the 550 to 740 ms time interval and is related to the evaluation of task-relevant information as well as motor preparation (Escera et al., 2001; Berti, 2008; Munka & Berti, 2006). Intriguingly, a prominent P600 was found in CWS. The P600 usually peaks around 500 to 600 ms after stimulus onset and is elicited by violations in rule-governed sequences or structures, such as linguistic structures and musical harmony (Luck, 2005;

Patel, Gibson, Ratner, Besson, & Holcomb, 1998). The existence of P600 may suggest that CWS detected violations from the sequence of stimuli (tones). More detailed information regarding these two phases of RON and P600 will be provided and discussed in Chapter 4. After visual inspection of the grand averages of difference waveforms (Figures B4 to B6), the ERPs were divided into five time windows to measure the local peak latencies for MMN (150–250 ms), P3a (250–400 ms), eRON (400–550 ms), and lRON (550–780 ms). The peak latency of P600 was measured in the time window of 500 to 620 51 ms after stimulus onset for CWS. The mean amplitude was measured within a 50 ms time window around the group-specific peak latency of each component, as depicted in the grand-average difference waveform. In detail, the following time windows were applied: MMN: 185–235 ms (CWS), 175–225 (CWNS); P3a: 290–340 ms (CWS), 300–350 ms (CWNS); eRON: 435–485 ms (CWS), 400–450 ms (CWNS); lRON: 695–745 ms (CWS), 605–655 ms (CWNS); P600: 550–600 ms (CWS). All ERP measures were analyzed by means of repeated-measures ANOVAs with the group (CWS vs. CWNS) as the between-subject factor and tone type (long vs. short) as the within-subject factor, for each component. An alpha level of .05 was used for all

2 statistical tests. Effect sizes (ηp ) are reported for all significant effects.

2.5.2 The visual search task 2.5.2.1 Behavioral data: RT, HR, and FA Mean HRs, RTs, and FAs for different targets were computed separately. Button-press responses to target-present trials (go trials) were counted as hits, while button-press responses to target-absent trials (no-go trials) were counted as false alarms. Mean RTs were calculated for hits only. The analysis for behavioral data was conducted using repeated-measures ANOVA with group (CWS vs. CWNS) as the between-subject factor and target condition (Target 1 vs. Target 2 vs. Target 3) as the within-subject factor.

2.5.2.2 ERP measures: N2pc The EEG was offline filtered with a 0.1–30 Hz bandpass, and then epoched into an 800 ms interval, with a 100 ms prestimulus baseline. Trials with blinks, excessive eye movement, and muscular artifacts were excluded. The analysis of ERPs included only those trials with correct behavioral responses. Artifact detection was performed prior to averaging, and a rejection threshold of 130 µV was used to exclude trials contaminated 52 with blinks, excessive eye movement, and muscular artifacts. All EEG records were visually inspected to confirm these decisions. To calculate the observed N2pc effect with respect to a given lateralized stimulus, difference waveforms were constructed using the following equation: N2pc effect = ERP contralateral to the target location – ERP ipsilateral to the target location ERPs contralateral to the target location (contralateral waveforms) were computed by averaging left-target waveforms at right-hemisphere electrode sites with right-target waveforms at left-hemisphere electrode sites. ERP ipsilateral to the target location (ipsilateral waveforms) were computed by averaging right-target waveforms at right-hemisphere electrode sites with left-target waveforms at left-hemisphere electrode sites. Then, difference waveforms were formed by subtracting the ipsilateral waveforms from the contralateral waveforms in order to isolate the N2pc component and calculate the N2pc effect. The difference waveforms across left- and right-hemisphere electrode pairs were averaged to eliminate hemispheric asymmetries that were unrelated to attention (Luck et al., 2006). The difference waveforms are presented in Figures B7 to B9 for Target 1 (orientation pop-out), Target 2 (size pop-out), and Target 3 (color pop-out), respectively. Peak latency and mean amplitude were measured from the difference waveforms at the posterior electrode sites (P3/4, O1/2, P7/8). The peak latency of N2pc was measured as the local peak latency between 175 and 400 ms. The mean amplitude of N2pc was measured as the mean amplitude between 200 and 400 ms. Repeated-measures ANOVAs with subject group (CWS vs. CWNS) as the between-subject factor and target condition (Target 1 vs. Target 2 vs. Target 3) as a within-subject factor were conducted. An alpha level of .05 was used for all statistical tests. Greenhous-Geisser correlation was used to adjust probability values when appropriate. Effect sizes, indexed 53

2 by ηp , were reported for all significant effects. Pairwise comparisons using the Bonferroni corrected alpha level were used for post hoc tests.

2.5.3 Temperament A temperament profile for each child was obtained by using the TMCQ (for children from 8 to 10 years of age) or EATQ-R (for children from 11 to 12 years of age). As described earlier, both the TMCQ and EATQ-R have the same high-level dimensions of temperament: Surgency, Negative Affect, and Effortful Control. Independent sample t-tests were used to compare CWS and CWNS on their scores for these three high-level dimensions. Effect sizes, indexed by Cohen’s d (d), were calculated for all significant effects. For the purpose of this study, between-group scores from Attention Control and Inhibitory Control, two subscales of Effortful Control, were also computed. Further,

Spearman’s rank correlation coefficients (rs) were used to investigate the relationships between (a) the five temperament dimensions (Surgency, Negative Affect, Effortful Control, Attentional Control, Inhibitory Control), (b) HR, RT, and FA measures from the duration discrimination and the target detection tasks, and (c) peak latencies and mean amplitudes of MMN, P3a, eRON, lRON, and N2pc.

CHAPTER 3 RESULTS

The primary aim of this study was to examine and compare attentional control across each stage of processing in CWS and CWNS through the use of auditory-auditory distraction and visual search tasks. We collected both ERP and behavioral measures related to these tasks, and conducted between-group comparisons at both levels of measurement. The second goal of this research was to compare the temperament characteristics of CWS and CWNS and their relationships to attentional control. This chapter is organized in three sections. First, we present the results of our analysis of the behavioral and ERP data obtained from both groups during the auditory-auditory distraction task. Second, we present the results from analyses of the same measures obtained during the visual search task. Finally, we present an analysis of the data obtained from the temperament scales completed by parents of both CWS and CWNS.

3.1 Auditory-Auditory Distraction Task

3.1.1 Behavioral data

Table A1 shows the means, standard deviations, and deviant-minus-standard difference (distraction effect) for RT, HR, and FA measures obtained from CWS and CWNS. The results of statistical analyses for these three measures are summarized in Table A2.

3.1.1.1 RT As shown in Table A1, the mean Deviant-RT was significantly longer than the mean Standard-RT for both CWS and CWNS. There was a significant main effect for 55

2 stimulus type (F (1,14) = 10.982, p = .005, ηp = .44) and subject group (F (1,14) = 8.177,

2 p = .013, ηp = .369); however, no significant stimulus type x group interaction effect was observed (p = .919). The main effect for stimulus indicates that, for both groups, RT was significantly longer (slower) for deviant as compared to standard tones (average RTs for all children, ms; 828.32 ms for deviant tones; 785.69 ms for standard tones; mean and standard deviation for each group are presented in Table A1). This finding indicates a significant behavioral cost on RT for both subject groups. Further, there was a main effect for group: the CWS responded significantly faster than CWNS (average RT for deviant and standard tones; 744.74 ms for CWS; 869.28 ms for CWNS). The distraction effect was defined as the deviant-minus-standard difference in RT. Results for both CWS and CWNS are reported in Table A1. Independent sample t-tests revealed no significant between-group difference in the distraction effect (t (14) = .103, p

= .92, d = .055).

3.1.1.2 HR Table A1 shows the HRs for deviant and standard tones for both groups of children. In general, deviants yielded lower HRs (less accuracy) compared to standards in both participant groups. Results of the ANOVA revealed no significant stimulus type × group interaction effect (p = .955), and no significant main effect was found for stimulus

2 type (p = .283). Group comparison was also nonsignificant (p = .077, ηp = .206); however, a trend was observed. An examination of the descriptive data (see Table A1) reveals that compared to CWNS, CWS produced a lower HR (i.e., less accurate) to both standard tones (CWNS vs. CWS: 92.12 ms vs. 75.21 ms) and deviant tones (CWNS vs. CWS: 88.15 ms vs. 70.81 ms). The distraction effect was defined as the deviant-minus-standard differences in HR. Results for CWS and CWNS are reported in Table A1. Independent sample t-tests 56 revealed no significant between-group difference in the distraction effect (t (14) = .65, p = .435, d = .346).

3.1.1.3 FA Table 1 shows the FAs for deviant and standard tones for CWS and CWNS. Results of the ANOVA revealed a significant main effect for group (F (1,14) = 15.22, p

2 = .002, ηp = .521); however, there was no significant stimulus type × group interaction effect (p = .868), and no significant main effect for stimulus type (p = .654). The main effect for group indicates that CWS produced more FAs (+22.4%), compared to CWNS. The distraction effect was defined as the deviant-minus-standard differences in FA. Results for CWS and CWNS are reported in Table A1. Independent sample t-tests revealed no significant between-group difference in the distraction effect (t (14) = -.17, p = .868, d = .09).

3.1.2 ERP data Grand-average ERPs for standard and deviant tones for both CWS and CWNS, as well as difference waveforms, are presented in Figure B4 for long tones, and Figure B5 for short tones. Deviant-minus-standard difference waveforms for both groups were over-plotted, as presented in Figure B6. Because the aim of this study was to investigate the effects of distraction, data analysis focused on the deviant-minus-standard difference waveforms. These waveforms showed a similar temporal pattern in CWS and CWNS: early negativity or negativities peaking between 100 and 300 ms (MMN) followed by a positivity between 250 and 400 ms (P3a), and one or more late negativities between 400 and 750 ms (RON). Two subcomponents of RON were found: eRON (400–550 ms) and lRON (550–780 ms). Moreover, a P600-like component was found in the CWS group. 57

Descriptive analyses for MMN, P3a, eRON, lRON, and P600 are presented in Tables A3 (long tones) and A4 (short tones). ANOVA results for individual ERP components are reported in the following sections.

