Effects of Pitch and Rhythm Priming Tasks on Accuracy and Fluency During Sight-Reading

Abstract

The purpose of this study was to investigate the effects of pitch and rhythm priming tasks on sight-reading accuracy and fluency. High school wind instrumentalists (N = 182) were assigned to one of four experimental groups: pre-/posttest rhythm, pre-/posttest pitch, posttest-only rhythm, or posttest-only pitch. Participants sight-read selected stimulus exercises from the Watkins-Farnum Performance Scale and completed two priming treatments and a control condition as part of a repeated-measures design. A three-way repeated-measures MANOVA, with rhythm accuracy, pitch accuracy, and fluency accuracy as dependent measures, revealed a significant main effect due to priming condition. Rhythm accuracy scores were significantly lower after both perceptual and conceptual priming than after a control condition. No significant differences in pitch accuracy or fluency existed based on priming condition. No significant differences were found in rhythm, pitch, or fluency accuracy based on treatment condition (pitch or rhythm) or exposure condition (pre-/posttest or post only). Two-way repeated-measures MANOVAs revealed significant main effects based on time. Pitch accuracy and fluency each significantly improved between pre- and posttest and from the first to third study tasks. Results suggest that performing rhythm alone or pitch alone requires different cognitive processes than does performing both together.

Keywords

sight-reading priming rhythm accuracy pitch accuracy fluency

Sight-reading, as defined by the Oxford Dictionary of Music, is “the reading or singing of music at first sight in order to perform it.” It is valued by educators for its ability to give access to a broader range of musical literature, to allow greater enjoyment of music, to facilitate the communication and interpretation of notated music, and to “help students reduce the time it takes to prepare” the music on which they are working (Crider, 1989, p. 29). Sight-reading is embedded into many musical activities, such as local and national ensemble festivals, auditions, recording studio work, and even filling in for another musician at the last minute. With this in mind, music teachers need to have effective methods of helping their students become more accomplished sight-readers. In spite of the oft-professed importance of sight-reading, there is a relatively limited understanding of the complex skill of reading and performing music at first sight or how to teach for proficient sight-reading.

Researchers and practitioners alike have focused on participants’ abilities to play the right notes at the right time, highlighting the fundamental nature of pitch and rhythm in the creation of music. Rhythm-reading ability is believed to be a primary predictor of sight-reading achievement (Boyle, 1970; Elliot, 1982a, 1982b; Gromko, 2004; McPherson, 1994). Practitioners and researchers have spent decades suggesting ways in which music teachers might improve the rhythmic ability of their students. Kinesthetic activities and counting the beat aloud have been shown to have mixed effects on rhythm-reading and sight-reading accuracy (Boyle, 1970; Pierce, 1992; Salzberg & Wang, 1989). Verbal counting methods, used by many practitioners to represent patterns of rhythms, seem to affect music-reading achievement positively. For young students, counting systems improved students’ accuracy in reproducing rhythms (Bebeau, 1982; Colley, 1987). In addition, it has been observed that systematic instruction in verbal counting systems improves rhythm-reading achievement (Palmer, 1976).

Although researchers have investigated best practices for teaching rhythm reading, and practitioners claim that “rhythm may be the most critical aspect of sightreading” (Lambrecht, 2008, p. 32), the actual impact of rhythm-reading ability on students’ sight-reading ability as a whole is less well established. Elliot (1982b) found rhythm-reading ability and performance jury grades accounted for 88% of sight-reading score variance in undergraduate music majors. In a further categorization of sight-reading errors, Elliot (1982a) found 61% of errors to be rhythmic in nature. However, of the total number of rhythm errors, only 23% were errors in durational value, suggesting a need to consider factors beyond the ability to count.

The element of pitch in sight-reading often has been addressed from the standpoint of interval training for sight-singing. As a long-standing topic of discussion among school music practitioners, pitch verbalization has also become an area of focus for music education researchers. Although different systems (moveable vs. fixed do, hand signs vs. no hand signs) have no significant effect on achievement (Cassidy, 1993; Henry & Demorest, 1994), using a solfège system seems to be beneficial for sight-singing achievement (Cassidy, 1993), particularly in familiar patterns (Reifinger, 2012).

