Zijin Yao
Yue Li- 1School of Arts, Beijing Language and Culture University, Beijing, China
- 2Department of Music, Beijing Institute of Education, Beijing, China
Individual zone of optimal functioning (IZOF) is a psychological model studied and applied to quantify athletes’ anxiety and predicts their achievement in sports competitions. This study aimed to determine the application of the IZOF model to evaluate music performance anxiety (MPA) in pianists because the causes of anxiety in athletes and musicians may be similar. A total of 30 college-level piano-major students were included in the study, and the anxiety level in performance was scored by the Competitive State Anxiety Inventory-2 questionnaire. In the first phase, participants recalled and self-scored the four important performances in the past year. Notably, seven piano teachers scored the performances. Both results were combined to identify the individual IZOF zone. Each student showed different anxiety scores for cognitive state anxiety (CA), somatic state anxiety (SA), and self-confidence (SC). In the second phase, all participants scored their anxiety level 1 day before the final performance, and the same judges evaluated the performance immediately afterward. A total of 60% of the participants who had at least two subscales inside the IZOF received performance scores greater than 90. In conclusion, the IZOF model provides information for both piano teachers and pianists to help review their anxiety intensity and predict their performance scores to some extent.
Introduction
Research on music performance anxiety (MPA) has been conducted for several decades and is still ongoing (Fishbein et al., 1988; Steptoe, 2001; Kenny and Osborne, 2006; Kenny, 2011; Topoğlu, 2014; Guyon et al., 2020b). MPA is a globally negative and debilitating psychological phenomenon in musicians regardless of age, gender, experience, practicing time, and music genre (Brugués, 2011a,b; Studer et al., 2011; Barbar et al., 2014; Nusseck et al., 2015; Bannai et al., 2016; Sousa et al., 2016; van Fenema et al., 2017; Burin et al., 2019; Guyon et al., 2020a). MPA had been identified in music students and shown statistically significant differences in various psychological constructs, including optimism, self-efficacy, achievement motivation, and sensitivity to reward and punishment (Alzugaray et al., 2016). A significant relationship was reported between the age of starting musical training and the individual’s current perceived level of MPA; students who started at the age of 7 or younger showed lower levels of MPA (Zarza-Alzugaray et al., 2018). Furthermore, the MPA level increased among advanced conservatory students during their 4-year university-level studies (Casanova et al., 2018). A previous study revealed that 33.9% of participants had used substances to cope with MPA, and more than half of them had considered abandoning their musical studies. Participants who used substances had more frequent thoughts of giving up their musical career and had a higher level of MPA than control students (Orejudo Hernández et al., 2018). The relevance of family support for self-efficacy in public performance was mediated through MPA directly and showed consequent differences between genders (Zarza-Alzugaray et al., 2020). Social supports, such as the role of parents, teachers, and peers, were crucial for predicting self-efficacy for learning in students from advanced music schools (Orejudo et al., 2021). Nevertheless, MPA is a validated construct that can harm musicians’ performance quality and their careers (Osborne and Kenny, 2005; Yoshie et al., 2009; Davison, 2020).
Musicians may be ashamed to admit that they are suffering from performance anxiety (Bodner and Bensimon, 2008; Brugués, 2009). However, performance anxiety represented a series of psychosomatic manifestations and was a furtive concept for musicians, causing doubt about their performance quality (Lee, 1988). In addition, music educators had often consciously avoided this issue in their teaching process since anxiety management was typically beyond their training, talent, practice, experience, and dedication (Nideffer and Hessler, 1978).
Numerous strategies have been presented in previous studies and were shown to be widely used to control and improve the physical responses to MPA, including music-assisted progressive muscle relaxation, relaxation breathing training, yoga, physical activity, improvisation-assisted desensitization, psychoanalytic and cognitive-behavioral therapies, imagery-based interventions, acceptance and commitment therapy, music performance skills course, oxytocin intake, and expressive writing intervention (Kim, 2008; Su et al., 2010; Khalsa et al., 2013; Rocha et al., 2014; Studer et al., 2014; Spahn, 2015; Finch and Moscovitch, 2016; Kenny et al., 2016; Juncos et al., 2017; Cohen and Bodner, 2018; Fernholz et al., 2019; Zhukov, 2019; Clarke et al., 2020; Sabino et al., 2020; Shaw et al., 2020; Tang and Ryan, 2020).