3.1.2.1 MMN Peak latency. There was no significant tone type × group interaction effect (p = .86), and no significant main effect for tone type (p = .46) and subject group (p = .69; see Table A5). Mean amplitude. There was no significant tone type x group interaction effect (p = .53), and no significant main effect for tone type (p = .92) and subject group (p = .72; see Table A5). In sum, the ANOVA results revealed that the MMN elicited by the frequency-change deviant did not significantly differ across two tone conditions (long vs. short tones) and cross the two subject groups (CWS vs. CWNS). The absence of significant between-group differences in the peak latency and mean amplitude of MMN suggests that CWS and CWNS did not differ in automatic scanning and change-detection, reflected by MMN.

3.1.2.2 P3a

Peak latency. There was no significant tone type × group interaction effect (p = .57), and no significant main effect for tone type (p = .98) and subject group (p = .54; see Table A6). Mean amplitude. There was no significant tone type × group interaction effect (p = .56), and no significant main effect for tone type (p = .87) and subject group (p = .46; see Table A6). In sum, the ANOVA results revealed that the P3a did not significantly differ across two tone conditions (long vs. short) and two subject groups (CWS vs. CWNS). 58

The absence of significant between-group differences in the peak latency and mean amplitude of MMN suggests that CWS and CWNS did not differ at the stage of involuntary attentional orientation, reflected by P3a.

3.1.2.3 eRON Peak latency. There was no significant tone type × group interaction effect (p = .47), and no significant main effect for tone type (p = .82) and group (p = .80; see Table A7). Mean amplitude. There was no significant tone type × group interaction effect (p = .89), and no significant main effect for tone (p = .23) and group (p = .66; see Table A7). In sum, the ANOVA results revealed that the eRON did not significantly differ between two tone conditions (long vs. short) and two subject groups (CWS vs. CWNS). The absence of significant between-group differences in the peak latency and mean amplitude of eRON suggests that CWS and CWNS did not differ at the stage of voluntary redirection of attention to task-relevant information (tone duration) in working memory, the index function of eRON.

3.1.2.4 lRON

Peak latency. There was a significant main effect for subject group (F (1,14) =

2 5.38, p = .04, ηp = .28); however, no significant tone type × group interaction effect (p = .24) or main effect for tone type (p = .37) was observed (see Table A8). The main effect for group indicates that CWS exhibited an increased peak latency in lRON, compared to CWNS. Mean amplitude. There was no significant tone type × group interaction effect (p = .31), and no significant main effect for tone type (p = .28) and subject group (p = .70; see Table A8). 59

In summation, the ANOVA results revealed that CWS, compared to CWNS, exhibited increased peak latency in lRON, suggesting a lower efficiency in triggering the stage of evaluating task-relevant information and motor preparation, the index function of lRON. No significant between-group difference was found in the mean amplitude of lRON, suggesting that CWS did not spend more effort in the process reflected by lRON, compared to CWNS. No significant difference was found in lRON across two tone conditions (long vs. short).

3.1.2.5 Occurrence of P600 As shown in Figures B4 to B6, a prominent positivity peaking between 500 and 600 ms, P600, was found for both long and short tones in CWS. However, no prominent P600 was found for CWNS for both tone conditions. The peak latencies and mean amplitude of P600 are shown in Tables A3 and A4 for long and short tones in CWS, respectively. Peak latency. A paired t-test was used to compare the peak latencies of P600 for long and short tones in the CWS group. No significant difference was found (t (7) = 1.01, p = .33, d = .55) in the peak latency of P600 for long and short tones. The peak latencies of P600 did not differ across different tone-type conditions. Mean amplitude. A paired t-test was used to compare the mean amplitudes for long and short tones for CWS. No significant difference was found for the mean amplitude under two tone-type conditions (t (7) = .35, p = .734, d = .167).

3.1.3 Summary In summary, there were two main findings for the behavioral measures. First, for both CWS and CWNS, deviant tones yielded a significant distraction on RT (slower); both CWS and CWNS responded more slowly to deviants than to standards. However, no significant between-group difference was found on the distraction effect of RT. Second, 60 compared to CWNS, CWS responded significantly faster for standards or deviants, and this faster response was associated with lower accuracy (HR; approached significance, p = .077) and higher FA (reached significance, p = .002). There were two main findings from the ERP analysis. First, a significant main effect for group was found for the peak latency of lRON; CWS exhibited significantly longer peak latency in lRON, compared to CWNS. There were no significant between-group differences in either peak latency in any of the other ERP components. Second, a prominent P600 was found for CWS but not for CWNS. No significant differences were found in the peak latencies and the mean amplitudes of P600 between long and short tones.

3.2 The Visual Search Task

3.2.1 Behavioral data Table A9 shows the means and standard deviations for RTs, HRs, and FAs in CWS and CWNS. The results of statistical analyses for these behavioral data are summarized in Table A10.

3.2.1.1 RT The ANOVA results revealed a significant main effect for target condition (F (2,

2 28) = 62.98, p < .001, ηp = .82); however, no significant target condition x group interaction effect (p = .091) or main effect (p = .098) for group was observed (see Table A10). In the visual search task, children were asked to perform a target detection task. Three target conditions were included in this task: Target 1 (orientation pop-out), Target 2 (size pop-out), and Target 3 (color pop-out). Pairwise comparisons with Bonferroni corrected alpha revealed that all children responded faster from the Target 1 condition to the Target 3 condition (comparisons of mean RTs for all children in three target conditions: Target 1 (649.32 ms) vs. Target 2 (615.42 ms), p = .005; Target 1 (649.32 ms) 61 vs. Target 3 (550.13 ms), p < .001; Target 2 (615.42 ms) vs. Target 3 (550.13 ms), p < .001). Subsequent one-way repeated ANOVAs were conducted for each group and revealed a significant difference in RT among the three target conditions for CWS (F (2,

2 14) = 30.06, p < .001, ηp = .811). Pairwise comparisons using Bonferroni corrected alpha level showed that CWS responded faster from the Target 1 condition to the Target 3 condition (Target 1 (680.12 ms) vs. Target 2 (625.12 ms), p = .023; Target 1 (680.12 ms) vs. Target 3 (572.66 ms), p = .001; Target 2 (625.81) vs. Target 3 (572.66 ms), p = .003; see Table A9). Similar effects were observed for CWNS, in that there was a significant

2 difference in RT among the three target conditions (F (2, 14) = 36.81, p = <.001, ηp = .84). Pairwise comparisons using Bonferroni corrected alpha level showed CWNS responded faster in the Target 3 condition than in the other two target conditions (Target 1 (618.52 ms) vs. Target 2 (605.03 ms), p = .587; Target 1 (618.52 ms) vs. Target 3 (527.6 ms), p = .001; Target 2 (605.03 ms) vs. Target 3 (527.6 ms), p < .001; see Table A9).

3.2.1.2 HR The ANOVA results revealed no significant target condition × group interaction effect (p = .39) and main effect for group (p = .128; see Table A10). The main effect for

2 target condition was also non-significant (p = .06, ηp = .18); however, a trend was observed. Examination of descriptive data (Table A9) reveals that the HRs in the Target 3 condition were higher than those in other two conditions for both subject groups (Target 1 vs. Target 2 vs. Target 3; CWS: 92.54% vs. 95.05% vs. 96%; CWNS: 96.35% vs. 95.44% vs. 99.09%).

3.2.1.3 FA There was no significant target condition × group interaction effect (p = .39), and no significant main effect for target (p = .129) and group (p = .762; see Table A10). The rates of FA did not differ across target conditions and subject groups.

3.2.2 ERP data Grand-average ERP waveforms are shown in Figures B7 to B9 for the three target conditions. Contralateral, ipsilateral, and the difference waveforms are presented in each figure. The N2pc component can be observed on the difference waveforms 200 to 400 ms after stimulus onset. Table A11 shows the means and standard deviations for the peak latency and mean amplitude in each of the three target conditions for CWS and CWNS. The results of statistical analyses for these ERP measures are summarized in Table A12. There was no significant target condition × group interaction effect (p = .39) and no significant main effect for target condition (p = .129) and subject group (p = .762). The rates of FA did not differ across target conditions and subject groups.

3.2.2.1 The peak latency of N2pc There was no significant target condition × group interaction effect (p = .99) and no significant main effect for target condition (p = .1) and group (p = .94; see Table A12).

The results suggest that the peak latency of N2pc did not differ across subject groups and target conditions.

3.2.2.2 The mean amplitude of N2pc There was no significant target condition × group interaction effect (p = .58) and no significant main effect for target condition (p = .16) and group (p = .29; see Table A12). The results suggest that the mean amplitude of N2pc did not differ between subject groups and across the three target conditions. 63

In sum, the absence of significant between-group differences in the peak latency and mean amplitude suggests that CWS did not differ from CWNS at the stage of attentional filtering in the visual search task.

3.2.3 Summary There are two main findings. First, both CWS and CWNS responded faster (shorter RT) in the Target 3 condition than in the other two conditions. Second, no significant group-difference was found in the peak latency and mean amplitude of N2pc, which suggests that CWS did not differ from their fluent peers at attentional filtering, the index function of N2pc.

3.3 Temperament Data The results of the temperament analysis are organized in two sections. First, we will present the results of both descriptive analyses and group comparisons. Second, we will discuss the results of the correlation analysis to determine the relationship between each of the five temperament dimensions (Surgency, Negative Affect, Effortful Control, Attentional Control, Inhibitory Control) and the behavioral and ERP data obtained from the auditory-auditory distraction visual search task.