Henry investigated the function of intervals in sight-singing through a series of studies focused on pitch sets. She created an inventory of pitch sets (interval patterns) of varying difficulties in an effort to create a sight-reading test to be used in secondary classrooms (Henry, 2001). Through the testing and validation process of different test versions, she found that context matters. The difficulty of each pitch set did not function in isolation but was dependent upon what preceded it (Henry, 2003). Students with lower sight-reading abilities significantly improved their sight-singing skills after receiving instruction focused on pitch sets (Henry, 2004). It is interesting to note that although the order of notes and, more specifically, intervals was a significant factor in sight-singing achievement, key signature was not (Henry, 2013). This does not seem to be the case for instrumentalists. In a similar effort to create pitch sets across different keys for string players, Alexander and Henry (2012) found significant differences based on key signature, indicating that context matters in instrumental sight-reading because different physical techniques are often required to produce various combinations of pitches.

Research seems to support the idea that pattern recognition plays an important role in sight-reading ability (Goolsby, 1994). Just as singers improved in sight-singing achievement after pitch-set instruction, wind players also saw significant improvement when they learned notes in the context of tonal patterns rather than in isolation (MacKnight, 1975). Students scored significantly better on both sight-reading tasks and aural skills tasks when instruction emphasized tonal patterns (Grutzmacher, 1987). Learning notes in the context of musical excerpts, rather than as isolated notes, may help students to better perceive musical contour and patterns (Hahn, 1987) and contributes to a significant difference in the performance quality of rehearsed music (Price, Blanton, & Parrish, 1998). Pike and Carter (2010) also emphasized tonal patterns, or chunks, during instruction of university class piano students. While there was no significant overall difference in sight-reading skills, they proposed that isolating pitch chunks allowed participants to recognize patterns faster and pay attention to other details based on significant findings in subcategories.

Reading and decoding symbol systems of any kind are controlled by cognitive processes, which are influenced by an array of variables, including the complexity of the material. Sight-reading is a complex task that requires a musician to simultaneously decode a visual symbol system and to perform specific pitches at a designated time in addition to executing those tasks at a specified loudness and in a stylistically appropriate manner. The multiple responsibilities required in musical performances lead to differing degrees of intrinsic cognitive load based on the expertise of the performer. Although much of the research into multitask performance has occurred in fields outside of music, a few studies do specifically address musicians’ need to attend to multiple tasks.

Fluency is a term borrowed from the field of reading literacy and refers to consistency of performance in time. Others in the music field have used terms such as hesitations (Kostka, 2000) or beat continuity (Hanberry, 2004) to describe this concept of pulse continuity. It is a distinctly different skill from performing notes for their correct durational value. The ability to play without stopping, hesitating, repeating material, or abruptly changing tempos is an essential skill for students to develop in order to perform musically. Fluency in performance may be an indicator of cognitive load (Fuchs, Fuchs, Hosp, & Jenkins, 2001).

Cognitive load impacts the attention musicians give to specific elements of performances (Chaffin, 2011; Keller 2001). Complexity of information seemed to cause younger piano students to repeat notes during sight-reading (Gudmundsdottir, 2010). These errors were self-corrected in 30% of the cases, indicating that students knew the correct notes but had difficulty producing them in the context of a musical performance. It also has been observed that students disrupt the pulse in order to find the correct notes (Alexander & Henry, 2015; Cassidy, Betts, & Hanberry, 2001; Henry, 2011; Pike & Carter, 2010), suggesting that the intrinsic cognitive load of the task could not be processed in the allotted time.

A relationship seems to exist between working memory and sight-reading (Kopiez & Lee, 2006; Meinz & Hambrick, 2010). Task complexity and attentional demands in musical performance can overload working memory resources to the detriment of performance quality. Given that the human brain processes a limited amount of material at any given time, limitations exist as to the complexity of music one is able to accurately perform at sight. Evidence also suggests a potential relationship between pitch and rhythm processing in sight-reading errors (Cassidy et al., 2001; Drake & Palmer, 2000; Lehmann & Ericsson, 1993; Pike & Carter, 2010).

