Sage Journals: Discover world-class research

Abstract

The ability to detect errors in performance is an important skill for musicians of all types. Previous research has indicated that tonal working memory capacity may play a role in musical error detection. Here we further tested the role of memory and attention in the error detection performance of college musicians. Participants (N = 39) from a pool of undergraduate music majors completed a test of error detection, pitch imagery (Pitch Imagery Arrow Task), and attentional flexibility (Trail Making Test) as well as a background questionnaire exploring past musical experience and training. Performance on a multi-part error detection test was not significantly related to year in school, level of aural skills class, or years of private piano. Years of experience on a second instrument and pitch imagery performance were significant predictors of melodic error detection skill, which was not correlated to our measure of attentional flexibility. We related these results to previous research suggesting the additive role of secondary instrument experience and to previous research indicating experience, not age, is a contributing factor to musical performance skills.

Keywords

Aural skills error monitoring musicianship pitch intervals preservice teachers skill acquisition

The ability to monitor multiple streams of musical information is important for musicians (Gates, 2021; Schlegel, 2010). Researchers have long attested that musicianship, in its many forms, demands the utilization of one's attention and one's memory in tandem (e.g., Brand & Burnsed, 1981; Pembrook, 1986). In particular, “taking dictation” is the act of hearing, classifying, and transcribing streams of pitches and rhythms (e.g., Buonviri, 2019). Also, the ability to detect errors in playing, called error detection, is a fundamental skill required for the accurate monitoring and production of music (Sorenson, 2021). This skill requires focus of attention and abundant working memory capacity. Further, there is evidence of a positive relationship between the amount of musical training and measures of attention and memory (Nichols et al., 2018). In this study, we are interested in how musicians process what they hear as they work to respond to various musical inputs and make decisions in their playing. Understanding the cognitive processes involved in musicianship may offer training and education implications for amateur and professional musicians.

Error detection is defined as the ability to recognize performance errors when comparing an aural example with written music (Montemayor et al., 2023). This skill is important for all musicians, including conductors. Error detection skill is related to melodic dictation and to the task of sight singing (singing music upon first reading “at sight”; Larson, 1977). Error detection is a perceptual task that may be easier or simpler to perform than sight singing, a perceptual-motor task, but does not guarantee successful execution. However, in a study of middle school students, some low-scoring sight-singers still performed accurately in error detection (Killian, 1991). Among undergraduate music majors, error detection and melodic dictation were more highly correlated than error detection and sight singing (Larson, 1977). For this review of literature, our focus will be limited to the two perceptual tasks of melodic dictation and error detection.

The number of parts in the music also affects error detection performance. Specifically, a greater number of musical parts correlates to lower error detection scores (Byo, 1997; Larkin, 2018; Sheldon, 2004). This provides evidence that abilities in memory and attention are relied upon in the execution of this skill. However, repeated listening does not appear to significantly improve performance. In a study by Sheldon (2004), test scores decreased across additional hearings. To explain this counterintuitive finding, Sheldon suggested the necessity of an aural reference pitch contained in short-term memory. This reference is cognitively less accessible across multiple hearings, even if multiple hearings of complex music offers increased opportunities to hear and classify all of what is played. Thus the detection of errors is hindered by an increasing number of parts, which is not ameliorated by multiple attempts at listening.

Errors in tonal melodies may be more frequently detected by collegiate music students than in atonal melodies. Similarly, chromatic errors may be correctly detected more often than diatonic errors (Groulx, 2013). Thus the melodic and harmonic contexts in music are also important for understanding what is heard. For example, in multi-part error detection exercises, errors were more frequently detected in the soprano parts than in the bass parts (Nápoles et al., 2017). These results depict areas of common weakness that may require special attention in music training, and importantly to the role of directed attention in the execution of this skill.

Beyond compositional factors of the excerpts, certain characteristics of an individual's musical background may be correlated with more successful error detection. In particular, years of experience playing the piano has been shown to predict more accurate error detection (Nápoles et al., 2017), but factors such as adults’ age (Talbert, 2021), adolescent students’ experience level (Thornton, 2008), and college students’ percussion experience and beat perception (Nichols & Stambaugh, 2022) have not been found to significantly predict rhythmic error detection ability. Such findings suggest that the nature of playing and practicing piano allows for the development of broadly applicable musical skills.

There is a long-established positive relationship between years of music teaching experience and error detection (cf. DeCarbo, 1984). Recent research suggests musical experience is consequential; Stambaugh (2016) found that musicians were significantly better at identifying errors when the music was within their area of expertise. Those college students with better aural skills grades in coursework were more likely to identify errors outside of their expertise of band or choir. The simpler ability to identify intervals may be required to identify melodic errors (Stambaugh & Nichols, 2020) and among these studies there is a clear association between musical experience and successful error detection.

Familiarity with an excerpt may increase error detection test scores in adult amateurs (Talbert, 2021), and studying scores prior to executing error detection tests has been shown to improve scores (Montemayor et al., 2023). Moreover, practice and mastery in one type of error detection test may improve the other, as success in pitch and melodic error detection predicts successful rhythmic error detection (Nichols & Stambaugh, 2022). Other requirements of the test used, such as performing music while listening for errors, may negatively impact error detection (Nápoles et al., 2017).