3.3.1 Descriptive data and group comparisons Table A13 shows the mean scores, standard deviations, and group-difference comparisons for the three high-level temperament dimensions and two scales under Effortful Control for CWS and CWNS. First, for the three high-level temperament dimensions, marginal significant between-group differences were found in Negative Affect (p = .067, d = 1.06) and Effortful Control (p = .06, d = 1.095); the values of Cohen’s d in these two group-difference comparisons were over .08, indicating a large 64 effect size (Cohen, 1992). No significant between-group difference was found in Surgency (p = .15). These results indicate that CWS, compared to CWNS, tended to score higher for either Negative Affect, and lower on Effortful Control. Next, a significant between-group difference was found in Attentional Control (t(14) = -2.72, p = .017, d = 1.454); CWS had a lower score in Attentional Control, compared to CWNS. No significant between-group difference was found on Inhibitory Control (p = .104). Taken together, these results may suggest that CWS, compared to CWNS, tend to exhibit higher negative emotional reactivity, but lower effortful control—especially lower attentional control.

3.3.2 Correlations between temperament scores and behavioral data For each subject group, Spearman’s rank correlations were conducted to investigate the relationships between the five temperament dimensions and the RT, HT, and FA scores obtained from the auditory-auditory distraction and the visual search task, respectively. The results will be presented separately in the following sections.

3.3.2.1 The auditory-auditory distraction task Table A14 shows the correlations between the five temperament dimensions and

HR, RT, and FA. Table A15 shows the correlations between the five temperament dimensions and the distraction effects on reaction time, hit rate, and false alarm (DERT, DEHR, DEFA). CWS. The Spearman’s rank correlations revealed no significant relationships between the five temperament dimensions and behavioral performance (see Table A14). Similarly, no significant relationship was found between the distraction effects on behavioral data (DEHR, DERT, DEFA) and temperament (see Table A15). 65

CWNS. The Spearman’s rank correlations revealed a significant correlation between RT and Attentional Control in CWNS, in that RT was negatively correlated with

Attentional Control (rs = -.755, p = .031). The result indicates that for CWNS, higher attentional control corresponded to a shorter RT. No significant relationship was found between the distraction effect on behavioral data (DEHR, DERT, DEFA) and temperament (Table A15).

3.3.2.2 The visual search task The correlations between the five temperament dimensions and behavioral data obtained from the three target conditions and the overall performances are presented in Table A24. CWS. Spearman’s rank correlations revealed a significant correlation between RT in the Target 2 condition and Effortful Control (rs = -.905, p = .002). The RT in the Target

2 condition was also negatively correlated with Attentional Control (rs = -.738, p = .37) and Inhibitory Control (rs = -.881, p = .004). Moreover, the scores for Effortful Control were negatively correlated with the overall RT. The results indicate that CWS who scored higher in these three temperament dimensions—Effortful Control, Attentional Control, and Inhibitory Control—tended to respond faster in the visual search task, compared to CWS who scored lower in these three dimensions

CWNS. The Spearman’s rank correlations revealed no significant relationships between the behavioral data (HR, RT, FA) and the five temperament dimensions.

3.3.3 Correlations between temperament and ERP data Spearman’s rank correlations were conducted for investigating the relationships between temperament and the ERP data, including the peak latencies and the mean amplitudes for MMN (Tables A16 and A17), P3a (Tables A18 and A19), eRON (Tables A20 and A21), lRON (Tables A22and A23), and N2pc (Table A25). 66

3.3.3.1 CWS No significant correlation was found between the five temperament dimensions and the peak latency and mean amplitude of MMN, P3a, eRON, lRON, and N2pc.

3.3.3.2 CWNS A significant negative correlation was found between the peak latency of eRON for long tones and Attentional Control (rs = -.892, p = .003). This observation indicates that CWNS who scored higher in Attentional Control exhibited earlier eRON peak activity. In other words, CWNS with good attentional control were able to quickly shift their attention back to the task-relevant information following a distraction. No significant correlation was found between temperament and the peak latency of other ERP components, including MMN, P3a, lRON, and N2pc. In addition, there was no significant correlation between the five temperament dimensions and the mean amplitude of MMN, P3a, eRON, lRONn, and N2pc.

3.3.4 Summary of temperament data Based on the results from parent-report temperament questionnaires, compared to CWNS, CWS as a group had a significant lower score on Attentional Control. In addition, CWS also tended to have a higher score on Negative Affect and a lower score on

Effortful Control in comparison with their fluent peers; these group comparisons approached significance. There were three main findings regarding the relationships between temperament characteristics, and the behavioral and ERP data. First, CWNS with higher attentional control tended to respond more quickly in the auditory-auditory distraction task. Second, CWS with a higher score on Effortful Control and/or its subscales (Attentional Control and Inhibitory Control) tended to produce shorter RTs in the visual search task. Last, CWNS with good attentional control tended to have a short peak latency in eRON, which 67 indexes the stage of redirecting attention to task-relevant information in working memory.

CHAPTER 4 DISCUSSION

The main purpose of this study was to investigate attentional control during the process of distraction in children who do and do not stutter. The distraction effect was measured across two levels: behavioral performance (RT, HR, and FA) and neural activity (ERP data). Behavioral data reflect contributions from two mechanisms and multiple stages of attentional processing, while ERP data reflect the proficiency and efficiency of each mechanism and each stage. The two mechanisms examined in this study were: the sensory/perceptual processing and goal-directed processing (the cognitive control after distraction). The sensory/perceptual processing mechanism includes the stages of automatic scanning and change detection (MMN), involuntary orienting of attention to distractors/changes (P3a), and attentional filtering (N2pc). The goal-directed processing includes the stage of voluntarily reorienting attention to task-relevant information of a current task (RON). We hypothesized that compared to CWNS, CWS would show higher levels of distractibility, as measured by both the behavioral performance and neural activity related to attentional processing, and that a high level of distractibility is linked to differences in either sensory/perceptual processing in the early stages of distraction, or goal-directed processing in the later stages, or both. Further, because previous research has shown that temperament influences attention (Rothbart & Bates, 1998), and that CWS are temperamentally different from their fluent peers, we wanted to examine the relationship between temperament and attention in CWS and CWNS. In order to accomplish this, we correlated the behavioral and ERP data with five dimensions: Surgency, Negative Affect, Effortful Control, Attentional Control, and Inhibitory Control, each of which has been linked to behavior associated with attentional processing. 69

This chapter will be divided into five sections. We will discuss results from analysis of the temperament data first (Section 4.1), followed by correlation analysis of the five temperament dimensions with behavioral and ERP measures from both the auditory-auditory distraction and the visual search task (Sections 4.2 and 4.3.) Section 4.2 will discuss the findings from the auditory-auditory distraction paradigm, and Section 4.3 will discuss the findings from the visual search task. Finally, limitations of this study and directions for future research will be discussed.

4.1 Temperament of CWS and CWNS The analysis of parent-report temperament questionnaire responses showed that CWS and CWNS were different in their temperament profiles (Table A13). CWS, as a group, scored higher on the high-level dimensions Surgency, and Negative Affect, and lower on the high-level dimension of Effortful Control, than CWNS. The between-group differences on the scores for Negative Affect and Effortful Control approached significance (Negative Affect: p = .067, d = 1.06; Effortful Control: p = .06, d = 1.095). For the purpose of this study, two scales of Effortful Control, Attentional and Inhibitory Control, were extracted, and group-comparisons were also computed. A significant between-group difference was found for Attentional Control; CWS scored lower on Attentional Control than CWNS.

These findings are in line with results from previous studies (e.g., Eggers et al., 2010; Karrass et al., 2006), which have indicated that CWS exhibit increased emotional reactivity and reduced emotional regulation, compared to their normally fluent peers. Both theoretical models (i.e., the CE and DD-S models) and empirical evidence (e.g., Johnson et al., 2010; Ntourou, et al., 2013) have converged to support the argument that these differences in temperament characteristics influence the development of persistent stuttering in children. For example, previous studies have indicated that CWS with high levels of emotional reactivity produce less stuttering if they simultaneously exhibit high 70 levels of emotional regulation and more frequent and prolonged use of regulatory strategies (Arnold et al., 2011; Walden et al., 2012). Thus, high efficiency of emotional regulation may be considered as a protective factor for the development of stuttering (Eggers et al., 2010), and it may be an important predictor for either unassisted recovery or stuttering persistence (Yairi & Amborse, 2005).

4.2 Auditory-Auditory Distraction There were three main findings in the first experiment. First, analysis of the ERP data revealed that CWS significantly differed from CWNS in cognitive control after distraction (the existence of P600 and a long peak latency for lRON), suggesting at the very least a different processing of deviant information. These differences were observed in spite of the fact that no significant between-group differences were observed in distraction effects on behavioral data, including the distraction effect on reaction time (DERT), hit rate (DEHR), and false alarm (DEFA). Second, compared to their fluent peers, CWS exhibited significantly faster RTs, lower HRs, and more FAs. Third, CWNS who scored high on Attentional Control exhibited a short RT and a short peak latency in eRON (for long tones). No significant relationships between temperament characteristics and behavioral and ERP data were found in CWS.