Based on current understandings of cognitive processes, it is reasonable to believe that as cognitive load decreases, performance quality may improve. One method for decreasing cognitive load may be through tasks that prime the brain for upcoming experiences. As a form of implicit memory (Tulving & Schacter, 1990), priming is “a change in the ability to identify or produce an item as a result of a specific prior encounter with the item” (Schacter & Buckner, 1998, p. 185). Priming allows individuals to respond more quickly and more accurately to previously experienced materials (Dell, Ratcliff, & McKoon, 1981). Perceptual priming can be achieved through simple repeated exposure to an item, but it is sensitive to context during repetitions (Wiggs & Martin, 1998). Alternatively, conceptual priming seems to involve a level of encoding that allows individuals to respond more quickly based on categorical information rather than direct repetition (Schacter & Buckner, 1998). Cognitive load therefore is decreased when information processing becomes more automatic as a result of repetition or connection to a previously established set of schema (Paas & van Merriënboer, 1994). Consequently, it stands to reason that skills such as rhythm reading or playing scales contribute to a musician’s ability to solve musical problems as presented in sight-reading examples more efficiently. In the practice of music education, teachers in rehearsal and knowledgeable students in their own practice often isolate pitch and rhythm in a process of decontextualization/recontextualization (Duke, Simmons, & Cash, 2009). Presumably by building more automaticity, through playing scales, recognizing various pitch patterns, and isolating rhythm patterns, students will be able to use that knowledge while sight-reading unfamiliar music.

Need for Study

Accurate sight-reading has been recognized as an important part of musicians’ abilities. The literature extends back over 80 years, with researchers and practitioners alike seeking ways to help musicians better those abilities and few clear outcomes. As Hodges (1992) points out, “it is apparent that the bulk of these studies are technique or strategy driven rather than based on any underlying theory of music reading” (p. 468). The cognitively complex task of simultaneously dealing with rhythm and pitch during musical performance has received little attention in the research literature. Therefore, the purpose of this study was to investigate the effect of rhythm and pitch priming tasks on sight-reading accuracy and fluency. Given the limited understanding of how these two elements are processed cognitively and evidence that there seems to be a relationship between pitch and rhythm errors, I sought to determine whether various types of rhythm or pitch priming tasks significantly change the accuracy and fluency of sight-reading musical excerpts and to provide further evidence regarding the roles of pitch and rhythm during sight-reading. Specifically, I aimed to address the following questions:

What is the effect of perceptual (specific) and conceptual (general) cognitive priming tasks on pitch, rhythm, and fluency accuracy during sight-reading?

Is there a significant difference in pitch, rhythm, or fluency accuracy between students who are primed through pitch exercises and those who are primed through rhythm exercises prior to playing the musical selection as originally composed?

Does playing through a musical selection a second time significantly change the accuracy of pitch, rhythm, or fluency?

Method

Participants

Participants (N = 182) in this study were high school band members recruited from public high schools surrounding a large public university in the southeastern United States. Statistical needs of the experimental design determined sample size. In addition, a priori power analyses indicated that an N ranging from 144 to 224 would yield acceptable power levels. All participants played either a woodwind (n = 105) or brass instrument (n = 77), with instrument distribution as follows: flute, 34; clarinet, 34; bass clarinet, 10; saxophone (alto, tenor), 20; double reeds (oboe, bassoon), 7; trumpet, 29; horn, 12; trombone, 20; euphonium, 6; tuba, 10. In order to minimize the confounding influence that level of performance skill might have on test results, participants had at least 3 years of prior playing experience on a wind instrument.

Given the complexity of issues inherent in the research questions, a repeated-measures design was chosen using both pretest/posttest and posttest-only groups (see Table 1). Because students were recruited from multiple schools and ensembles and played various instruments, stratified random sampling techniques were employed to assign participants to experimental groups. Prior to the start of testing at each school, participants’ names were sorted first by ensemble level, then by instrument family. Stratified sampling of brass and woodwind instrumentalists accounted for the possibility that different methods of sound production also could be a confounding factor in test scores. Names then were drawn randomly and assigned sequentially to one of four experimental groups: pre-/posttest rhythm (n = 45), pre-/posttest pitch (n = 45), post-only rhythm (n = 46), or post-only pitch (n = 46).