Melodic Dictation

Several factors can impact the success of writing down what one hears, which we refer to as melodic dictation in Western music notation. These factors include one’s ability to identify intervals, working memory, cognitive skills, the complexity of the task, the procedure used, and how familiar the individual is with tonal and rhythmic patterns (Nichols & Springer, 2022). For example, singing the melody before writing it down can negatively impact performance by causing distractions, especially if the singing is not highly accurate (Buonviri, 2019). Similarly, using solfège patterns before dictation has been found to have a negative impact on melodic detection performance (Buonviri, 2015). Some students utilize physical techniques like fingerings or mental visualization to aid in recalling pitch and rhythm during melodic dictation (Buonviri, 2014). To improve students’ melodic detection skills, music educators should guide students to explore strategies that suit them (Buonviri, 2017). Therefore, training programs generally explore various methods to help students develop personalized strategies for mastering melodic dictation skills given that musicians vary in their abilities.

Cognitive Processes

Working memory, while linked to short-term memory, has been shown by previous studies to be a distinct psychological process. Short-term memory refers to the storage of recent information, whereas working memory concerns newly processed information that is being actively referenced and utilized (Kail & Hall, 2001). The integrity of memory can depend on the type of sensory information that is to be encoded. In the short term, visual memory may be more detailed and defined than auditory memory, but in the long term, both forms become less detailed and more gist-oriented (Gloede & Gregg, 2019). In children, music training has been associated with auditory and visual memory (Degé et al., 2011). Working memory is closely associated with attention. If information can be prioritized by one's attention while it is encoded into memory, it may be recalled more successfully at a later date (Sandry et al., 2020). This connection between working memory and attention may have implications for musicians. In one study, guiding participants’ attention to rhythm was correlated to better scores on two-part melodic dictation tests (Beckett, 1997). These results further illustrate the significance of attention when processing auditory stimuli.

Prior research has supported that if new information remains important to the task at hand, it can be brought back to the forefront of one's limited attention span (Zokaei et al., 2014). This is the basis of attentional flexibility, the ability to shift or transfer focus. Attentional flexibility has been reported to be potentially relevant to various musical applications. For example, there is evidence to support that conductors, on average, may have higher attentional flexibility than pianists (Wöllner & Halpern, 2015). However, research has not always supported a clear association between musical training and attentional flexibility (Jones et al., 1995). High attentional flexibility is potentially correlated with superior performance in certain exercises, including high-difficulty sight reading (Herrero & Carriedo, 2020). Further, focusing on one part in a multi-part score does improve accuracy in melodic dictation on that one specific part, but shows decreased accuracy in other parts (Paney, 2016). Overall, there is a possibility that attentional flexibility is important to musicianship.

Another important component of cognitive musical processing is auditory imagery, which refers to the ability to “hear” music within our minds and may be a beneficial strategy in musical performance. Self-report on vividness of auditory imagery predicts how well a person performs on auditory memory tasks as well as higher activity within the auditory cortex of the brain (Halpern, 2015). Vividness of imagery has also been correlated to training (Foster et al., 2013). Mental rehearsal (thinking through musical performance) can be a useful strategy in preparing for music-making, and two factors—vividness and mental rehearsal—have been associated with higher achieving mental performances (Clark & Williamon, 2011). Individuals with higher auditory imagery skills can maintain tonal accuracy even when given altered auditory feedback (Reed et al., 2024).

Given these previous results, we aimed to test the relationships among pitch imagery and attentional flexibility to measures of musicianship such as melodic dictation and error detection. Prior findings showed that various conditions can affect individuals’ performance in error detection (Nápoles et al., 2017; Nichols & Springer, 2022; Nichols & Stambaugh, 2022; Stambaugh & Nichols, 2020), but other factors such as the type of error also affect musicians’ ability to detect errors in music (Waggoner, 2011). Additionally, because there are significant distinctions between musicians and non-musicians on tonal working memory (Ding et al., 2018; Pallesen et al., 2010), error detection (Ding et al., 2018), and other musicianship skills (Ding et al., 2018), our aims were to determine the role of attention and whether individuals’ background can affect musician's error detection and interval identification abilities. Our two research questions were:
What is the relationship among pitch imagery, attentional flexibility, error detection, and melodic dictation?

What factors of musical experience and training can predict performance in error detection and melodic dictation?

Method

Participants and Procedures

We recruited participants from among collegiate music majors enrolled in three music courses at a large research university in the United States: two first-year music theory sections and one third-year music education course. First-year participants were generally in the second semester of music theory/aural skills coursework; third-year participants had generally completed this sequence of coursework. Similarly, first-year participants were generally enrolled in the second semester of a four-course piano class while third-year participants were in the fourth semester or had completed the coursework.

The participants (N = 39) represented music majors of all types including music education (n = 31), music performance (n = 4), music technology (n = 2), and double music degrees (n = 2). The mean age of these musicians was 19.38 (SD = 1.31), and they represented primary instruments including woodwind (n = 11), strings (n = 9), brass (n = 8), voice (n = 6), piano (n = 2), percussion (n = 2), or multiple instruments (n = 1). Participants indicated a mean of 6.25 years of experience taking private lessons on their primary instrument (SD = 3.52). Some participants indicated experience playing a secondary instrument (n = 26) of which voice was the most frequently reported (n = 6). Of those who had private lessons on a secondary instrument (n = 13), participants reported a mean of 2.06 years of secondary lessons (SD = 3.57). Some participants reported having taken piano lessons (n = 19), ranging from 1 to 14 years (M = 2.25, SD = 3.84).