Anxiety was thought to have both facilitated and attenuated individuals’ performances (Burton and Naylor, 1997). Performers with facilitative anxiety often described it as excitement, being pumped, or being “in the zone,” and they did not seek help from psychologists or other treatment professionals for assistance to reduce their anxiety (Lehrer et al., 1990; Robertson and Eisensmith, 2010). Wolfe also noted that MPA had positive effects on performance and explained these as an adaptive component of MPA (Wolfe, 1989). The adaptive component, also known as functional anxiety, readied the performer for the challenge ahead by directing preparatory arousal into practical task-oriented actions (Mor et al., 1995). Therefore, anxiety reduction may not be the most appropriate strategy for intervention to manage performance anxiety and achieve peak performance (Chamberlain and Hale, 2007). Increasing clinical reports, especially in the field of music performance, shows that some musicians need to experience pre-performance anxiety to perform at their best level (Nideffer and Hessler, 1978). In these cases, MPA was viewed as a more positive emotion in the performance of specific individuals (Kendrick et al., 1982; Brodsky, 1996; Kim, 2005; McGinnis and Milling, 2005).
Meanwhile, MPA was reported to be a more neutral concept and was viewed as a daily healthy aspect of stress and anxiety intrinsic to the music profession. Brodsky pointed out the complex designs of previous studies and revealed the misleading definitions and ineffective remedies for managing performance-related psychological problems in musicians, indicating that the interaction between anxiety level and actual performance remained in question and needed more research (Brodsky, 1996).
We would naturally hesitate to face these contradictory views and treatments relative to MPA. If there is a type of anxiety that facilitates performance, how would it present? If this anxiety feels differently to different individuals, what would be the difference between those who perceive anxiety as excitement and those who perceive anxiety as a catastrophe?
Various representative theories explained the relationship between performance and emotions (reflecting upon mental and physical arousal). Sports psychologists increasingly agreed that unidimensional approaches to the arousal-performance or anxiety-performance relationship were ineffective and simplistic (Hanin, 2000). Thus, approaches that used a single cumulative score of anxiety to demonstrate the relationship between performance and emotions were inadequate for examining an occupation with the complex emotional and motor skill requirements of music performance. More multidimensional approaches were called for anxiety-related research.
In the 1980s, Hanin introduced the theory of the individual zone of optimal functioning (IZOF), which proposed that an athlete’s performance was successful when his or her precompetitive anxiety was within or near the optimal zone (Hanin, 2000). It was a theoretical, multidimensional approach to describe, predict, and explain athletes’ performance-related, biopsychosocial states that affected individual activity. Athletes were asked to imagine their biopsychosocial states, and a personal IZOF was established to predict their future performances. The IZOF proposed that an athlete’s performance was successful when his or her pre-competitive anxiety was within or near the IZOF, which had been widely applied among athletes (Hanin, 2000, 2010; Harmison, 2006; Robazza et al., 2016, 2018; Ruiz et al., 2017, 2019; Cooper et al., 2021), and in physical activity at school (Robazza and Bortoli, 2005; Morano et al., 2020).
Compared to research exploring emotions in sports and the individual optimal zone, far less research had been published applying IZOF theory on MPA treatments (Yao, 2016). In music performance circumstances, as the subjective experience of anxiety varied from person to person, the optimal zone differed from person to person. By defining the optimal functioning zone for individual pianists and predicting upcoming performance results, this study verified that IZOF can still effectively describe, predict, explain, and regulate piano performance-related bio-psycho-social states as well. In particular, the location and width of the IZOF helped determine a possible range of performance scores.
We conducted a pilot study and found the IZOF zone in two cases. The best performances by these two pianists were presented in the IZOF zone with a significantly higher IZOF score than the average out-of-zone score (Yao, 2016). In this retrospective study, IZOF was assumed to be fully applied in piano performance analysis. Moreover, the performance prediction process showed that it was vital to know each pianist’s IZOF since it varied widely from person to person and may determine each pianist’s personal mental and physical practice.
This study aimed to clarify pianists’ personal IZOF zone, assess the contribution of MPA on their optimal performance, and examine the prediction accuracy of future performance results. This information may help pianists to prepare more and regulate their mental and physical states before future performances.
Materials and Methods
Participants
A total of 30 participants aged between 18 and 24 years were enrolled, including 7 male and 23 advanced female pianists. Participants were all undergraduates with piano majors in a conservatory in Beijing, China. The students came from 13 different provinces in China. At the time of the survey, 6 were sophomores, 10 were juniors, and 14 were seniors, all in a 4-year bachelor’s degree system. The study protocol is shown in Figure 1.
Figure 1. The study protocol.
Ethics Statement
This study was conducted anonymously. No names or other identifying personal data were recorded. Consent forms were sent to the participants to be filled out and signed before the study began, and all included students provided signed informed consent. This study had no risks associated with the physical or psychological state of the participants.
