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The Effect of Musical Training on Verbal and Tonal Working Memory Ching-I Lu Wayne State University

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The Effect of Musical Training on Verbal and Tonal Working Memory Ching-I Lu Wayne State University Wayne State University DigitalCommons@WayneState Wayne State University Dissertations 1-1-2012 The effect of musical training on verbal and tonal working memory Ching-i Lu Wayne State University, Follow this and additional works at: http://digitalcommons.wayne.edu/oa_dissertations Recommended Citation Lu, Ching-i, "The effect of musical training on verbal and tonal working memory" (2012). Wayne State University Dissertations. Paper 548. This Open Access Dissertation is brought to you for free and open access by DigitalCommons@WayneState. It has been accepted for inclusion in Wayne State University Dissertations by an authorized administrator of DigitalCommons@WayneState. THE EFFECT OF MUSICAL TRAINING ON VERBAL AND TONAL WORKING MEMORY by CHING-I LU DISSERTATION Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2012 MAJOR: COMMUNICATION SCIENCES & DISORDERS Approved By Advisor Date ACKNOWLEDGMENTS I would like to thank all the people who have helped me in the process of completing this dissertation. First, I would like to express my gratitude to my advisor, Dr. Margaret Greenwald, for her support and guidance in academia and everyday life throughout my five years in the doctoral program. I would also like to thank my committee members: Dr. Anthony Cacace, an expert in audiology, who offered me an opportunity to learn TMS; Dr. Jean Andruski, an expert in linguistics, who has always nurtured me and offered me to attend conferences around the world; Dr. Mark Greenwald is the only person who was able to use a metaphor, such as the Leitmotif (leading motive) of Richard Wagner, to explain academic writing to me. My committee members have always offered insights that not only challenged my thinking, but also encouraged me to further improve my work. I would like to thank Dr. Susan Bowyer for offering me an opportunity to learn MEG; Dr. Li Hsieh, as a friend, who has always supported me. Dr. Derek Daniels, as a collaborator, who allowed me access to his dataset for different analysis; I have also learned much about stuttering from him. Special thanks to my friends who have been supporting me over these five years. Many thanks to Siny at UVA; when I felt frustrated, you have sent many cute pictures of Marbles to make me smile. Thank you so much. Many thanks to Jack at UW, Seattle, who came to the States the same year as I did. We encouraged and complained to each other by phone every day during our first year in our graduate program and traveled around to so many National Parks in US and Canada during these five years. Many thanks for Lai-Yee and Ting-Yi: we met each other in Detroit. I will always miss having dinner with you on Friday nights. We encouraged and supported each other; you are my friends forever. Many thanks for Fang-Tzu at BGSU, who is a ii long time friend from my college always supporting me. Thank you, Vanessa, for giving me a ride to grocery shopping every week when I did not have a car in my first year in Detroit. Thank you, Daniel at Yale U, your statistical and psychology knowledge has helped me clarify many concepts. Many thanks to Andrea, Carol, Denise, Nicole, and Sean. Once again, thank you, all my friends; I could not have achieved so much without your help. I would also like to thank my younger brother and my sister-in-law, who took care of my parents when I was away from home and supported me financially. Without your help, I could not have completed everything in the US. I would like to dedicate my dissertation to my parents. Thank you for your support and encouragement to follow my dreams; my dream would not have come true without you. iii TABLE OF CONTENTS Acknowledgments ii List of Tables vi List of Figures vii Chapter 1 Introduction 1 Chapter 2 Review of the Literature 3 Cognitive-Linguistic Processes in Reading 3 A theoretical model of English reading 4 The Chinese writing system 8 A theoretical model of Chinese reading 12 A theoretical model of reading music 16 Components of Verbal Working Memory 17 The phonological loop 18 Characteristics of the phonological loop 19 Other factors affecting the phonological loop 26 The central executive 33 Working Memory for Tone Information 34 Working memory and reading Mandarin Chinese 35 Working memory for musical tone 37 Central executive function in musical working memory 41 Chapter 3 Method 47 Participants 47 Experimental Materials 48 iv Design of the Study 49 Procedure 55 Scoring and Data Screening 57 Chapter 4 Results 59 Comparisons of Performance Accuracy 59 Comparisons of Performance Speed 68 Comparing the Effects of Visually Similar