4.2.1 Distraction effect on behavioral and ERP data The first finding of this study indicated that CWS, as a group, differed in the processing of deviant information in the cognitive control after distraction. However, this between-group difference in the deviance processing was not reflected on the behavioral data. The behavioral data showed that the frequency change of tones yielded a significant distraction effect on RT for both groups; that is, the RT to deviant tones was significantly longer than to standard tones for all children. Although no significant 71 between-group difference was found for HR and FA for standard and deviant tones, a trend toward a higher hit rate for standard tones than for deviant tones was observed both in CWS (standards: 75.21%; deviants: 70.81%) and CWNS (standards: 92.12%; deviants: 88.15%). These results indicate that for all children, the frequency or task-irrelevant change in tones yielded a behavioral cost in the performance of the duration discrimination task; that is, the deviant tones were associated with increased RT and decreased HR for both CWS and CWNS. The degree or amount of distraction effect did not distinguish between the two groups of children; there were no significant deviant-minus-standard differences for any of the behavioral measures (DERT, DEHR, DEFA) between CWS and CWNS. This observation indicates that the deviant information did not yield a larger behavioral cost for CWS, as opposed to CWNS. In contrast to the behavioral data, analysis of ERP data indicated that CWS and CWNS differed in the processing of deviant information during the distraction task. These between-group differences will be discussed in the temporal order of stages reflected by MMN, P3a, eRON, and lRON. Recall that MMN and P3a reflect the first two stages in sensory/perceptual processing: automatic scanning and change detection (MMN), and involuntary attentional orientation (P3a). The eRON and lRON reflect the early and late stages of cognitive control after distraction, namely, distraction compensation. Further, we also found a prominent P600 in CWS, but not in CWNS, which may suggest that the distraction elicited by deviants remained substantial after the early stage of cognitive control (after eRON). These findings will be discussed below. First, CWS and CWNS did not differ in either peak latency or mean amplitude of MMN. The MMN component reflects the first stage of distraction processing: automatically scanning the environment, and detecting irregularities by comparison of a new stimulus to a memory trace for the previously presented stimuli (Näätänen, 1992). Research has shown that MMN generated in different brain areas may have different functional roles. The frontal MMN that was measured in this study is involved in the 72 processing of rare deviant stimuli and the initiation of attentional orienting (Gomot, Giard, Roux, Barthelemy, & Bruneau, 2000; Sato et al., 2000; Yago, Escera, Alho, & Giard, 2001). The lack of group difference in MMN suggests that the neural processes that are involved in the automatic scanning and irregularity detection are similar in CWS and CWNS. This result is in agreement with a previous study by Kaganovich et al. (2010). They also reported no significant difference in MMN between CWS and CWNS, and suggested that CWS did not differ from their fluent peers in the change-detection system reflected by MMN. Second, no statistically significant between-group difference was found in the peak latency and mean amplitude of P3a. The P3a reflects the stage of involuntary orienting of attention to deviant information—in this case, the frequency change. Based on the statistical results, we conclude that CWS, compared to CWNS, did not show increased reactivity and distractibility at the level of sensory/perceptual processing. However, inspection of the raw data and the ERP difference waveforms showed that CWS produced a larger P3a than their normally fluent peers in the long tone condition (CWS: 4.18 µV; CWNS: 1.82 µV; see Tables A3 and A4 and Figure B6). Based on this inspection, we cannot completely rule out the possibility of increased distractibility from the sensory/perceptual processing in CWS. The lack of significance may be due to the small sample size and the large individual variation within each group (large standard deviation). Future research may focus on this line of inquiry and investigate whether CWS exhibit increased involuntary orienting toward unexpected or task-irrelevant stimuli, which can contribute to high distractibility. Third, two phases of RON were observed in CWS and CWNS in this study: eRON and lRON. The existence of the two-phase RON is more prominent in CWS. Recent research has indicated that these two phases of RON reflect different processes in the stage of distraction compensation. Escera, Yago, and Alho (2001) manipulated the time interval between task-relevant and distracting stimuli and found that two phases of 73

RON were dissociated on the basis of their scalp distribution. In addition, they found that the lRON was time-locked to task-relevant information, suggesting that the lRON may reflect the process of evaluating task-relevant stimuli. Munka and Berti (2006) also confirmed the existence of two RON phases. Munka and Berti observed that the eRON was elicited when tasks had a working memory load (e.g., classical odd/even classification), whereas the lRON was elicited when a task decision was based on a physical feature of the stimuli (size or color). Based on their results, the authors suggested that the eRON reflects reorienting attention to task-relevant information (the duration of a tone) in working memory, while lRON reflects a general aspect of attentional allocation or evaluation of task-relevant information after distraction (e.g., rehearsal of instructions or self-motivation (Escera et al., 2001; Munka & Berti, 2006). Furthermore, evidence from magnetoencephalography (MEG) shows that the primary motor cortex also contributes to the reorientation of attention to the main task (Horváth, Maess, Berti, & Schröger, 2008). The primary motor cortex activation may reflect the selection of a proper action set for the task at hand, for example, pressing the right button for long tones but the left one for short tones (Berti, 2008; Horváth et al, 2008). Besides the involvement of the primary motor cortex, results from fMRI also shows an involvement of the cingulate gyrus during the auditory-auditory distraction task (Rinne et al., 2007). Based on these results, Berti (2008) argued that the RON may also reflect a process of conflict or online performance monitoring, which is a function of the anterior cingulate gyrus (Carter et al., 1998). On the basis of these findings, Berti (2008) suggested that the eRON is related to the process of attentional orientation to task-relevant information at the level of working memory (e.g., task switching), whereas the lRON is associated with processes of evaluation for task-relevant information and preparation for a required behavioral response (e.g., task switching, action selection and conflict/online performance monitoring). 74

In the present study, no significant between-group difference was found in the peak latency or mean amplitude for eRON, suggesting that CWS and CWNS did not differ in redirecting attention to task-relevant information (tone duration) in working memory. As for lRON, a significant between-group difference was found in peak latency. lRON is thought to reflect two processes: evaluation of task-relevant information after distraction, and motor preparation for the task at hand (Berti, 2008; Escera et al., 2001; Munka & Berti, 2006). In the tone duration discrimination task, children were asked to attend to tone duration while preparing to respond, with a button-press to long tones (i.e., go trials), but not short tones (i.e., no-go trials). The increased peak latency of lRON for the CWS suggests that they may be delayed in the process of evaluating task-relevant information and motor preparation after distraction. Surprisingly, a prominent P600 response was found only in CWS, occurring between the eRON and lRON components (see Figure B6). The P600 is a positivity peaking around 500 to 600 ms, and it is elicited by unexpected structures in linguistic stimuli, such as syntactic violations (Luck, 2005), and non-linguistic stimuli, such as errors in musical harmony (whether or not a chord is played out of key; Patel et al., 1998). Given the existence of P600 in both linguistic and non-linguistic contexts, some researchers have proposed that it is elicited by violations in rule-governed sequences (Patel et al., 1998)—or that it is an effect of encountering an unexpected stimulus, similar to the index function of P3b (Coulson, King, & Kutas, 1998). In the present study, the auditory-auditory distraction task consisted of a standard tone of either 400 or 200 ms duration, always followed by another standard tone with either 400 or 200 ms duration. Therefore, the appearance of a deviant tone (frequency change) was an unexpected violation of this sequence or task “rule,” and this violation resulted in the elicitation of a P600 for CWS. Further, P600 was observed after the component of eRON, which is associated with voluntary reorienting of attention to task-relevant information (the duration of a tone). The network of voluntary attention orientation encompasses the 75 ability to select information from sensory inputs, while inhibiting information that is either unwanted or task-irrelevant (Lepsien & Nobre, 2006). Fuentes (2004) has argued that the main role of inhibitory control in voluntary attention is to avoid re-examining task-irrelevant information. It is assumed that, after eRON, the distraction effect elicited by deviant information (frequency change) should be effectively inhibited. Then, attention can refocus on task-relevant information, making this information available for advanced processing in the late stage of the distraction process (the stage of lRON). However, the lower efficiency in inhibiting distraction and refraining from returning to the deviant information may lead to a delay to start the next stage (i.e., the process reflected by lRON).

4.2.2 RT, HR, and FA rates in CWS For CWS, analysis of behavioral data revealed an interesting combination of relatively short RT, low HR, and a high rate of FA. This finding is consistent with recent work in inhibitory control using a go/no-go task with CWS (Eggers, De Nil, & Van den Bergh, 2013). A tendency to a relatively short RT was also observed in two separate studies of attentional shifting in CWS (Egger et al., 2012; Subramanian & Yairi, 2006). There are two possible explanations for the finding of short RT, low HR, and high FA in CWS: low efficiency in inhibitory control and a speed-accuracy trade-off effect.

Go/no-go tasks are a common measure of inhibitory control, in which the rate of FA is an important index of proficiency in suppressing prepotent responses (Christ, White, Mandernach, & Keys, 2001; Nelson, Thomas, & de Haan, 2006). In a go/no-go task, subjects are asked to respond to a specific stimulus (e.g., a specific auditory or visual stimulus; go), but not to others (no-go). The ability to refrain from making the responses to no-go trials is used as an index of inhibitory control. More FAs represent an under-controlled tendency to make responses to no-go trials (Christ et al., 2001; Nelson et al., 2006). Some studies have manipulated the occurrence probabilities of go and no-go 76 trials; typically go trials are more frequently presented than no-go trials. This manipulation is believed to enhance the prepotent response tendency and increase the inhibitory control demand when no-go trials are presented (Christ et al., 2001). In the auditory-auditory distraction paradigm, the occurrence probabilities for go and no-go trials hold equal; however, the inhibitory control demand may still be high for CWS, given that a high rate of FA was found. This finding is consistent with a study by Eggers, De Nil, and Van den Bergh (2013); they also found a high rate of FA in CWS when the occurrence probabilities for go and no-go trials were equal. The relatively high rate of FA exhibited by CWS may be explained, in part, by temperament. For example, CWS in this study were rated higher in Surgency and lower in Inhibitory Control, compared to CWNS, although the between-group difference did not reach significance (Table A13). For CWS, this mismatch in Surgency and Inhibitory Control most likely contributed to the high rate of FA; that is, they were less able than CWNS to inhibit prepotent responses (pressing the button) on no-go trials. This observation is consistent with a study by Eggers et al. (2010); they also reported that CWS scored lower on Inhibitory Control and higher on Approach, which is a scale under Surgency and defined as the amount of excitement and positive anticipation for expected pleasurable activities. Eggers et al. suggested that CWS are less able to regulate their excess or inappropriate approach responses due to the low inhibitory control.