Scoring the Piano Performances
The performances of each participant were evaluated by a group of seven professional college piano teachers. Each teacher evaluated the performance of each participant on a scale of 1–100, where 1 = worst possible performance and 100 = best possible performance. The judges were told to score the performance immediately after the performances based on the participants’ playing. The score was to represent an overall impression of their performances. The highest and lowest scores were removed in the final grading, and the remaining five scores were selected and used to calculate the average score of the final performance result.
The First Phase of This Study: Locating the Zone
In the first phase of the study, participants were required to reflect upon their past four performances (2 mid-term and 2 final examinations in the past academic year) and complete the Competitive State Anxiety Inventory-2 (CSAI-2). The CSAI-2 was a self-reported inventory with 27 simple questions. It took about 5 min to complete each evaluation and was used to measure the performance anxiety state. It showed the anxiety level of the three different dimensions (subscales), namely, somatic anxiety (SA), cognitive anxiety (CA), and self-confidence (SC). The subscale scores of each dimension ranged from 9 to 36. According to data collected from the CSAI-2, the IZOF in SA, CA, and SC dimensions was identified separately for each pianist. Statistically, IZOF is shown as M ± 1/2 SD. M is the mean of CSAI-2 subscales corresponding to the personal best piano performance, and SD is the standard deviation of CSAI-2 subscales. The difference (D) between the mean of in-IZOF and out-of-IZOF was calculated as performance scores and the percentage of the CSAI-2 subscale when the participant was below, in, or above the IZOF zone. Furthermore, the relative efficacy of the method for determining anxiety was compared by the percentage of outstanding or less-than-outstanding performance on the IZOF (i.e., percentage of outstanding performances inside the IZOF and less-than-outstanding outside the IZOF). We identified the outstanding performance as mean plus one SD of the 30 pianists with four performance scores. Outstanding performances in the first phase of the study were set at 92 (performance score mean: 88.98, SD: 3.47). Performance evaluations were made by seven piano teachers of judges as mentioned earlier.
The Secondary Phase of This Study: Performance Prediction
In the second phase, we evaluated the predictive accuracy of CSAI-2. The IZOF zone was identified again. Predictions can be projected for each subject based on their answers to the CSAI-2 before an upcoming jury. The IZOF theory was used for performance prediction and analysis. Subjects answered the CSAI-2 on the day before their final jury of the semester. Anxiety intensity from three subscales was compared with the upper and lower thresholds of corresponding zones to see if the subjects’ performances fell within their zones. After the final jury, performance evaluations were made and collected by the same group of judges using the same method. Data were collected to examine the hypothesis that the IZOF model can help to predict the upcoming performance and be fully applied within the piano performance anxiety description, explanation, assessment, and performance prediction.
Statistical Analysis
Continuous data were presented as mean, SD, minima, and maxima. Categorical data were presented as count and percentage. In the first phase of this study, we conducted the IZOF for each pianist and calculated the performance score difference between in-IZOF and out-of-IZOF. Furthermore, we calculated the percentage of outstanding or less-than-outstanding performance on the IZOF. In the second phase of this study, a correlation between how many pianists were in or out of their IZOF and performance was analyzed. We calculated the predictive in-zone performances and the statistical description of the actual performance score. Scatterplots were drawn to show the distance between pre-performance CSAI-2, their IZOF, and the jury’s judged performance score. The distance from the closest zone border and performance score was conducted, and the distance was set to 0 if a value falls within the zone. We used the Spearman correlation coefficient to show the correlation between the distance and the performance score because these data did not show normal distribution. A two-sided p-value of <0.05 was regarded as statistically significant. Data management and statistical analyses were conducted using SAS version 9.4 software (SAS Institute, Inc.).
Results
Table 1 summarizes the overall descriptive statistics for CSAI-2 subscales. The average CA score of 30 students corresponding to the best performances is 18.0 ± 4.9 (minimum-maximum: 11–34), the average SA score is 17.3 ± 4.8 (minimum-maximum: 11–26), and the average SC score is 20.9 ± 5.5 (minimum-maximum: 11–31). Students’ states of CA, SA, and SC are different according to personal best performance. The individual IZOF by M ± 1/2 SD is shown in Supplementary Table 1.
Table 1. Overall descriptive statistics for CSAI-2 Subscales.
The D (mean of in-IZOF–mean of out-of-IZOF) ranks from large to small showed that the differences in SA and SC have a similar trend (Supplementary Table 1). The different levels of performance scores in CA, SA, and SC range from 3.3 to 11. The average D in CA, SA, and SC are 6.2 ± 1.9, 6.2 ± 2.0, and 6.1 ± 2.1, respectively. For example, if we consider student #14, the different levels in all three subscales reach 11 and three performance scores are in the IZOF area (75%) with the best performance score of 91 ± 1 points.