Participants were recruited during the spring semester following institutional human subjects protocols, and they reported a range of 1–6 semesters of experience as a music major at the university. Participants completed consent procedures, and then began the study during a regular class meeting through a series of tasks led by a researcher and research assistants; participants were given assurance that participation nor performance would affect their standing in the class. The session began with a background questionnaire completed by all participants.

Melodic Dictation Test

Test Design

Our melodic dictation exercise consisted of stimuli from Mozart chamber music first employed by Paney (2016) and used again later by Nichols and Springer (2022). The excerpt is the beginning of the second movement (Andante grazioso, measures 1 through 8) of Mozart's Divertimento No. 9 in B-Flat Major, K. 240 as performed by St. Luke's Chamber Ensemble (Mozart, 1991). For this test, we provided participants with a blank paper with staff notation, the time signature, the key signature, and the first pitch and rhythm from the melody. The task was to notate one part, the melody, as accurately as possible. We played the excerpt four times with a pause of 30 s of silence after each hearing. Using procedures from Paney (2016), we calculated pitch scores and rhythm scores then created a composite test score by summing the two scores.

Scoring

Rhythm scores were counted out of a possible 32 points, with each half beat corresponding to one possible point. Points were given when the order of the dictated and original rhythms matched, regardless of whether they overlapped in real time. This procedure was chosen because a small mistake would otherwise completely offset the participant's attempt and call for no points after the error. Pitch scores were counted out of a possible 31 notes. If the dictated note matched what was played in the excerpt at that time, one point was given. A procedure similar to the rhythmic scoring method was used: If the researcher could understand which part of the excerpt the participant was dictating, credit was given. Two research assistants both scored approximately half the participants (n = 21) with a resulting high agreement rating for rhythm (α = .99) and for pitch (α = .99). Based on this, the remaining participants were scored by one assistant.

Error Detection Test

Test Design

We combined two approaches to assessing error detection skills. In the first set of items, we administered 12 of the original 20 three-part stimuli from a previous study showing visual scores of oboe, horn, and bassoon parts from the composer Frans Josef Haydn (Williams, 2022). We asked participants to identify errors by indicating their location on a score. The excerpts ranged from 7 to 11 measures and contained zero, one, or two errors. These items were played only once, preceded by a practice item at the beginning of the test. For the second set of items, we asked participants to mark both the error and type of error (e.g., “rushed,” “B-flat instead of B,” or “not dotted,” etc.). These items were more difficult in that they contained more errors, and participants were also asked to indicate the type of error in addition to the location of the error. We used multi-part exercises 13.1, 14.1, 15.1, and 16.1 from the textbook Developing Error Detection Skills in the Wind Band Educator (Foster & Miller, 2023). These stimuli were written on one, two, or four staves, some with one part each and some with two parts notated per staff. These items were also preceded by a practice item, and all items were eight measures in duration. These items were played twice due to the increased number of errors per item.

Scoring

We used an answer key made available by the original test author for the first set of items, and we used the answer key from the textbook for the second set of items. Two research assistants completed a training session and then independently scored separate sets of participants (n = 5 each). Subsequently, the assistants completed a second training session and then divided the remaining participants. The scoring method involved awarding one point for each correctly identified error and in the second set of items, an additional point was given for identifying how the error was incorrect. Incorrectly identified errors did not result in point deductions.

Attention and Imagery Tests

Pitch Imagery

We evaluated pitch imagery with the Pitch Imagery Arrow Task (PIAT, Gelding et al., 2015), using the online adaptive version (Gelding et al., 2021). Participants were played an initial sequence of tones that they were instructed to continue mentally (i.e., without vocalization) following visually presented cues (arrows that indicate movement up or down the diatonic scale). The online measure produced an item response theory score for the participants. Using this ability score, 0 corresponds to average ability, 1.0 corresponds to 1 standard deviation above average, 2.0 to 2 standard deviations above average, and so on. This brief automated measure was shown to be significantly correlated with the Bucknell Auditory Imagery Scale for self-reported Imagery (r = .49) and for self-reported Vividness (r = .59; Gelding et al., 2015).

Attentional Flexibility

To evaluate participants’ ability to attend to multiple streams of information, we administered the Trail Making Test “Part B” (Reitan & Wolfson, 1985). Visual tasks for attentional flexibility have been successfully used in music studies (e.g., Herrero & Carriedo, 2020), so we chose this brief pencil-and-paper test which was administered one-on-one with a research assistant. We asked participants to draw a line connecting alternating series (A to 1, B to 2, C to 3, and so on), and the score was the time it took until task completion. Consistent with common usage, when a participant made an error, the research assistant corrected them which added to the duration time thereby worsening the score (i.e., increasing the time to task completion). For the subsequent analyses, a lower score for attentional flexibility reflects a faster processing speed.

Procedure

The order of melodic dictation and error detection tasks varied by class; two classes completed melodic dictation followed by error detection and one class completed the error detection followed by melodic dictation. After these two tasks, participants completed the test of pitch imagery followed by the test of attentional flexibility.