In the present study, children were instructed to respond to specific tones as fast and as accurately as possible. The combination of short RT, low HR, and high FA for CWS may also result from a speed-accuracy trade-off effect (Dudschig, & Jentzsch, 2009; Förster, Higgins, & Bianco, 2003; Jentzsch & Leuthold, 2006). To provide empirical evidence for the explanation of speed/accuracy tradeoff, an additional Spearman’s rank correlation was conducted to investigate the relationships between RT, HR, and FA. In CWS, the Spearman’s rank correlation revealed that FA was negatively correlated with

RT (rs = -.667, p = .071); the correlation approached significance. This finding suggests 77 that CWS who exhibited faster RTs produced more FAs. No significant relationship was found between FA and HR (rs = -.048, p = .911), and HR and RT (rs = .262, p = .513). The negative correlation between RT and FA lends support for the speed/accuracy trade-off effect in CWS. Individuals have been shown to adopt either a promotion focus or a prevention focus when asked to perform a task (Förster et al., 2003). Those who use a promotion focus tend to respond faster at the expense of accuracy, whereas individuals with a prevention focus tend to be more accurate at the expense of speed (Förster et al., 2003). Our temperament data indicated that CWS scored higher in Surgency than CWNS, suggesting that they are more likely to respond faster at the risk of performing less accurately on the auditory-auditory distraction task. This interpretation fits well with Eggers et al.’s (2013) finding that CWS were less flexible in adjusting performance strategies during high-speed performance tasks; that is, they maintained a high speed of response to a go-task even though their HR was low. The maintenance of a high speed or response following the production of an error is relatively atypical; in general, research has found that people usually respond faster before and slower after the production of an error (Brewer & Smith, 1989; Dudschig & Jentzsch, 2009; Jentzsch & Leuthold, 2006, 2009). The slowing down of response rate following errors is interpreted to be a behavior modulation strategy to avoid future errors and optimize overall performance (Jentzsch &

Leuthold, 2006, 2009). In other words, it provides a balance between accuracy and speed of responding.

4.2.3 Exploring the role of P600 in the process of distraction in CWS Our results indicated the occurrence of P600 in CWS, which suggest an atypical processing for deviant information. However, whether the atypical processing for deviants results from stuttering or from adopting different strategies for high-speed tasks 78

(promotion focus vs. prevention focus) remains unknown. In addition, the aforementioned ERP data analyses focused only on correct trials, which seems insufficient to understand the nature of P600 during the process of distraction based on only responses from correct trials. It is important to know if P600 also appears during incorrect trials. To answer these two questions, additional data analyses were conducted. Results are summarized and discussed below. According to the discussion in 4.2.2, CWS tend to adopt a promotion focus strategy (fast RT and high FA), whereas CWNS tend to adopt a prevention focus strategy (slow RT and low FA). To determine whether the occurrence of P600 in CWS results from adopting different strategies in high-speed tasks, prevention focused CWS were selected and the difference waveforms for long and short tones were computed. We hypothesized that the occurrence of P600 would be a consequence of stuttering if these prevention focused CWS exhibit a P600. On the other hand, we hypothesized that the occurrence of P600 would be better explained by adopting different strategies during high-speed tasks if no P600 could be found in CWS using a prevention focus strategy. First, to determine the possible cause of P600, a scatter plot of the rate of FA and the mean RT was constructed (Figure B10). CWS with low FAs and slow RTs, which matches the behavioral performance of prevention focused CWNS, were selected. As shown in Figure B10, three CWS exhibited low FAs in combination with slow RTs.

These three CWS were considered to adopt a strategy of prevention focus, like CWNS. The difference waveforms for long and short tones for these three CWS were computed. As shown in Figure B11, a P600 was found for CWS using prevention focus. This suggests that the occurrence of P600 may result from stuttering rather then using different strategies during high-speed tasks. Next, to determine whether or not a P600 occurs during incorrect trials, the ERPs for FAs were computed and compared with ERPs for correct trials. As CWNS exhibited very few FAs, there were an insufficient number of trials for measurement, so only the 79 incorrect trials of CWS were analyzed. We hypothesized that if P600 was only found in correct trials it would suggest that the cognitive process reflected by P600 is helpful in producing correct responses. On the other hand, we hypothesized that if P600 was found in both correct and erroneous trials it is more likely to reflect atypical processing of deviant information in CWS. Figure B12 shows the comparison of difference waveforms for correct trials (ERPs for long and short tones) and erroneous trials (FAs). No P600 was found in ERPs for FAs in CWS. However, we found a large negativity around the time interval of 500 to 700 ms, which may be the error-related negativity (ERN). ERN is a negative-going deflection and the onset of ERN occurs at or shortly before the erroneous responses and peaks within 100 ms after errors (Gehring, Goss, Coles, Meyer, & Donchin, 1993; Gehring, Liu, Orr, & Carp, 2012). The mean RT for FAs in CWS was 598.69 ms (SD = 170.82; Standard tones: Mean = 616.73, SD = 178.74; Deviant tones: Mean = 580.65, SD = 183.63). The large negativity occurring between 500 -700 ms is within the time interval for the onset and peak of ERN for FAs; thus, it should be considered the component of ERN. This large ERN may overlap with P600, making it impossible to determine whether or not P600 occurs in the trials of FAs and answer the second question. However, the first prominent difference between the difference waveforms of correct and erroneous trials was found during the stage of automatic scanning and change detection (MMN), indicating that the source of errors may be at this stage. The amplitude of MMN was larger for FAs than correct trials. In addition, the large ERN for FAs may suggest that CWS were aware of their mistakes in button-pressing responses; they were able to do online error-monitoring. Arnstein, Lakey, Compton, and Kleinow (2011) found that CWS showed a heightened amplitude for ERN compared to CWNS. Arnstein et al. suggest that stuttering may result from over-monitoring the process of speech-language planning. Between-group comparison between ERN cannot be conducted in this study 80 due to the insufficient data for FAs in CWNS. Future studies may consider focusing on this line of research. To conclude, the presence of P600 suggests a tendency to return and re-evaluate deviant information in CWS. Even though we could not determine the role of P600 because of the overlapping of ERN and P600, we would still suggest this tendency exacerbate the process of speech-language planning and production for CWS after a speech disruption. In this study we used neutral stimuli (tones), which were unlikely to prompt an emotional response when inaccurately identified. Therefore revisiting deviants may not have affected the task performance. However, in speaking, repeatedly visiting a stuttering moment may increase emotional reactivity. Managing the increased emotional reactivity may tax more of the information processing resources available to CWS with low attentional control, leaving fewer resources for speech-language planning and production, and causing more speech disruptions.

4.2.4 Relationship between temperament traits and behavioral and ERP data in the auditory-auditory distraction task Two significant relationships were found in the CWNS group between temperament dimension scores and the behavioral and ERP data obtained in the auditory-auditory distraction task: CWNS who scored high on Attentional Control tended to have a short RT and a short peak latency in eRON. That is, CWNS with good attentional control were able to more efficiently suppress the distraction effect generated by the deviant stimuli, more quickly reorienting their attention to task-relevant information (decreased peak latency in eRON). It is likely that heightened ability to shift attention is one of the main factors in short RT during high-speed tasks. No significant relationships were found in the CWS group, which may due to the small sample size. We noticed that although relationships between the five temperament dimensions and 81

behavioral and ERP data did not reach significance, some of these rs were over .5, such as the peak latency of P3a and the score of Attentional Control (rs = -.643, p = .09). We expect to find more significant relationships between temperament and behavioral and ERP data when using a larger sample size. Another reason for the lack of significant relationships between temperament dimension scores and behavioral and ERP data may be inaccurate data on the temperament questionnaires due to inaccurate recall, parents misinterpreting their child’s behaviors, or responses that reflect parents’ desires rather than the child’s actual behavior (Rothbart & Goldsmith, 1985; Rothbart & Mauro, 1990). These biases can influence parents’ interpretation and rating of their children’s behaviors. Biased reports cannot reflect the actual abilities of children; thus, a possible explanation for why few significant relationships were found between the temperament scores and the behavioral and ERP data, which are a direct measure of children’s abilities and behaviors.

4.3 Visual Search

4.3.1 Behavioral and ERP data, and their relationships to temperament traits No significant between-group difference was found in behavioral performance.

However, we found that both CWS and CWNS tended to respond more quickly and more accurately in the Target 3 condition (color pop-out) than in the other two target conditions (orientation and size pop-outs). There are two possible explanations for this observation. First of all, decreased RT and increased HR may be due to the practice effect, given that the order of presentation for all three target conditions was fixed (Target 1: orientation pop-out, Target 2: size pop-out, Target 3: color pop-out). Second, compared to the difference in orientation (Target 1) and size (Target 2) from non-targets (small blue horizontal bars), the difference in color (Target 3) might be more salient. Therefore, the 82 children in our study responded faster and more accurately to novel color stimuli than they did to stimuli that represented changes in visual orientation or size. The results of ERP analysis revealed no significant between-group differences in either the peak latency or mean amplitude of N2pc between CWS and CWNS. N2pc reflects the process of attentional filtering (i.e., Luck & Hillyard, 1994a). Given the lack of significance in N2pc between the two groups, we conclude that CWS did not differ from their fluent peers in attentional filtering, as reflected by N2pc. That is, CWS performed similarly to CWNS in filtering unwanted stimuli (non-targets: small blue horizontal bars) and focusing on the target. With regard to the correlations between the temperament scores and behavioral and ERP data in the visual search task, significant relationships were found between the score of Effortful Control and RT in CWS. Recall that effortful control refers to self-regulation abilities, including inhibitory and attentional control. CWS who rated relatively high in Effortful Control or its two scales (Attentional and Inhibitory Control) tended to exhibit short RT. This finding suggests that those CWS with higher effortful control were more efficient in inhibiting their response to distractors and maintaining their attention on task targets, leading to faster reaction time. No significant relationships were found between behavioral and ERP data and the five temperament dimensions in CWNS, which may due to the small sample size and biased reports of temperament data. Another reason may be that the task was too easy and CWNS’ performance reached the ceiling level, leaving no room for variability.