Table 1.

Experimental Design.

Group		Task 1			Task 2			Task 3
1	R	O	X₁	O	O	X₂	O	O	O
2	R	O	X₃	O	O	X₄	O	O	O
3	R		X₁	O		X₂	O		O
4	R		X₃	O		X₄	O		O

Note: R = random assignment; X₁ = specific or general rhythm treatment; X₂ = general or specific rhythm treatment; X₃ = specific or general pitch treatment; X₄ = general or specific pitch treatment; O = observations (sight-reading performances)

Prior to school and participant recruitment, institutional review board (IRB) approval was requested and granted. Following IRB-approved protocols, signed consent forms from participating schools’ administration and band directors, as well as parental consent and student assent forms, were collected before treatment began.

Materials

The Watkins-Farnum Performance Scale (WFPS; Watkins & Farnum, 1954/1962) has been a standard in research literature for measuring sight-reading ability. In order to present appropriate challenges in rhythm and pitch, Exercise 10 from Forms A and B were chosen as two of the three stimulus exercises for this study. The investigator composed a third stimulus exercise (Form C) using the same harmonic structure, rhythm units, patterns of motion, size of interval leaps, range, and style as the original WFPS exercises in order to provide enough material for the repeated-measures design (see Supplemental Figure S1 in the online version of the article). Results of pilot testing revealed a normal distribution of mean scores, indicating no floor or ceiling effect in the performance of the exercises, musical equivalency, and appropriateness for use in the study.

Treatment exercises were developed from each stimulus exercise. Specific rhythm and pitch treatments served as perceptual priming tasks. These treatments were literal, near-transfer reproductions of the rhythm and pitch of the stimulus exercises (see Supplemental Figures S2 and S3 in the online version of the article). Using a single-line staff, specific rhythm treatments were created by notating the rhythm directly from each stimulus exercise with only a meter signature at the beginning and bar lines to identify measures. A specific pitch treatment included all pitches renotated as quarter notes, regardless of their original value of duration, in the order in which they appeared in each stimulus exercise. The original clef and key signature were provided. In order to allow for the use of accidentals, bar lines were also provided but meter signatures were not, and the number of pitches between bar lines varied from four to six.

General treatments consisting of rhythm and scale patterns served as conceptual priming tasks (see Supplemental Figures S4 and S5 in the online version of the article). A general rhythm treatment was composed by the investigator using rhythm units contained in the stimulus exercises. Musical analysis revealed 14 unique one- or two-beat rhythm units across the three stimulus exercises. The units were ordered in various patterns throughout the treatment. As with the specific treatments, rhythms were notated on a single line, composed in common time, and 16 measures in length. A general pitch treatment composed of five-note scale patterns, along with their corresponding arpeggios, was also created. The scales were chosen based on the harmonic structure of the melody and were notated in a simple rhythm pattern. As with the specific treatments, the original clef, key signature, and bar lines were provided.

Procedure

At the time of testing, individual participants were invited into a practice room or office adjacent to their school band room and were asked several demographic survey questions regarding their primary instrument, secondary instruments, and playing experiences in and out of school. After collecting initial information, the investigator read the instructions for study tasks appropriate to the experimental group to which the participant had been assigned. Those assigned to pretest/posttest groups were given 30 s to silently preview the first stimulus exercise. At the end of 30 s, a metronome was turned on to give the performance tempo indicated (quarter note = 63), and participants were asked to play through the exercise one time as accurately as possible. After each participant began playing, the metronome was silenced and participants continued playing through the entire stimulus exercise. At the end of each performance, the music was removed from sight.

Participants then completed the first treatment task (specific exercise, general exercise, or contact control). During task instructions, the investigator focused participants’ attention on the relationship between stimulus and treatment rhythms or pitches. Participants then played through the exercise one time as accurately as possible. In the contact control period, when no treatment was administered, the investigator engaged the participants in conversation unrelated to the music being sight-read for an equivalent amount of time, approximately 1 min, between each playing of the stimulus exercise. Participants were asked what they most liked about playing their instrument and about any favorite music they had played in band.