Results

To explore the relationships between measures of pitch imagery, attentional flexibility, melodic dictation, and error detection, we calculated Pearson correlations among them (Table 1). We chose to report sub-scores of pitch and rhythm along with the composite score for melodic dictation because scoring methods for each of these varies across previous studies. We did the same for the error detection score. For the test of attentional flexibility in which lower scores are considered better, we multiplied the correlation results by −1 for consistency with the other measures. There was no correlation between performance in pitch imagery and error detection (p = .112). There was a positive association between pitch imagery and melodic dictation pitch sub-score (r = .327, p = .049) and melodic dictation total score (r = .326, p = .049). Error detection and melodic dictation pitch sub-score (r = .447, p = .004), melodic dictation rhythm sub-score (r = .328, p = .041) and melodic dictation total score were positively correlated (r = .454, p = .004). Attentional flexibility was not associated with pitch imagery nor with error detection or melodic dictation total scores or sub-scores.

Table 1.
Correlation matrix and descriptive statistics.

Variables 1. Melodic dictation—pitch 2. Melodic dictation—rhythm 3. Melodic dictation—total 4. Error detection 5. Attentional flexibility 6. Pitch imagery 7. Years lessons—primary instrument 8. Years lessons—piano

1. Melodic dictation—pitch — — — — — — — —

2. Melodic dictation—rhythm .490* — — — — — — —

3. Melodic dictation—total .887* .837* — — — — — —

4. Error detection .447* .328* .454* — — — — —

5. Attentional flexibility −.004 .009 .001 −.108 — — — —

6. Pitch imagery .327* .240 .326* .266 −.048 — — —

7. Years lessons—primary instrument .050 .210 .142 −.030 .265 −.144 — —

8. Years lessons—piano .178 .159 .389* .217 .096 .135 .199 —

M 15.59 23.51 39.10 22.64 69.73 1.45 6.25 2.25

SD 10.60 8.95 16.89 7.87 22.70 1.39 3.52 3.84

Note. Participants reported the number of years of private lessons on the primary instrument and on piano.

*
p < .05

Regarding the second research question concerning the role of background factors in predicting musical performance, years of lessons on the primary instrument or years of lessons on a secondary instrument were not significantly related to the melodic dictation or error detection scores (p > .05), though years of piano lessons were positively associated with the melodic dictation pitch sub-score (r = .474, p < .05) and total score (r = .389, p < .05). Next, we employed a regression model (using the “enter” method in SPSS) based on the variables above plus two selected background factors: years lessons on the primary instrument and years of piano lessons. We chose years of piano lessons based on its role predicting in previous research in musicianship tasks. Given the associations with melodic dictation and error detection, we chose the melodic dictation pitch sub-score as the dependent variable to represent musicianship. We used only the pitch score for melodic dictation using the rationale that pitch imagery should be most predictive of pitch-related musician tasks. Thus, we entered attentional flexibility (Trail Making Test), pitch imagery (Pitch Imagery Arrow Task), years of lessons on their primary instrument, and years of lessons on piano as predictors of melodic error detection.

After checking and confirming assumptions for normality, the Durbin–Watson statistic of 2.173 suggests the absence of autocorrelation. In addition, the analysis yielded variance inflation factors below 1.20 for multicollinearity. Further, we plotted standardized residuals against standardized predicted values to affirm the assumption of homoscedasticity. Correlations among these variables are reported with their means in Table 1. Results indicated the model was a significant predictor of the pitch sub-score for melodic error detection, F(4, 34) = 4.492, p = .005, R2 = .346, adj. R² = .269 (see Table 2). Of the variables entered, years of piano lessons (b = 1.304, p = .003) and pitch imagery (b = 1.304, 2.490, p = .035) were significant predictors.

Table 2.
Multiple regression predicting melodic dictation pitch sub-score.

Variable b SE β t p

(Constant) 6.600 4.436 1.488 .146

Attentional flexibility .030 .039 .117 .776 .443

Pitch imagery* 2.490 1.133 .326 2.197 .035

Years lessons—primary instrument .145 .427 .050 .339 .736

Years lessons—piano* 1.304 .415 .454 3.141 .003

Note. The table includes both unstandardized (b) and standardized (β) beta coefficients.

* p < .05

Discussion

We investigated the role of auditory imagery and attention in the performance of college musicians on two tasks relevant to musicians. Participants from a pool of undergraduate music majors completed previously-designed tests of melodic dictation and error detection, plus an online measure of pitch imagery (Pitch Imagery Arrow Task, Gelding et al., 2015) and a pencil-and-paper test of attentional flexibility (Trail Making Test—Part B, Reitan & Wolfson, 1985), as well as a background questionnaire documenting past musical experience and training. Given our interest in pitch-related tasks, we employed a model in which the melodic error detection sub-score (pitch score) was regressed on two significant predictors: years of piano lessons and the Pitch Imagery Arrow Task (PIAT). Two variables were not significant predictors: a background variable consisting of years of lessons on participants’ primary instrument and the measure of attentional flexibility.

Regarding Research Question 1 as to the relationship among pitch imagery, attentional flexibility, and measures of musicianship skills: Pitch imagery was not significantly related to error detection performance. Pitch imagery was significantly related to melodic dictation. The significant positive correlation between performance on error detection and melodic dictation tasks in previous research lends support to the conclusion that pitch imagery may be a requisite ability for pitch-related music perception tasks including both error detection and melodic dictation. However, we cannot conclude that attentional flexibility is a construct related to the performance of these tasks.