Table 2 shows the average correct classification (in percentage) of outstanding performances inside IZOF and less-than-outstanding performances outside IZOF with the CSAI-2 questionnaires. Outstanding performances were set at a score of 92 points. The IZOF in CA, SA, and SC results in an average of 84.2, 80.8, and 77.5%, respectively, correct predictions (all range of the three subscales: 50–100%).
Table 2. Average correct classification (in percentage) of outstanding performances* inside IZOF and less-than outstanding performances outside IZOF with the CSAI-2 questionnaires.
Two figures were presented to display two contrasting cases of personal IZOF. Figure 2 shows the performance distribution in the three subscales of student #14. It shows that if the student has a high CA (IZOF: 24.0–28.0) and SA (IZOF: 23.4–26.6) and a low SC (IZOF: 13.9–16.1), he or she would perform well. Figure 3 shows that if student #29 has a low CA (IZOF: 18.0–20.0) and SA (IZOF: 15.6–16.4) and a high SC (IZOF: 20.8–25.2), he or she would have a less-than-ideal performance.
Figure 2. The best performance scores and CSAI-2 subscales for participant #14. The green band represents the IZOF of participant #14. The red and blue horizontal lines represent the lowest and highest scores of the best performance scores. The blue spots indicate the performance scores.
Figure 3. The best performance scores and CSAI-2 subscales inside or outside the IZOF for participant #29. The green band represents the IZOF of participant #29. The red and blue horizontal lines represent the lowest and highest scores of the best performance scores. The blue spots indicate the performance scores.
In the second phase study, the 30 participants conducted the new IZOF. Table 3 shows the prediction in-zone performances and the statistical description of the actual performance score. A total of 14 (46.7%) students have all three subscales inside the IZOF, and the average performance score is 93.4 ± 1.5 (minimum-maximum: 91–96). A total of 60% of the participants have at least two subscales inside the IZOF and also receive performance scores ≥90 out of 100. In total, 10 (33.3%) students have none of the subscales inside the IZOF, and the average performance score is 86.2 ± 1.4 (minimum-maximum: 84–88). Totally, 18 (60%) and 16 (53.3%) students have CA and SA scores that fall above or in the IZOF, respectively, while 12 (40%) students have SC scores that fell below the IZOF.
Table 3. Predictive in-zone performances and the statistic description of actual performance score.
Figure 4 shows the correlation between distance from closest zone border and performance score. The distance of CA, SA, and IZOF has significant strong negative correlation with the pianist’s performance score (CA: ρ = −0.79, p < 0.001; SA: ρ = −0.86, p < 0.001). The distance of SC and IZOF has a moderate negative correlation with the pianist’s performance score (CA: ρ = −0.55, p = 0.002).
Figure 4. The scatterplot of distance from closest zone border and performance score.
Discussion
This is the first study to apply IZOFs to musicians using CSAI-2 subscales. The results have verified the individual nature of each pianist relative to each subscale and demonstrated the zone’s efficiency in describing the relationship between MPA and optimal performance. The IZOF theory can be used for pre-performance anxiety analysis and performance prediction.
The IZOF zone was found for two cases in our previous pilot study. All of their best performances were presented in the IZOFs, and their average in-zone performance score was significantly better than the average out-zone score (Yao, 2016). This study further revealed the regulation and relationship between an individual’s anxiety intensity and their piano performance results. Everyone has different optimal levels of anxiety intensity. Therefore, applying the IZOF theory to music performance offers a new perspective on managing performance anxiety. With the help of the IZOF model, the study can define the “zone” in a quantified and measurable way. Moreover, with more than four sets of CSAI-2 data provided by pianists, the IZOF model may well be applied to predict pianists’ upcoming performances more precisely.
Music performance anxiety has been observed from different perspectives and studied with countless methods for many years, and researchers will continue studying this area with the help of cognitive and psychological science as it is developed. However, no matter how deeply this area has been studied, individual differences in reaction to performance anxiety issues cannot be denied, especially those of advanced music majors in colleges. Music interpretation is based on technique but is an emotion-supported performance activity. It involves a great deal of personal and emotional investment, which increases uncertainty and contributes to anxiety. Individual reactions to MPA vary widely among college-level pianists. In China, students enrolled in music conservatories have already achieved an advanced level of proficiency in piano performance. However, not all of them are aware of their optimal zone for performance and attempt to master every public performance consistently with applied consciousness. As a result, even after years of training, only a few piano majors end up with a career in professional performance. With the application of the IZOF model, young pianists may become aware of certain dimensions that impact their performances in addition to technical skills and finger abilities. Therefore, knowing that the IZOF may help to enhance performance and improve personal satisfaction maybe even more important than deciding whether one should continue a performance career despite their MPA issues.