4.3.2 Comparison of performance in the visual search and auditory-auditory distraction tasks in CWS Unlike the auditory-auditory task, interestingly, CWS did not produce significant short RTs in combination with low HRs and high FAs in the visual search task, although they were still instructed to respond as fast and as accurately as possible. A possible 83 explanation for the different performances in the auditory-auditory distraction task and the visual search task may be that the task demand on inhibitory control was higher in the auditory-auditory distraction task than in the visual search task. The longer duration of inhibiting prepotent responses (pressing the button) may result in the increased task demand on inhibitory control in the auditory-auditory distraction task. Children were instructed to determine the duration of a tone (long: 400 ms; short: 200 ms) and press a specific button only to long tones in the auditory-auditory distraction task. The earliest time point to determine whether a tone was long or short was 200 ms. Thus, children should refrain from pressing the button until 200 ms after stimulus onset in order to correctly discriminate the duration of a tone. Children with low inhibitory control may have more difficulty refraining from pressing the button for, at least, 200 ms, resulting in more FAs. However, in the visual search task, children were instructed to determine whether a specific target was present in each stimulus array, and press a specific button for target-present arrays. It was not necessary to wait; children could make their decision immediately after the stimulus array was displayed. Thus, the task demand on inhibitory control may be lower in the visual search task. Another reason for the increased task demand on inhibitory control in the auditory-auditory distraction paradigm is the occurrence probability of no-go trials. The occurrence probability of no-go trials was 50% in the auditory-auditory distraction paradigm, and 75% in the visual search task. The higher occurrence probability in no-go trials decreased the inhibitory control demand in the visual search task (Christ et al., 2001). Thus, compared to the auditory-auditory distraction paradigm, the visual search task may be easier for CWS.

4.4 Conclusion

4.4.1 Attentional control in the process of distraction Taken together, our findings converge with previous research results and reveal that CWS were less efficient in attentional control (e.g., Eggers et al., 2012; Schwenk et al., 2007). Although no significant group-difference was found in the behavioral cost (distraction effect on behavioral performance) caused by frequency-change deviants, CWS differed from their fluent peers in the goal-directed processing (cognitive control) during the distraction process. Throughout the various stages of distraction processing, CWS showed no difference in the sensory/perceptual processing, including automatic scanning and change-detection (MMN), involuntary attentional orientation (P3a), and attentional filtering (N2pc). However, CWS were less efficient in cognitive control to counteract distraction, the stage of attentional reorientation (RON). Two phases of RON were found (eRON and lRON). CWS did not differ from CWNS in the ability to redirect attention to task-relevant information in working memory (eRON), but they tended to be less efficient in reevaluating task-relevant information (tone duration), as reflected by lRON. The existence of P600, elicited by violations in rule-governed sequences or the effect of encountering unexpected stimuli, suggests that CWS return to and re-examine the deviant information; as a result, they are delayed in starting the process reflected by lRON: evaluating task-relevant information and preparing a proper response for the task at hand. This tendency to “return and re-evaluate” novel stimuli may result from reduced inhibitory control. Inhibitory control is essential for voluntary attention control (goal-directed processing; Banfield, Wyland, Macrae, Munte, & Heatherton, 2004; Bell & Calkins, 2012; Fuentes, 2004), and its main function is avoiding re-examining task-irrelevant information (Fuentes, 2004) The lower efficiency of inhibitory control in CWS was confirmed by the findings of behavioral performance in the auditory-auditory distraction task. CWS, compared to 85

CWNS, exhibited more false alarms, which is an index of low inhibitory control in a go/no-go task.

4.4.2 Temperament, attentional and inhibitory control and stuttering Consistent with previous findings, our temperament data also revealed that CWS tended to exhibit high negative affect in combination with low effortful control and attentional control. Taken together, our findings support the speculation that CWS, as a group, exhibit relatively high emotional reactivity paired with relatively low emotional regulation. The consequences of a more reactive temperament coupled with lower regulation have been implicated as one of the main contributors to the exacerbation of stuttering in children (Conture & Walden, 2012; Eggers et al., 2010; Johnson et al., 2010; Karrass et al., 2006). Attentional control has been considered important in regulating emotions, and inhibitory control is involved in the process of voluntary attentional control (Fuentes, 2004). The integration of attentional and inhibitory control is important for self-regulation, including emotional regulation. Based on our findings, we speculate that CWS with high emotional reactivity combined with low attentional control may be less efficient in voluntarily re-directing their attention back to speaking after being distracted by a speech disruption. They may be less able to inhibit the influence from the speech disruption and revisit the stuttering moment, leading to increased emotional reactivity. As suggested by the DD-S model (Conture & Walden, 2012), reducing the increased emotional reactivity may tax the limited processing resources, leaving fewer resources for speech-language planning and production. As a result, this may lead to more speech disruptions.

4.5 Limitations and Directions for Future Research The present findings provided evidence to support that CWS, as a group, differ from CWNS in the processing of distraction. Specifically, CWS may be less proficient in distraction compensation. However, there are some limitations that may be overcome in future studies. First, the lack of statistical significance in some findings (i.e., increased P3a in CWS), perhaps reflecting samples that were not sufficiently large is the most obvious limitation. Future studies may consider recruiting a larger sample size. Attention is a multifaceted construct that encompasses different modes (such as involuntary and voluntary attention) and domains (such as visual and auditory attention). In this study, we focused on the four stages of attentional control involved in the process of distraction. Whether or not CWS and CWNS differ in other domains or stages in attentional processing remains unknown. In addition, the performance in attentional control can be affected by emotions. Research has shown that attention is easily captured by affectively salient stimuli (e.g., Eimer & Kiss, 2007; Keil & Ihssen, 2004). Neutral stimuli (tones and bars) were used in this study. Our results showed that CWS and CWNS differed in attentional control in a neutral condition. However, it is still unclear if CWS and CWNS differ in attentional control in emotional conditions and how emotions affect CWS’ performance in controlling their attention. To explore the relationships among emotional variables, attentional control and stuttering, future studies may take the factor of emotion into account and investigate attentional control of CWS in emotion-laden conditions. Moreover, as discussed in Section 4.3.2, CWS perform differently as the task demand on inhibitory control increases. Inhibitory control is one of the important strategies in emotional regulation. Whether or not the ability for inhibitory control also varies across different emotional conditions in CWS remains unknown. Given that emotional regulation may be a protective factor in the development of stuttering, future 87 studies may investigate the influence of emotional and/or linguistic stressors on strategies of emotional regulation, including attentional and inhibitory control.

APPENDIX A TABLES 89

Table A1. Descriptive statistics for behavioral data: The auditory-auditory distraction task.

Performance Stimulus Group Mean(SD) CWS CWNS RT (ms) Deviant 765.39 (85.76) 891.26 (55.53) Standard 724.08 (105.1) 847.3 (85.76) Distraction effect 41.31 (61.52) 43.96 (38.89)

HR (%) Deviant 70.81 (29.03) 88.15 (13.13) Standard 75.21 (20.26) 92.12 (9.39) Distraction effect -4.4 (16.06) -3.97 (13.82)

FA (%) Deviant 27.18 (19.34) 4.26 (5.32) Standard 28.02 (15.03) 6.1 (6.16) Distraction effect -.85 (16.14) -1.84 (3.8) Note. Distraction effect = Deviant minus Standard. 90

Table A2. Major statistical evaluation of effects of stimulus type (standard vs. deviant) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for RT, 2 HR, and FA. F values, p values and ηp are summarized.

Factor df RT HR FA F p F p F p 2 2 2 (ηp ) (ηp ) (ηp ) stimulus 1, 14 10.98** .005 1.25 .283 .21 .654 type (.44) (.082) (.015)

group 1, 14 8.18* .013 3.64 .077 15.22** .002 (.369) (.206) (.521) stimulus 1, 14 .011 .919 .003 .955 .03 .868 type (.001) (.000) (.002) × group *p ≤ .05, **p ≤ .01. 91

Table A3. Descriptive statistics for the peak latency and amplitude of MMN, P3a, RON, and P600 for long tones.

Component Peak latency (ms) Mean amplitude (µV) CWS CWNS CWS CWNS MMN Mean 209.75 207.58 -3.02 -3.03 (SD) (19.43) (21.84) (4.24) (4.74)

P3a Mean 316.92 327 4.18 1.82 (SD) (18) (23.64) (3.7) (5.42)

Early RON Mean 475.08 486.42 -3.38 -2.04 (SD) (35.94) (36.5) (7.89) (6.86)

Late RON Mean 682.83 654.25 -1.16 -2.83 (SD) (35.69) (57.3) (7) (5.97)

P600 Mean 579.92 .800 (SD) (27.55) (9.25) *p ≤ .05. 92

Table A4. Descriptive statistics for the peak latency and amplitude of MMN, P3a, and RON for short tones.

Component Peak latency (ms) Mean amplitude (µV) CWS CWNS CWS CWNS MMN Mean 206 201.58 -2.56 -1.53 (SD) (18.22) (22.19) (5.18) (6.04)

P3a Mean 320.58 323.67 3.53 2.95 (SD) (24.21) (29.73) (6.08) (3.67)

Early RON Mean 484.33 481.67 -1 .96 (SD) (40.69) (41.08) (8.86) (9.64)

Late RON Mean 686.08 630.5 -6.26 -3.01 (SD) (34.83) (38.76) (6.99) (4.43)

P600 Mean 564.67 546.5 -.745 (SD) (29.34) (37.86) (11.1) 93

Table A5. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the peak 2 latency and mean amplitude of MMN. F values, p values and ηp are summarized.