The pitch imagery task required musicians to maintain a tonal environment and to follow a sequence of pitches. At the end of the sequence, musicians were asked to imagine subsequent pitches and were given a probe tone after which participants indicated whether the probe matched the imagined pitch. Previous research indicates musicians who employed a musical strategy for this task performed higher than those using an alternative cognitive strategy (Gelding et al., 2015). Given the number of tasks in the current study design, we chose not to ask participants about their approach to tasks used in our study. Individuals may have varied in their familiarity or experience or training with imagining pitches, and it remains possible that different musician types, such as classical or jazz musicians, could respond differently to the task (Nichols et al., 2018).

Gelding et al. (2021) indicated pitch imagery was related to a measure of musical sophistication and to musical working memory. This is confirmed by other studies (e.g., Nichols & Barrett, 2025) which have indicated musical working memory to be related to error detection such as was used in the present study. Similarly, participants in a previous study of musicians who scored higher on the Bucknell Auditory Imagery Scale were able to indicate a reference pitch that was less susceptible to pitch shifts and speech distractions (Reed et al., 2024). In that production task and in the present perception task, pitch imagery serves the auditory processes in supporting musical tasks like hearing and monitoring pitches. These results suggest that training in these component tasks of aural imagery and memory may be used by musicians to potentially enhance their musical abilities in error detection or musical dictation.

To address the second question of what background factors can predict performance in measures of musicianship, we found that performance on a multi-part error detection test was not significantly related to year in school, level of aural skills class, or years of private piano lessons. Years of experience on a second instrument was a significant predictor of melodic error detection skill, which was significantly correlated with performance on the melodic dictation task. Previous research has also reported that years of lessons on a second instrument are associated with melodic dictation skills (Nichols & Barrett, 2025). The replication of those results may indicate that years and type of musical experience are important as part of the musician's background, particularly given previous research indicating jazz and classical musicians may have different working memory capacities in dictation (Nichols et al., 2018). The additional experience of playing a second instrument may evoke an additive effect by which multiple instrument experiences or playing settings yield additional skills. Or possibly, some individuals played two instruments simultaneously, meaning that years practicing as a musician is not an ideal indicator for one's playing experience; five years of experience in one individual could equate to twice as much experience in someone studying two instruments compared to another individual with five years of musical experience on a single instrument.

Error detection exercises can be used to quantify an individual's ability to determine discrepancies between a score of music and a performed representation of that score. Previous research has aimed to uncover factors that can predict participants’ success in detecting errors, including both the type and composition of the excerpt tested and the characteristics of the participants (e.g., Nichols & Barrett, 2025). Despite the evidence that conductors may have higher attentional flexibility than other musician types (Wöllner & Halpern, 2015), attentional flexibility was not related to error detection or to melodic dictation performance in the present study. Some research on attentional flexibility has not reported a relationship to musical training (Jones et al., 1995); however, high attentional flexibility has been related to superior performance in high-difficulty sightreading (Herrero & Carriedo, 2020), which itself has been correlated to error detection and melodic dictation performance (Larson, 1977). Herrero and Carriedo's participants were generally similar to the present participants in that they were musicians with a requisite minimum number of years of music playing: adolescents (at least six years of musical experience) and young adults (at least 12 years). Herrero and Carriedo (2020) used a different test for cognitive flexibility than we chose: The Flanker test in which participants switch tasks and the timing to complete the tasks is compared to doing a series of one task type only. It remains possible that our use of the Trail Making Test—Part B, in which participants were timed connecting letters and numbers in an alternating fashion, did not provide a type of attentional task related to musicianship. Musicians must generally attend to both pitch and rhythm in their playing; thus we expected attentional flexibility to possibly be related to tasks of monitoring musical auditory streams.

One limitation of the study is that our melodic dictation was a single-part melody excerpted from a classical piece, as is typically used in melodic dictation exercises. Future research related to the cognitive processes of attentional flexibility might explore the extent to which musicians are capable of classifying and writing down multi-part dictation, or melodic and harmonic dictations. Previous research indicates that focusing on one element in a multi-part exercise improves accuracy on that part but to the detriment of others (Paney, 2016). We are unclear to what extent current teaching methods address the multi-part processing of music in coursework. Attentional flexibility would seem to be important in considering multiple parts in a score—or even the two components of pitch and rhythm in one part—yet attentional flexibility was not related to the tasks in this study.

There is evidence musicians sometimes prioritize melody or rhythm in melodic dictation tasks. Beckett (1997) reported that music students notated rhythms more accurately when directed to rhythms first, but the same did not exist for attention directed to pitches first. They recommended music students’ attention could be directed to rhythm first given this finding. In another previous study, particularly unique melody or rhythm elements led to that element being prioritized (i.e., an interesting melody yielded prioritization to melody, and an interesting rhythm yielded prioritization to the rhythm component in Szalárdy et al., 2014). It remains possible our participants divided or alternated attention between pitch and rhythm elements in error detection and melodic dictation tasks in in different ways based on the stimuli or even in individually different ways. Given the positive association between performance on melodic dictation and performance in error detection here and between melodic dictation and interval identification in a previous study (Nichols & Springer, 2022), it is possible that musicians may benefit from considering rhythm and melody together rather than separately in tasks like those above.