One participant who has a performance score greater than 90 has only one subscale inside the IZOF (Table 3, Combination 5). The participant’s CA score is 19 and SC score is 28, both are very close to the lower thresholds of the optimal CA zone (19.68) and the lower thresholds of the optimal SC zone (28.11). This contradictory result may be eliminated by improving the accuracy of identifying the IZOF zone. Increasing the measured frequency of conducting IZOF or using prospective studies instead of recalling may help with improvement.
Limitations
This study has several limitations. First, although scholars in sports psychology have called for testing the IZOF model in more performance-related domains (Spielberger, 2013), few studies of MPA have adopted it as an applicable theory. Therefore, only limited resources can be found for comparison. Second, MPA might not be the only component affecting piano performance. The effects of other factors such as self-efficacy or social support may be underestimated and need to be considered. Third, fewer piano juries and participants may result in biased analyses and restrictions associated with future performance prediction. Piano juries are typically held four times per year, far less frequently than sports performances. Fourth, subjects were told to reflect on their most impressive performance to fulfill the retrospective recollection, which may increase the difficulty of defining the precise zone based upon fewer recollections and may result in inherent biases. Fifth, the lack of long-term data for tracking may decrease the accuracy of defining the IZOFs. Finally, since the scores did not show fluctuation for well-trained advanced performing musicians, the differences in performance scores for each person were very subtle (e.g., the lowest score was 84 and the highest was 95 on a scale of 0–100), which may affect the accuracy of prediction.
Conclusion
Personal IZOF zones were identified for each of the 30 pianists. Notably, 60% of the participants had at least two subscales within the IZOF and also received performance scores ≥90 out of 100. The IZOF model provides information for both piano teachers and pianists to help review their anxiety intensity and predict their performance scores to some extent.
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Author Contributions
ZY: guarantor of integrity of the entire study, study concepts, study design, definition of intellectual content, literature research, data analysis, statistical analysis, manuscript preparation, manuscript editing, and manuscript review. YL: literature research, clinical studies, experimental studies, and data acquisition. Both authors contributed to the article and approved the submitted version.
Funding
This research project was supported by the Science Foundation of Beijing Language and Culture University (supported by “Fundamental Research Funds for the Central Universities”) (Approval number: 19YBB25).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.764147/full#supplementary-material
References
Alzugaray, F. J. Z., Lopez, O. C., and Hernandez, S. O. (2016). Music performance anxiety and related psychological constructs. Students of five Spanish music conservatories. Revista Int. de Edu. Music. 4, 13–24. doi: 10.12967/RIEM-2016-4-p013-024
Bannai, K., Kase, T., Endo, S., and Oishi, K. (2016). Relationships among performance anxiety, agari experience, and depressive tendencies in Japanese music students. Med. Probl. Perform. Art. 31, 205–210. doi: 10.21091/mppa.2016.4037
Barbar, A. E., de Souza Crippa, J. A., and de Lima Osório, F. (2014). Performance anxiety in Brazilian musicians: prevalence and association with psychopathology indicators. J. Affect. Disord. 15, 381–386. doi: 10.1016/j.jad.2013.09.041
Bodner, E., and Bensimon, M. (2008). After the curtain falls: On the post-performance adjustment of solo singers. Med. Probl. Perform. Art. 23, 172–177. doi: 10.21091/mppa.2008.4035
Brodsky, W. (1996). Music performance anxiety reconceptualized: A critique of current research practices and findings. Med. Probl. Perform. Art. 11, 88–98.
Brugués, A. O. (2009). Music performance anxiety: A review of the literature (Doctoral dissertation). Breisgau: University of Freiburg.
Brugués, A. O. (2011a). Music performance anxiety–part 1. A review of its epidemiology. Med. Probl. Perform. Art. 26, 102–105. doi: 10.21091/mppa.2011.2015
Brugués, A. O. (2011b). Music performance anxiety-part 2. a review of treatment options. Med. Probl. Perform. Art. 26, 164–171.
Burin, A. B., Barbar, A. E. M., Nirenberg, I. S., and Osório, F. L. (2019). Music performance anxiety: perceived causes, coping strategies and clinical profiles of Brazilian musicians. Trends Psychiatry Psychother. 41, 348–357. doi: 10.1590/2237-6089-2018-0104
Burton, D., and Naylor, S. (1997). Is anxiety really facilitative? reaction to the myth that cognitive anxiety always impairs sport performance. J. Appl. Sport Psychol. 9, 295–302. doi: 10.1080/10413209708406488
Casanova, O., Zarza, F. J., and Orejudo, S. (2018). Differences in performance anxiety levels among advanced conservatory students in Spain, according to type of instrument and academic year of enrolment. Music Edu. Res. 20, 377–389. doi: 10.1080/14613808.2018.1433145
Chamberlain, S. T., and Hale, B. D. (2007). Competitive state anxiety and self-confidence: intensity and direction as relative predictors of performance on a golf putting task. Anx. Stress Coping 20, 197–207. doi: 10.1080/10615800701288572
Clarke, L. K., Osborne, M. S., and Baranoff, J. A. (2020). Examining a group acceptance and commitment therapy intervention for music performance anxiety in student vocalists. Front. Psychol. 11:1127. doi: 10.3389/fpsyg.2020.01127
Cohen, S., and Bodner, E. (2018). Music performance skills: A two-pronged approach – facilitating optimal music performance and reducing music performance anxiety. Psychol. Music 2018:534.