Factor Df Peak latency Mean amplitude F p F p 2 2 (ηp ) (ηp ) tone type 1, 14 .59 .46 .01 .92 (.04) (.001)

group 1, 14 .17 .69 .14 .72 (.01) (.01)

tone type 1, 14 .03 .86 .42 .53 × (.002) (.03) group *p ≤ .05. 94

Table A6. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the peak 2 latency and mean amplitude of P3a. F values, p values and ηp are summarized.

Factor df Peak latency Mean amplitude F p F p 2 2 (ηp ) (ηp ) tone type 1, 14 .001 .98 .03 .87 (.000) (.002)

group 1, 14 .39 .54 .59 .46 (.03) (.04)

tone type 1, 14 .34 .57 .37 .56 × (.02) (.03) group *p ≤ .05. 95

Table A7. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the peak 2 latency and mean amplitude of eRON. F values, p values and ηp are summarized.

Factor df Peak latency Mean amplitude F p F p 2 2 (ηp ) (ηp ) tone type 1, 14 .06 .82 1.61 .23 (.004) (.1)

group 1, 14 .07 .8 .21 .66 (.005) (.02)

tone type 1, 14 .54 .47 .02 .89 × (.04) (.002) group *p ≤ .05. 96

Table A8. Major statistical evaluation of effects of tone type (long vs. short) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs separately for the peak 2 latency and mean amplitude of lRON. F values, p values and ηp are summarized.

Factor df Peak latency Mean amplitude F p F p 2 2 (ηp ) (ηp ) tone type 1, 14 .87 .37 1.27 .28 (.06) (.08)

group 1, 14 5.38* .04 .15 .7 (.28) (.01)

tone type 1, 14 1.49 .24 1.1 .31 × (.1) (.07) group *p ≤ .05. 97

Table A9. Descriptive statistics for behavioral data: The visual search task.

Condition RT (ms) HR (%) FA (%) CWS CWNS CWS CWNS CWS CWNS Target 1 Mean 680.12 618.52 92.54 96.35 1.74 1.1 (SD) (38.15) (59.13) (7.31) (2.08) (1.04) (0.66)

Target 2 Mean 625.81 605.03 95.05 95.44 1.81 3.21 (SD) (57.94) (48.03) (4.62) (4.16) (1.33) (5.67)

Target 3 Mean 572.66 527.6 96 99.09 0.67 0.61 (SD) (69.67) (29.38) (3.17) (1.03) (0.56) (0.51)

Overall Mean 625.44 584.97 94.53 96.8 1.4 1.77 (SD) (52.37) (42.68) (4.05) (1.45) (0.75) (2.06) 98

Table A10. Major statistical evaluation of effects of target condition (Target 1 vs. Target 2 vs. Target 3) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs 2 separately for RT, HR, and FA. F values, p values and ηp are summarized.

Factor df RT HR FA F p F p F p 2 2 2 (ηp ) (ηp ) (ηp ) target 2, 28 62.98*** <.001 3.11 .06 2.57 .129 condition (.82) (.18) (.155)

group 1, 14 3.15 .098 2.62 .128 .1 .762 (.18) (.158) (.007)

target 2, 28 2.61 .091 .98 .39 .8 .39 condition (.18) (.07) (.054) × group ***p ≤ .001. 99

Table A11. Descriptive statistics for the peak latency and mean amplitude of N2pc.

Target Peak latency (ms) Mean amplitude (µV) Condition CWS CWNS CWS CWNS Target 1 Mean 305.78 307.07 -3.02 -3.03 (SD) (42.33) (34.94) (4.24) (4.74)

Target 2 Mean 267.31 270.28 -1.72 -0.85 (SD) (50.6) (73.52) (1.4) (2.63)

Target 3 Mean 287.46 286.7 -0.94 -0.79 (SD) (57.67) (28.87) (3.68) (3.87) 100

Table A12. Major statistical evaluation of effects of target condition (Target 1 vs. Target 2 vs. Target 3) and group (CWS vs. CWNS) by means of repeated-measures ANOVAs 2 separately for the peak latency and mean amplitude of N2pc. F values, p values andηp are summarized.

Factor df Peak latency Mean amplitude F p F p 2 2 (ηp ) (ηp ) target 2, 28 2.56 .1 1.98 .16 condition (.15) (.12)

group 1, 14 .01 .94 1.23 .29 (.00) (.08)

target 2, 28 .01 .99 .54 .58 condition (.00) (.04) × group *p ≤ .05. 101

Table A13. Means, standards deviations, and between-group analysis of temperament dimension scores for CWS and CWNS.

Dimension CWS CWNS Mean SD Mean SD t p d Surgency 3.49 0.38 3.18 0.43 1.51 .15 .809

Negative 2.88 0.66 2.27 0.56 1.98 .067 1.06 Affectivity

Effortful 3.18 0.29 3.64 0.57 2.05 .06 1.095 Control

Attentional 2.85 0.82 3.78 0.5 -2.72* .017 1.454 Control

Inhibitory 3.11 0.48 3.60 0.64 -1.74 .104 .929 Control *p ≤ .05. 102

Table A14. Spearman’s rank correlations between temperament dimension scores and behavioral data in the auditory-auditory distraction task for CWS and CWNS.

Dimension CWS CWNS RT HR FA RT HR FA Surgency -.095 -.476 -.156 -.214 .024 .643

Negative -.515 -.18 .181 .024 .119 -.667 Affectivity

Effortful .476 .476 .036 -.524 .5 -.071 Control

Attentional .667 .429 -.443 -.755* .695 -.515 Control

Inhibitory .31 .595 .06 -.491 .371 .252 Control *p ≤ .05. 103

Table A15. Spearman’s rank correlations between temperament dimension scores and distraction effects on HR, RT, and FA in the auditory-auditory distraction task for CWS and CWNS.

Dimension CWS CWNS DERT DEHR DEFA DERT DEHR DEFA Surgency -.143 -.595 .381 -.071 -.071 .095

Negative .12 -.359 -.455 .071 .024 .048 Affectivity

Effortful .119 .333 .286 .619 -.286 .643 Control

Attentional -.19 .262 -.238 .678 -.671 .505 Control

Inhibitory .19 .619 .071 .407 -.108 .419 Control Note. DERT = Distraction effect on reaction time. DEHR = Distraction effect on hit rate. DEFA = Distraction effect on false alarm. *p ≤ .05. 104

Table A16. Spearman’s rank correlations between temperament dimension scores and the peak latency of MMN for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency .06 .614 .563 -.614

Negative -.309 -.132 -.635 -.262 Affectivity

Effortful -.12 .333 .563 .833 Control

Attentional -.133 .286 .078 .078 Control

Inhibitory .12 .548 .611 .491 Control

*p ≤ .05. 105

Table A17. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of MMN for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency .238 .19 -.143 .238

Negative -.467 -.12 -.476 -.048 Affectivity

Effortful .19 .119 .31 .214 Control

Attentional .19 .048 -.132 .455 Control

Inhibitory .095 .333 -.491 .228 Control

*p ≤ .05. 106

Table A18. Spearman’s rank correlations between temperament dimension scores and the peak latency of P3a for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency .024 -.262 -.18 -.357

Negative .491 -.431 .048 .214 Affectivity

Effortful -.262 .476 .012 -.286 Control

Attentional -.643 .214 -.343 -.371 Control

Inhibitory -.286 .429 .036 -.431 Control

*p ≤ .05. 107

Table A19. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of P3a for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency .452 .119 .333 .548

Negative -.683 -.419 -.238 -.548 Affectivity

Effortful .429 .310 .357 .681 Control

Attentional .31 .024 .263 .117 Control

Inhibitory .238 .333 .407 .684 Control

*p ≤ .05. 108

Table A20. Spearman’s rank correlations between temperament dimension scores and the peak latency of eRON for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency .524 .405 -.072 -.548

Negative -.611 -.299 -.096 .571 Affectivity

Effortful .024 -.119 -.515 .119 Control

Attentional .048 .405 -.892** .275 Control

Inhibitory .048 -.19 -.343 -.168 Control

*p ≤ .05, **p ≤ .01. 109

Table A21. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of eRON for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency -.214 -.676 .119 -.548

Negative -.192 .036 .024 .548 Affectivity

Effortful .19 .452 .143 -.662 Control

Attentional .071 .333 .599 -.431 Control

Inhibitory .286 .619 .036 -.683 Control

*p ≤ .05. 110

Table A22. Spearman’s rank correlations between temperament dimension scores and the peak latency of lRON for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency .286 .381 .286 -.429

Negative .323 -.144 -.381 .190 Affectivity

Effortful -.262 -.119 .214 -.095 Control

Attentional -.238 .048 .261 -.287 Control

Inhibitory -.286 -.095 .144 -.275 Control

*p ≤ .05. 111

Table A23. Spearman’s rank correlations between temperament dimension scores and the mean amplitude of lRON for CWS and CWNS.

Dimension CWS CWNS Long tones Short tones Long tones Short tones Surgency -.095 .095 -.286 -.19

Negative .036 .347 .333 .024 Affectivity

Effortful .286 -.476 -.429 -.286 Control

Attentional -.429 -.19 .072 -.323 Control

Inhibitory .119 -.286 -.443 -.395 Control

*p ≤ .05. Table A24. Spearman’s rank correlations between temperament dimension scores and behavioral data in the visual search task for CWS and CWNS.