The results of the present study offer several implications for the training of musicians. We suggest the possibility that melodic dictation or error detection performance may increase with experience. Nápoles et al. (2017) specifically recommended training in error detection exercises for music students. Indeed, training exercises exist for practice in error detection (e.g., Hondorp, 2015; Spradling, 2010). Researchers have scrutinized the effectiveness of particular strategies for melodic dictation (e.g., Buonviri, 2014; Paney, 2016). Melodic dictation is a regular component of music theory and aural skills coursework for college musicians. However, music training may not currently include regular training in error detection or pitch imagery training, as its inclusion was not identified in our search of the literature.

Given that pitch imagery is associated with musical training (Foster et al., 2013), future research might explore whether and to what extent pitch imagery increases with training exercises in melodic dictation or error detection, or whether musicianship capabilities increase with pitch imagery training. Performance on the Pitch Imagery Arrow Task is not known to be associated with age (Gelding et al., 2021) and beyond the factor of musical training, the strategies used by each participant may be the next most salient factor in the performance of pitch imagery tasks. For example, one high-performing participant in a previous study was a pianist who reported the use of a visual-motor strategy to be successful (Gelding et al., 2015). These and other strategies should be explored in future research.

Conclusion

Our overarching question was, “How do perceptual skills develop in musicians, and what cognitive processes support musician tasks such as those related to memory, attention, and imagery?” We related these results to previous research suggesting verbal working memory is correlated to rhythm tasks but not pitch tasks and to previous research indicating tonal working memory may represent a unique, separate skill from verbal working memory. A participant's year in school seems unrelated to performance on error detection tasks (Nichols & Stambaugh, 2022) and to pitch imagery (Gelding et al., 2021), though our current results suggest experience plays a role in these skills. We sought to understand how musicians manage multiple streams of information as they classify, interpret, and evaluate pitches and rhythms. This appears to depend on pitch imagery but may not be highly reliant on a high degree of attentional flexibility. Musicians vary in attentional flexibility and pitch imagery. They also vary in terms of musicianship as measured by melodic dictation and error detection—musical skills that continue to be emphasized in musician training programs. Musicians rely on the processes of monitoring and classifying musical stimuli, which remains important to their work as public performers and recording artists.

Variables	1. Melodic dictation—pitch	2. Melodic dictation—rhythm	3. Melodic dictation—total	4. Error detection	5. Attentional flexibility	6. Pitch imagery	7. Years lessons—primary instrument	8. Years lessons—piano
1. Melodic dictation—pitch	—	—	—	—	—	—	—	—
2. Melodic dictation—rhythm	.490*	—	—	—	—	—	—	—
3. Melodic dictation—total	.887*	.837*	—	—	—	—	—	—
4. Error detection	.447*	.328*	.454*	—	—	—	—	—
5. Attentional flexibility	−.004	.009	.001	−.108	—	—	—	—
6. Pitch imagery	.327*	.240	.326*	.266	−.048	—	—	—
7. Years lessons—primary instrument	.050	.210	.142	−.030	.265	−.144	—	—
8. Years lessons—piano	.178	.159	.389*	.217	.096	.135	.199	—
M	15.59	23.51	39.10	22.64	69.73	1.45	6.25	2.25
SD	10.60	8.95	16.89	7.87	22.70	1.39	3.52	3.84

Variable	b	SE	β	t	p
(Constant)	6.600	4.436		1.488	.146
Attentional flexibility	.030	.039	.117	.776	.443
Pitch imagery*	2.490	1.133	.326	2.197	.035
Years lessons—primary instrument	.145	.427	.050	.339	.736
Years lessons—piano*	1.304	.415	.454	3.141	.003

Footnotes

Action Editor

José Luis Aróstegui, University of Granada, Music Education Department

Peer Review

Mark Montemayor, University of North Texas, College of Music

One anonymous reviewer

Data Availability

The data will be made available at —the Open Science sharing network.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

The ethics committee of the Penn State University approved this study (Ref 00010764).

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Bryan Nichols

Pei-Hsuan Lu

Sydney Ortega

Michelle Bojalad

References

Beckett

C. A.

(1997). Directing student attention during two-part dictation. Journal of Research in Music Education, 45(4), 613–625. https://doi.org/10.2307/3345426

Brand

Burnsed

(1981). Music abilities and experiences as predictors of error-detection skill. Journal of Research in Music Education, 29(2), 91–96. https://doi.org/10.2307/3345017

Buonviri

N. O.

(2014). An exploration of undergraduate music majors’ melodic dictation strategies. Update: Applications of Research in Music Education, 33(1), 21–30. https://doi.org/10.1177/8755123314521036

Buonviri

N. O.

(2015). Effects of a preparatory singing pattern on melodic dictation success. Journal of Research in Music Education, 63(1), 102–113. https://doi.org/10.1177/002249415570754

Buonviri

N. O.

(2017). Effects of two listening strategies for melodic dictation. Journal of Research in Music Education, 65(3), 347–359. https://doi.org/10.1177/0022429417728925

Buonviri

N. O.

(2019). Effects of silence, sound, and singing on melodic dictation accuracy. Journal of Research in Music Education, 66(4), 365–374. https://doi.org/10.1177/0022429418801333

Byo

J. L.

(1997). The effects of texture and number of parts on the ability of music majors to detect performance errors. Journal of Research in Music Education, 45(1), 51–66. https://doi.org/10.2307/3345465

Clark

Williamon

(2011). Imagining the music: Methods for assessing musical imagery ability. Psychology of Music, 40(4), 471–493. https://doi.org/10.1177/0305735611401126

DeCarbo

(1984). The effect of years of teaching experience and major performance instrument on error detection scores of instrumental music teachers. Contributions to Music Education, 11, 28–32. http://www.jstor.org/stable/24127284

10.