Cooper, J. J., Johnson, M., Radcliffe, J., and Fisher, J. (2021). Optimal emotional profiles for peak performance in strength and conditioning. J. Strength Cond. Res. 35, 833–840. doi: 10.1519/jsc.0000000000002832
Davison, D. (2020). Sources of occupational stress among the military musicians of the Royal Air Force. BMJ Mil. Health 2020:1432. doi: 10.1136/bmjmilitary-2020-001432
Fernholz, I., Mumm, J. L. M., Plag, J., Noeres, K., Rotter, G., Willich, S. N., et al. (2019). Performance anxiety in professional musicians: a systematic review on prevalence, risk factors and clinical treatment effects. Psychol. Med. 49, 2287–2306. doi: 10.1017/s0033291719001910
Finch, K., and Moscovitch, D. A. (2016). Imagery-based interventions for music performance anxiety: an integrative review. Med. Probl. Perform. Art. 31, 222–231. doi: 10.21091/mppa.2016.4040
Fishbein, M., Middlestadt, S. E., Ottati, V., Straus, S., and Ellis, A. (1988). Medical problems among ICSOM musicians: overview of a national survey. Rochester, NY: Institute for Music Leadership
Guyon, A., Cannavò, R., Studer, R. K., Hildebrandt, H., Danuser, B., Vlemincx, E., et al. (2020a). Respiratory variability, sighing, anxiety, and breathing symptoms in low- and high-anxious music students before and after performing. Front. Psychol. 11:303. doi: 10.3389/fpsyg.2020.00303
Guyon, A., Studer, R. K., Hildebrandt, H., Horsch, A., Nater, U. M., and Gomez, P. (2020b). Music performance anxiety from the challenge and threat perspective: psychophysiological and performance outcomes. BMC Psychol. 8:87. doi: 10.1186/s40359-020-00448-8
Hanin, Y. L. (2000). “Individual zone of optimal functioning (IZOF) model: Emotion-performance relationships in sport,” in Emotions in Sport, ed. Y. L. Hanin (Champaign, IL: Human Kinetics Publisher), 65–89.
Hanin, Y. L. (2010). “Coping with anxiety in sport,” in Coping in Sport: Theory, Methods, and Related constructs, ed. A. Nicholls (Hauppauge, NY: Nova Science Publishers), 159–175.
Harmison, R. J. (2006). Peak performance in sport: Identifying ideal performance states and developing athletes’ psychological skills. Prof. Psychol. Res. Prac. 37, 233–243. doi: 10.1037/0735-7028.37.3.233
Juncos, D. G., Heinrichs, G. A., Towle, P., Duffy, K., Grand, S. M., Morgan, M. C., et al. (2017). Acceptance and commitment therapy for the treatment of music performance anxiety: a pilot study with student vocalists. Front. Psychol. 8:986. doi: 10.3389/fpsyg.2017.00986
Kendrick, M. J., Craig, K. D., Lawson, D. M., and Davidson, P. O. (1982). Cognitive and behavioral therapy for musical-performance anxiety. J. Consult. Clin. Psychol. 50, 353–362. doi: 10.1037//0022-006x.50.3.353
Kenny, D. T. (2011). The psychology of music performance anxiety. New York: Oxford University Press.
Kenny, D. T., and Osborne, M. S. (2006). Music performance anxiety: New insights from young musicians. Adv. Cogn. Psychol. 2:495. doi: 10.2478/v10053-008-0049-5
Kenny, D. T., Arthey, S., and Abbass, A. (2016). Identifying attachment ruptures underlying severe music performance anxiety in a professional musician undertaking an assessment and trial therapy of Intensive Short-Term Dynamic Psychotherapy (ISTDP). Springerplus 5:1591. doi: 10.1186/s40064-016-3268-0
Khalsa, S. B., Butzer, B., Shorter, S. M., Reinhardt, K. M., and Cope, S. (2013). Yoga reduces performance anxiety in adolescent musicians. Altern. Ther. Health. Med. 19, 34–45.