Stimuli 75 The English Rhyming Task: Effects of Visual Similarity on Accuracy 77 The Mandarin Homophone Task: Effects of Visual Similarity on Accuracy 79 The Music Task: Effects of Visual Similarity on Accuracy 81 The English Rhyming Task: Effects of Visual Similarity on Reaction Time 85 The Mandarin Homophone Task: Effects of Visual Similarity on Reaction Time 87 The Music Task: Effects of Visual Similarity on Reaction Time 89 Chapter 5 Discussion 92 The Effect of Musical Training on Verbal and Tonal Working Memory 92 The Effect of Visual Similarity of Target Stimuli 98 Conclusion 100 Limitations and Future Studies 101 Appendix All Stimuli 104 References 112 Abstract 128 Autobiographical Statement 129 v LIST OF TABLES Table 3.1: Demographic Data of Musicians and Non-musicians 48 Table 3.2: Response Patterns of 1-back and 2-back Tasks 57 Table 4.1: Mean Accuracy Rates of 1-back Tasks by Group 60 Table 4.2: Mean Accuracy Rates of 2-back Tasks by Group 61 Table 4.3: Results of Three-Way Mixed Model ANOVA: Accuracy for Group by Task by Difficulty Level 62 Table 4.4: Mean Reaction Times of 1-back Tasks by Group 69 Table 4.5: Mean Reaction Times of 2-back Tasks by Group 69 Table 4.6: Results of Three-Way Mixed Model ANOVA: Reaction Times for Group by Task by Difficulty Level 70 Table 4.7: Accuracy for Four Categories of Target Stimuli by Group 77 Table 4.8: Reaction Time for Four Categories of Target Stimuli by Group 85 vi LIST OF FIGURES Figure 2.1 An example of musical notation 16 Figure 2.2 Schematic of the phonological loop with visual and auditory input (Vallar, 2006). 30 Figure 3.1 Example stimuli for the Musical note task. 51 Figure 3.2 Number of items under each category in 1-back paradigm. 52 Figure 3.3 An Example of 1-back paradigm shown in a graphic format. 53 Figure 3.4 Number of items in each category in 2-back paradigm. 54 Figure 3.5 An Example of the 2-back paradigm. 55 Figure 4.1 Accuracy for all participants in the 1-back vs. 2-back tasks. 63 Figure 4.2 Accuracy rates for musicians and non-musicians in the 1-back tasks. 65 Figure 4.3 Accuracy rates for musicians and non-musicians in the 2-back tasks. 67 Figure 4.4 Reaction times for all participants in the 1-back tasks vs. 2-back tasks 71 Figure 4.5 Reaction times of musicians and non-musicians for the 1-back tasks. 73 Figure 4.6 Reaction times of musicians and non-musicians for the 2-back tasks. 74 Figure 4.7 Accuracy rates for each stimulus category for English rhyming task in both groups. 78 Figure 4.8 Accuracy rates for each stimulus category for Mandarin homophone task in both groups. 80 Figure 4.9 Accuracy rates for each stimulus category for the music task in both groups. 82 Figure 4.10 Reaction time for each stimulus category for the English rhyming task in both groups. 86 Figure 4.11 Reaction time for each stimulus category for the Mandarin homophone task in both groups. 88 Figure 4.12 Reaction time for each stimulus category for the music task in both groups. 90 vii 1 CHAPTER 1 INTRODUCTION Music and language require complex cognitive systems of the human mind. Both types of information processing depend on temporal memory storage, and there is some evidence that working memory systems for language and music may overlap. Despite an increasing interest in the comparison of working memory for language and music, there is a lack of behavioral paradigms for directly comparing these forms of working memory, especially as related to reading. No study has examined working memory for linguistic tone information in a tonal language, and few studies have examined working memory for musical tone (Berz, 1995; Ockelford, 2007), especially comparisons between musicians and non-musicians. There is little research on how working memory for phonological information compares to working memory for tone information, both of which are involved in working memory for tonal languages such as Mandarin Chinese (Jing & Lu, 2009; Lu & Zhang, 2007; So & Siegel, 1997; Xu & Li, 2009). Studies addressing the relationship between verbal working memory and musical working memory have focused on the presumed effect of musical training on central executive function (Degé, Kubicek & Schwarzer, 2011; Hargreaves & Aksentijevic, 2011; Moreno, Bialystok, Barac, Schellenberg, Cepeda & Chau, 2011; Schellenberg, 2011). There is some evidence that musical training results in improved executive function, inhibitory control and verbal ability (Degé, Kubicek & Schwarzer, 2011; Moreno et al., 2011). Musical training appears to alter working memory for music, such that in recalling musical tones musicians are better able to discriminate pitches that are close together as compared to non-musicians (Williamson, Baddeley & Hitch, 2010). Musical skill training has been found to influence working memory capacity (Lee, Lu & Ko, 2007).
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