Degé

Wehrum

Stark

Schwarzer

(2011). The influence of two years of school music training in secondary school on visual and auditory memory. European Journal of Developmental Psychology, 8(5), 608–623. https://doi.org/10.1080/17405629.2011.590668

11.

Ding

Gray

Forrence

Wang

Huang

(2018). A behavioral study on tonal working memory in musicians and non-musicians. PLoS ONE, 13(8). https://doi.org/10.1371/journal.pone.0201765

12.

Foster

Miller

(2023). Developing error detection skills in the wind band educator. GIA Publications.

13.

Foster

Halpern

Zatorre

(2013). Common parietal activation in musical mental transformations across pitch and time. NeuroImage, 75, 27–35. https://doi.org/10.1016/j.neuroimage.2013.02.044

14.

Gates

(2021). Developing musical imagery: Contributions from pedagogy and cognitive science. Music Theory Online, 27(2). https://doi.org/10.30535/mto.27.2.3

15.

Gelding

R. W.

Harrison

P. M. C.

Silas

Johnson

B. W.

Thompson

W. F.

Müllensiefen

(2021). An efficient and adaptive test of auditory mental imagery. Psychological Research, 85, 1201–1220. https://doi.org/10.1007/s00426-020-01322-3

16.

Gelding

R. W.

Thompson

W. F.

Johnson

B. W.

(2015). The pitch imagery arrow task: Effects of musical training, vividness, and mental control. PLoS ONE, 10(3), e0121809. https://doi.org/10.1371/journal.pone.0121809

17.

Gloede

M. E.

Gregg

M. K.

(2019). The fidelity of visual and auditory memory. Psychonomic Bulletin & Review, 26(4), 1325–1332. https://doi.org/10.3758/s13423-019-01597-7

18.

Groulx

(2013). The influence of tonal and atonal contexts on error detection accuracy. Journal of Research in Music Education, 61(2), 233–243. https://doi.org/10.1177/0022429413485602

19.

Halpern

(2015). Differences in auditory imagery self-report predict neural and behavioral outcomes. Psychomusicology: Music, Mind and Brain, 25(1), 37–47. https://doi.org/10.1037/pmu0000081

20.

Herrero

Carriedo

(2020). The role of cognitive flexibility and inhibition in complex dynamic tasks: The case of sight reading music. Current Psychology, 41(7), 4625–4637. https://doi.org/10.1007/s12144-020-00983-y

21.

Hondorp

(2015). Choral error detection: Exercises for developing musicianship. Carl Fischer.

22.

Jones

M. R.

Jagacinski

R. J.

Yee

Floyd

R. L.

Klapp

S. T.

(1995). Tests of attentional flexibility in listening to polyrhythmic patterns. Journal of Experimental Psychology: Human Perception and Performance, 21(2), 293–307. https://doi.org/10.1037/0096-1523.21.2.293

23.

Kail

Hall

L. K.

(2001). Distinguishing short-term memory from working memory. Memory & Cognition, 29(1), 1–9. https://doi.org/10.3758/BF03195735

24.

Killian

(1991). The relationship between sightsinging accuracy and error detection in junior high singers. Journal of Research in Music Education, 39(3), 216–224. https://doi.org/10.2307/3344721

25.

Larkin

(2018). The effect of selective attention on error-detection abilities. (Publication No. 10828525). [Doctoral dissertation, University of Washington]. ProQuest Dissertations and Theses Global.

26.

Larson

R. C.

(1977). Relationships between melodic error detection, melodic dictation, and melodic sightsinging. Journal of Research in Music Education, 25(4), 264–271. https://doi.org/10.2307/3345267

27.

Montemayor

Nápoles

Silvey

B. A.

Wiese

(2023). Effects of score study and conducting gesture on collegiate musicians’ ability to detect errors in a choral score. Journal of Research in Music Education, 70(4), 449–462. https://doi.org/10.1177/00224294221090432

28.

Mozart

W. A.

(1991). Divertimento no. 9 in B-flat major, K. 240 [Recorded by St. Luke’s Chamber Ensemble]. On Wolfgang Amadeus Mozart: Divertimenti. Musical Heritage Society. (Original work published 1776).

29.

Nápoles

Babb

Bowers

Hankle

Zrust

(2017). The effect of piano playing on preservice teachers’ ability to detect errors in a choral score. Journal of Music Teacher Education, 26(2), 149–162. https://doi.org/10.1177/1057083716639724

30.

Nichols

Wöllner

Halpern

(2018). Score one for jazz: Working memory in jazz and classical musicians. Psychomusicology: Music, Mind, and Brain, 28(2), 101–107. https://doi.org/10.1037/pmu0000211

31.