Kim, Y. (2005). Combined treatment of improvisation and desensitization to alleviate music performance anxiety in female college pianists: a pilot study. Med. Probl. Perform. Art. 20, 17–24. doi: 10.21091/mppa.2005.1004
Kim, Y. (2008). The effect of improvisation-assisted desensitization, and music-assisted progressive muscle relaxation and imagery on reducing pianists’ music performance anxiety. J. Music Ther. 45, 165–191. doi: 10.1093/jmt/45.2.165
Lee, S. H. (1988). Functional performance anxiety modifications in adult pianists by Barbara Ann McCune. Bull. Coun. Res. Music Edu. 2, 93–98.
Lehrer, P. M., Goldman, N. S., and Strommen, E. F. (1990). A principal components assessment of performance anxiety among musicians. Med. Probl. Perform. Art. 5, 12–18.
McGinnis, A. M., and Milling, L. S. (2005). Psychological treatment of musical performance anxiety: Current status and future directions. Psychother. Theory Res. Pract. Train. 42, 357–373. doi: 10.1037/0033-3204.42.3.357
Mor, S., Day, H. I., Flett, G. L., and Hewitt, P. L. (1995). Perfectionism, control, and components of performance anxiety in professional artists. Cogn. Ther. Res. 19, 207–225. doi: 10.1007/bf02229695
Morano, M., Bortoli, L., Ruiz, M. C., and Robazza, C. (2020). Psychobiosocial states as mediators of the effects of basic psychological need satisfaction on burnout symptoms in youth sport. Int. J. Environ. Res. Public Health 17:447. doi: 10.3390/ijerph17124447
Nideffer, R. M., and Hessler, N. D. (1978). Controlling performance anxiety. Coll. Music Symp. 18, 146–153.
Nusseck, M., Zander, M., and Spahn, C. (2015). Music performance anxiety in young musicians: comparison of playing classical or popular music. Med. Probl. Perform. Art. 30, 30–37. doi: 10.21091/mppa.2015.1005
Orejudo Hernández, S., Zarza-Alzugaray, F. J., and Casanova, O. (2018). Music performance anxiety. Substance use and career abandonment in Spanish music students. Int. J. Music Edu. 36, 460–472. doi: 10.1177/0255761418763903
Orejudo, S., Zarza-Alzugaray, F. J., Casanova, O., and McPherson, G. E. (2021). Social support as a facilitator of musical self-efficacy. Front. Psychol. 12:722082. doi: 10.3389/fpsyg.2021.722082
Osborne, M. S., and Kenny, D. T. (2005). Development and validation of a music performance anxiety inventory for gifted adolescent musicians. J. Anx. Disord. 19, 725–751. doi: 10.1016/j.janxdis.2004.09.002
Robazza, C., and Bortoli, L. (2005). Changing students’ attitudes towards risky motor tasks: an application of the IZOF model. J. Sports Sci. 23, 1075–1088. doi: 10.1080/02640410500128205
Robazza, C., Bertollo, M., Filho, E., Hanin, Y., and Bortoli, L. (2016). Perceived control and hedonic tone dynamics during performance in elite shooters. Res. Q Exerc. Sport 87, 284–294. doi: 10.1080/02701367.2016.1185081
Robazza, C., Izzicupo, P., D’Amico, M. A., Ghinassi, B., Crippa, M. C., Di Cecco, V., et al. (2018). Psychophysiological responses of junior orienteers under competitive pressure. PLoS One 13:e0196273. doi: 10.1371/journal.pone.0196273
Robertson, D., and Eisensmith, K. (2010). Teaching students about performance anxiety: The scratch pad pop-up model. Music Edu. J. 97, 31–35. doi: 10.1177/0027432109335078
Rocha, S. F., Marocolo, M., Corrêa, E. N., Morato, G. S., and da Mota, G. R. (2014). Physical activity helps to control music performance anxiety. Med. Probl. Perform. Art. 29, 111–112. doi: 10.21091/mppa.2014.2022
Ruiz, M. C., Haapanen, S., Tolvanen, A., Robazza, C., and Duda, J. L. (2017). Predicting athletes’ functional and dysfunctional emotions: The role of the motivational climate and motivation regulations. J. Sports Sci. 35, 1598–1606. doi: 10.1080/02640414.2016.1225975
Ruiz, M. C., Robazza, C., Tolvanen, A., Haapanen, S., and Duda, J. L. (2019). Coach-created motivational climate and athletes’ adaptation to psychological stress: temporal motivation-emotion interplay. Front. Psychol. 10:617. doi: 10.3389/fpsyg.2019.00617
Sabino, A. D. V., Chagas, M. H. N., and Osório, F. L. (2020). Acute effects of oxytocin in music performance anxiety: a crossover, randomized, placebo-controlled trial. Psychopharmacology 237, 1757–1767. doi: 10.1007/s00213-020-05493-0
Shaw, T. A., Juncos, D. G., and Winter, D. (2020). Piloting a new model for treating music performance anxiety: training a singing teacher to use acceptance and commitment coaching with a student. Front. Psychol. 11:882. doi: 10.3389/fpsyg.2020.00882
Sousa, C. M., Machado, J. P., Greten, H. J., and Coimbra, D. (2016). Occupational diseases of professional orchestra musicians from northern portugal: a descriptive study. Med. Probl. Perform. Art. 31, 8–12. doi: 10.21091/mppa.2016.1002
Spahn, C. (2015). Treatment and prevention of music performance anxiety. Prog. Brain Res. 217, 129–140. doi: 10.1016/bs.pbr.2014.11.024
Steptoe, A. (2001). “Negative emotions in music making: The problem of performance anxiety,” in Music and Emotion: Theory and Research. Series in Affective Science, eds P. N. Juslin and J. A. Sloboda (New York, NY: Oxford University Press), 291–307.