Target CWS CWNS Condition Sur Neg Effco Att Inh Sur Neg Effco Att Inh Aff Aff Target 1 RT .214 .275 -.571 -.452 -.381 .071 -.048 -.238 -.395 -.096 HR .119 .096 -.071 -.214 -.024 -.266 .190 .406 .549 .128 FA -.347 .187 .359 -.132 .192 .410 -.458 -.145 -.200 .127

Target 2 RT .500 .659 -.905** -.738* -.881* -.167 .048 -.524 -.611 -.335 HR .000 .037 .073 -.049 -.881 -.122 .268 .171 .270 .037 FA -.180 -.410 .335 .132 .323 .366 -.415 -.122 -.221 .123

Target 3 RT .381 .275 -.667 -.524 -.571 .190 -.286 .000 -.299 .108 HR -.361 .006 .000 .410 .241 -.091 .026 -.274 .020 -.138 FA -.265 -.242 .386 -.048 .374 .115 -.217 -.396 -.674 -.308

Overall RT .333 .395 -.714* -.571 -.595 .190 -.238 -.262 -.467 -.036 HR .048 .120 .000 -.143 .024 -.404 .464 .220 .344 -.012 FA -.119 -.229 .500 .119 .405 .310 -.381 -.167 -.275 .084

Note. Sur = Surgency. Neg Aff = Negative Affect. Effco = Effortful Control. Att = Attentional Control. Inh = Inhibitory Control. *p ≤ .05, **p ≤ .01. 112 Table A25. Spearman’s rank correlations between the five temperament dimensions and the ERP data obtained from the visual search task for CWS and CWNS.

Target CWS CWNS Condition Sur Neg Effco Att Inh Sur Neg Effco Att Inh Aff Aff Target 1 Peak .357 .168 -.095 -.119 -.238 .071 -.143 .333 .012 .240 lat. Mean .524 -.443 -.048 .190 -.024 -.143 .071 -071 -.491 -.048 amp.

Target 2 Peak .024 .263 -.071 -.024 -.048 -.595 .619 -.333 .024 -.575 Lat. Mean .048 .539 -.238 -.286 -.429 -.190 .190 -.238 -.323 -.168 amp.

Target 3 Peak .190 .012 -.024 .048 -.190 .143 -.310 -.310 -.132 .395 Lat. Mean .524 -.275 -.238 -.286 -.143 -.310 .095 -.143 -.407 -.263 amp.

Overall Peak .548 .228 -.286 -.310 -.429 .619 -.452 .452 .611 .515 Lat. Mean -.119 -.299 -.500 .119 -.405 -.119 .024 -.476 -.407 -.275 amp. Note. Latency = lat. Amplitude = amp. Sur = Surgency. Neg Aff = Negative Affect. Effco = Effortful Control. Att = Attentional Control. Inh = Inhibitory Control. *p ≤ .05. 113 114

APPENDIX B FIGURES

115

Figure B1. Relative placement of 30 scalp electrodes on the electrode cap used in this study. 116

A. Stimulus Type

Standard tones Deviant tones

Duration 400 ms 200 ms 400 ms 200 ms Frequency 1000 Hz 900 or 1100 Hz Probability 90% 10%

B. Stimulus sequence

1800 ms

Stimulus

Figure B2. Stimulus type and sequence for the auditory-auditory distraction paradigm. (A) There were two types of stimuli: Standard and deviant tones. Standard tones (90% of all trials) were presented with a frequency of 1,000 Hz. Deviant tones (10% of all trials) were either higher or lower in pitch by 10% (either 900 or 1,100 Hz) from the standard. (B) Stimuli were presented every 1800 ms. The sequence of stimulus presentation was pseudo-randomized; a deviant tone was always followed by three standard tones. 117

Homogeneous Small Blue Horizontal: Orientation Pop-Out

＋＋

Large Blue Vertical: Small Green Vertical: Size Pop-Out Color Pop-Out

＋＋

Figure B3. Examples of the stimulus arrays. The upper left is the homogeneous stimulus array of eight small, blue, vertical bars. The other three are pop-out stimulus arrays, which consist of seven small, blue, vertical bars and one of three pop-out bars. The bars were placed at random locations within an imaginary rectangle which subtended 9.2 × 6.9 degrees of visual angle that was centered around a fixation point. Four types of stimulus arrays were presented randomly and equally: 25% probability for each type. Long tones

F3 FZ F4 μV μV -10 -10 -10 μV

CWS ms ms ms -100 200 400 600 -100 200 400 600 -100 200 400 600

10 10 10

F3 FZ F4

μV μV μV -10 BIN32: Std long_button press_within-10 200_1500 -10

BIN33: Dev long_button press_within 200_1500 CWNS ms ms ms -100 200 400 600 -100 200 400 600 -100 200 400 600 BIN42: Correct Long Diff;

10 10 10 Standard Deviant Deviant －Standard

Figure B4. Grand average ERPs forBIN9: standard Std long_button (black press_within line) and 200_1500 deviant (red line) stimuli and difference waveforms (deviant-standard; blue line) for long tones at selected electrodes, separately for CWS and CWNS. 118 BIN10: Dev long_button press_within 200_1500

BIN19: Correct Long Diff;

Short tones

F3 FZ F4

μV μV -10 -10 μV -10 CWS

ms ms ms -100 200 400 600 -100 200 400 600 -100 200 400 600

10 10 10

F3 FZ F4 BIN34: Std short_no response_Correct1

-10 μV -10 μV -10 μV

CWNS BIN35: Dev short_no response_Correct2 ms ms ms

-100 200 BIN43:400 Correct600 Short Diff; -100 200 400 600 -100 200 400 600

10 10 10 Standard Deviant Deviant －Standard

BIN11: Std short_no response_Correct1 Figure B5. Grand average ERPs for standard (black line) and deviant (red line) stimuli and difference waveforms (deviant-standard; blue line) for short tones at selected electrodes, separately for CWS and CWNS. BIN12: Dev short_no response_Correct2 119

BIN20: Correct Short Diff;

Difference waves

F3 FZ F4 μV μV μV -5 -5 lRON -5 MMN eRON

Long tones ms ms ms -100 200 400 600 -100 200 400 600 -100 200 400 600

P600 5 5 5

P3a

F3 FZ F4

μV μV μV -5 -5 -5

BIN19: Correct Long Diff; Short tones ms ms ms -100 200 400 600 -100 200 400 600 -100 200 400 600

BIN42: Correct Long Diff; 5 5 5

CWS CWNS

Figure B6. Comparisons of the difference waveforms between CWS (red line) and CWNS (black line) at selected electrodes, separately for long and short tones. 120

BIN20: Correct Short Diff;

BIN43: Correct Short Diff;

Target 1

-10 P3/P4 -10 P7/P8 -10 O1/O2 μV μV μV N2pc CWS ms ms ms -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

10 10 10

-10 P3/P4 -10 P7/P8 -10 O1/O2 μV μV μV BIN19: Ori Ipsi; CWNS ms ms ms -100 100 200 300 400 500 600 700BIN20: Ori Contra;-100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

10 BIN28: Ori N2pc 10Diff; 10

Ipsilateral Contralateral Contralateral－Ipsilateral

Figure B7. Grand average ERP waveforms elicited by the target arrays at posterior electrodes ipsilateral (black line) and contralateral (red line) to the target locationBIN3: in Target Ori Ipsi; 1 condition. Difference waves (contralateral – ipsilateral; blue line) were computed to isolate the N2pc component. BIN4: Ori Contra; 121

BIN12: Ori N2pc Diff; Target 2

-10 P3/P4 -10 P7/P8 -10 O1/O2 μV μV μV N2pc CWS ms ms ms -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

10 10 10

-10 P3/P4 -10 P7/P8 -10 O1/O2 μV μV μV

ms ms ms CWNS BIN21: Size Ipsi; -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

10 BIN22: Size Contra;10 10

BIN29: Size N2pc Diff; Ipsilateral Contralateral Contralateral －Ipsilateral

Figure B8. Grand average ERP waveforms elicited by the target arrays at posterior electrodes ipsilateral (black line) and contralateral (red line) to the target locationBIN5: in Target Size Ipsi; 2 condition. Difference waveforms (contralateral – ipsilateral; blue line) were computed to isolate the N2pc component. 122

BIN6: Size Contra;

BIN13: Size N2pc Diff;

Target 3

-10 P3/P4 -10 P7/P8 -10 O1/O2 μV μV μV N2pc ms CWS ms ms -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

10 10 10

-10 P3/P4 -10 P7/P8 -10 O1/O2 μV μV μV

ms ms ms CWNS BIN23: Color Ipsi; -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

10 BIN24: Color Contra;10 10

BIN30: Color N2pc Diff; Ipsilateral Contralateral Contralateral －Ipsilateral Figure B9. Grand average ERP waveforms elicited by the target arrays at posterior electrodes ipsilateral (black line) and contralateral (red line) to the target location in Target 3 condition. Difference waves (contralateral – ipsilateral; blue line) were computed to isolate the N2pc component.BIN7: Color Ipsi; 123 BIN8: Color Contra;

BIN14: Color N2pc Diff;

1000 Group: CWS CWNS

800

600

RT (ms) 400

200

0 10 20 30 40 FA (%)

Figure B10. A scatter plot of the percentage of FA and RT (N = 16). Different symbols are used to represent two subject groups: triangle for CWS and circle for CWNS. Three triangles in the dashed ellipse are CWS with low FAs in combination with slow RTs. 124

CWS with low FAs and slow RTs

F3 FZ F4 μV μV μV -10 -10 -10

-5 -5 -5

ms ms ms -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

5 5 5 P600

10 10 10

Long tones Short tones

Figure B11. Difference waveforms forBIN19: three Correct CWS Long with Diff; low FAs and slow RTs in the long (black line) and short (red line) tone conditions at selected electrodes.

BIN20: Correct Short Diff; 125

CWS

F3 FZ F4 μV μV ERN μV -10 -10 -10

-5 -5 -5

ms ms ms -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700 -100 100 200 300 400 500 600 700

5 5 5

Long tones Short tones FAs

Figure B12. Comparisons of the differenceBIN19: waveformsCorrect Long Diff; between correct (ERPs for long and short tones) and erroneous (FAs) trials in CWS at selected electrodes.

BIN20: Correct Short Diff;

BIN24: FA 126 127

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