Nichols

B. E.

Barrett

(2025). The effect of memory and presentation mode in melodic error detection. Journal of Research in Music Education, 72(4), 376–394. https://doi.org/10.1177/00224294231225408

32.

Nichols

B. E.

Springer

D. G.

(2022). Interval identification predicts success in melodic dictation. Journal of Research in Music Education, 70(1), 109–126. https://doi.org/10.1177/00224294211011962

33.

Nichols

B. E.

Stambaugh

(2022). Melody, not beat perception, predicts rhythmic error detection. Journal of Research in Music Education, 70(2), 190–205. https://doi.org/10.1177/00224294211032160

34.

Pallesen

K. J.

Brattico

Bailey

C. J.

Korvenoja

Koivisto

Gjedde

Carlson

(2010). Cognitive control in auditory working memory is enhanced in musicians. PLoS ONE, 5(6), e11120. https://doi.org/10.1371/journal.pone.0011120

35.

Paney

A. S.

(2016). The effect of directing attention on melodic dictation testing. Psychology of Music, 44(1), 15–24. https://doi.org/10.1177/0305735614547409

36.

Pembrook

R. G.

(1986). Interference of the transcription process and other selected variables on perception and memory during melodic dictation. Journal of Research in Music Education, 34(4), 238–261. https://doi.org/10.2307/3345259

37.

Reed

C. N.

Pearce

McPherson

(2024). Auditory imagery ability influences accuracy when singing with altered auditory feedback. Musicae Scientiae, 28(3), 478–501. https://doi.org/10.1177/10298649231223077

38.

Reitan

R. M.

Wolfson

(1985). The Halstead–Reitan neuropsychological test battery. Neuropsychological Press.

39.

Sandry

Zuppichini

M. D.

Ricker

T. J.

(2020). Attentional flexibility and prioritization improves long-term memory. Acta Psychologica, 208, 103104. https://doi.org/10.1016/j.actpsy.2020.103104

40.

Schlegel

(2010). The effect of directed attention score study procedures on music majors’ error detection in three-part instrumental music [Doctoral dissertation, Louisiana State University]. https://digitalcommons.lsu.edu/gradschool_dissertations/1215

41.

Sheldon

(2004). Effects of multiple listenings on error-detection acuity in multivoice, multitimbral musical examples. Journal of Research in Music Education, 52(2), 102–115. https://doi.org/10.2307/3345433

42.

Sorenson

R. A.

(2021). Error detection in music education: A review of literature. Update: Applications of Research in Music Education, 40(1), 54–62. https://doi.org/10.1177/87551233211020989

43.

Spradling

(2010). Error detection: Exercises for the instrumental conductor. Carl Fischer.

44.

Stambaugh

(2016). Differences in error detection skills by band and choral preservice teachers. Journal of Research in Music Education, 25(2), 25–36. https://doi.org/10.1177/1057083714558421

45.

Stambaugh

Nichols

B. E.

(2020). The relationships among interval identification, pitch error detection, and stimulus timbre by preservice teachers. Journal of Research in Music Education, 67(4), 465–480. https://doi.org/10.1177/0022429419885931

46.

Szalárdy

Bendixen

Böhm

Davies

Denham

Winkler

(2014). The effects of rhythm and melody on auditory stream segregation. Journal of the Acoustic Society of America, 135(3), 1392–1405. https://doi.org/10.1121/1.4865196

47.

Talbert

M. D.

(2021). Melodic error detection of adult amateur musicians. Contributions to Music Education, 46, 53–70. https://www.jstor.org/stable/27125930

48.

Thornton

L. C.

(2008). The effect of grade, experience, and listening condition on the melodic error detection of fifth- and sixth-grade woodwind students. Update: Applications of Research in Music Education, 26(2), 4–10. https://doi.org/10.1177/8755123308317612

49.

Waggoner

D. T.

(2011). Effects of listening conditions, error types, and ensemble textures on error detection skills. Journal of Research in Music Education, 59(1), 56–71. https://doi.org/10.1177/0022429410396094

50.

Williams

M. L.

(2022). The effect of focus of attention on error detection ability. Journal of Research in Music Education, 69(4), 473–485. https://doi.org/10.1177/0022429421997449

51.

Wöllner

Halpern

A. R.

(2015). Attentional flexibility and memory capacity in conductors and pianists. Attention, Perception, & Psychophysics, 78(1), 198–208. https://doi.org/10.3758/s13414-015-0989-z

52.

Zokaei

Ning

Manohar

Feredoes

Husain

(2014). Flexibility of representational states in working memory. Frontiers in Human Neuroscience, 8, https://doi.org/10.3389/fnhum.2014.00853

The Roles of Attentional Flexibility and Pitch Imagery in Error Detection and Melodic Dictation

Abstract

Keywords

Melodic Dictation

Cognitive Processes

Method

Participants and Procedures

Melodic Dictation Test

Test Design

Scoring

Error Detection Test

Test Design

Scoring

Attention and Imagery Tests

Pitch Imagery

Attentional Flexibility

Procedure

Results

Discussion

Conclusion

Footnotes

Action Editor

Peer Review

Data Availability

Declaration of Conflicting Interests

Ethical Approval

Funding

ORCID iDs

References