Studer, R. K., Danuser, B., Wild, P., Hildebrandt, H., and Gomez, P. (2014). Psychophysiological activation during preparation, performance, and recovery in high- and low-anxious music students. Appl. Psychophysiol. Biofeedback 39, 45–57. doi: 10.1007/s10484-014-9240-2
Studer, R., Gomez, P., Hildebrandt, H., Arial, M., and Danuser, B. (2011). Stage fright: its experience as a problem and coping with it. Int. Arch. Occup. Environ. Health 84, 761–771. doi: 10.1007/s00420-010-0608-1
Su, Y. H., Luh, J. J., Chen, H. I., Lin, C. C., Liao, M. J., and Chen, H. S. (2010). Effects of using relaxation breathing training to reduce music performance anxiety in 3rd to 6th graders. Med. Probl. Perform. Art. 25, 82–86. doi: 10.21091/mppa.2010.2016
Tang, Y., and Ryan, L. (2020). Music performance anxiety: can expressive writing intervention help? Front. Psychol. 11:1334. doi: 10.3389/fpsyg.2020.01334
Topoğlu, O. (2014). Musical performance anxiety: relations between personal features and state anxiety levels of pre-service music teachers. Int. Online J. Edu. Sci. 6, 337–348.
van Fenema, E. M., Gal, P., van de Griend, M. V., Jacobs, G. E., and Cohen, A. F. (2017). A pilot study evaluating the physiological parameters of performance-induced stress in undergraduate music students. Digit. Biomark. 1, 118–125. doi: 10.1159/000485469
Wolfe, M. L. (1989). Correlates of adaptive and maladaptive musical performance anxiety. Med. Probl. Perform. Art. 4, 49–56.
Yao, Z. (2016). Anxiety and optimal piano performance: a pilot study on the application of the individual zone of optimal functioning (IZOF) Model. Int. J. Psychol. Stud. 8:60. doi: 10.5539/ijps.v8n4p60
Yoshie, M., Kudo, K., Murakoshi, T., and Ohtsuki, T. (2009). Music performance anxiety in skilled pianists: effects of social-evaluative performance situation on subjective, autonomic, and electromyographic reactions. Exp. Brain Res. 199, 117–126. doi: 10.1007/s00221-009-1979-y
Zarza-Alzugaray, F. J., Casanova, O., McPherson, G. E., and Orejudo, S. (2020). Music self-efficacy for performance: an explanatory model based on social support. Front. Psychol. 11:1249. doi: 10.3389/fpsyg.2020.01249
Zarza-Alzugaray, F. J., Orejudo, S., Casanova, O., and Aparicio-Moreno, L. (2018). Music Performance Anxiety in adolescence and early adulthood: Its relation with the age of onset in musical training. Psychol. Music 46, 18–32. doi: 10.1177/0305735617691592
Keywords: music performance anxiety, IZOF model, optimal performance, performance predicting, cognitive state anxiety (CA)
Citation: Yao Z and Li Y (2022) Preliminary Assessment of Individual Zone of Optimal Functioning Model Applied to Music Performance Anxiety in College Piano Majors. Front. Psychol. 13:764147. doi: 10.3389/fpsyg.2022.764147
Received: 29 September 2021; Accepted: 22 February 2022;
Published: 07 April 2022.
Edited by:
Andrea Schiavio, University of Graz, AustriaReviewed by:
Anja-Xiaoxing Cui, University of British Columbia, CanadaCasanova, University of Zaragoza, Spain
Copyright © 2022 Yao and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zijin Yao, 20181051@blcu.edu.cn