After completing the treatment period, participants once again had 30 s to scan the stimulus exercise silently before playing through the exercise. This procedure was followed two more times with the two additional stimulus exercises. Those in the posttest-only groups also followed this procedure with the exception that they started each task with the treatment rather than the stimulus. Stimulus and treatment exercises were rotated to control for possible order effects or unintended treatment effects. Within each experimental group, there were six different orders of stimulus exercises and treatments.

All testing and treatment sessions were digitally audio recorded using a Zoom H5 portable recorder. Prior to analysis, individual audio files of each pretest and posttest were isolated from session recordings using digital audio software. These files then were randomized using a random-number-sequence generator (Random.org, 2015) and compiled for blind analysis by the investigator and a reliability judge.

Finally, following all study tasks, participants completed a short descriptive survey based on their perception of tasks included in the study. This survey solicited their opinion of the difficulty of tasks, the helpfulness of treatments, and their perception of pitch and rhythm in their own general performance.

Scoring

Participants’ sight-reading performances were scored for pitch accuracy, rhythm accuracy, and fluency accuracy. For the purposes of this study, pitch accuracy was defined as playing the correct pitch as notated in the exercises. Pitches added by participants or not played counted as errors. Intonation was not considered as long as each note was recognizable to scorers as the right pitch. Rhythm accuracy was defined as playing the correct durational value in relation to the notes preceding and/or following the note being played. In this study, fluency was defined as the degree to which music was performed with a consistent pulse. Given the hypothesis that priming may decrease cognitive load during musical performance, it follows that fluency, as a measure of consistent performance in time, should be considered in addition to pitch and rhythm.

In a modification of the WFPS original scoring system, the beat was used as the scoring unit and scored as either correct or incorrect for each category (pitch, rhythm, or fluency) of errors. Each category was considered independently. All stimulus exercises were 16 measures in length with four beats per measure; therefore, the highest possible raw score for rhythm was 64. Pitch and fluency accuracy was determined based on the onset of each note. Possible maximum raw scores for pitch and fluency then were 54 (10A), 55 (10B), and 53 (10C).

All recordings (N = 816) were scored by the investigator. Multiple hearings were utilized as necessary to determine the number of errors in each category. The number of errors was then subtracted from the total possible score to determine raw pitch, rhythm, and fluency scores. Raw scores were converted to percentages in order to allow for direct comparisons between musical elements and forms. Fifteen percent of the recordings (n = 123) were selected randomly to be scored by an independent reliability judge. Reliability between judges’ scores for each category and total summed scores for each recording was determined using Cronbach’s alpha set at .80 for this study. All measures exceeded minimum standards for reliability in this study, and full statistical analyses proceeded.

Results

Given the complexity of the study design and the number of potential confounding variables, a series of two-way repeated-measures ANOVAs were run to confirm balance between experimental groups. There were no significant differences (p > .05) in group means based on participants’ gender, grade, or self-reported outside music experiences, such as lessons or community-based ensemble participation. School, ensemble level, and instrument family were balanced through stratified sampling methods, and two-way repeated-measures ANOVAs confirmed no significant differences (p > .05) in group means based on those factors. Based on these analyses, each experimental group was considered to have equivalent samples of participants. Due to the repeated-measures design of the study, multiple musical examples were needed as stimulus exercises. A one-way repeated-measure ANOVA, with musical exercise as a within-subjects factor, showed no significant difference (p > .05) in means, confirming musical equivalency.

Main Effects

In order to address the three research questions, a three-way repeated-measures MANOVA was computed using posttest scores to determine the main effects of priming condition (specific, general, or control), treatment condition (rhythm or pitch), and exposure condition (pre-/posttest or post-only) on the accuracy of three musical elements (rhythm, pitch, and fluency). Results of the MANOVA were considered robust against effects of departures from multivariate normality due to equal cell sizes and the large sample size; therefore, Wilks’ λ values were used. Mauchley’s tests of sphericity were not significant, and sphericity was assumed for all univariate comparisons.

There was a significant main effect due to priming condition, Wilks’ λ = .92, F(6, 173) = 2.60, p = .02, partial η² = .08. Univariate tests revealed a significant difference in mean rhythm scores based on priming conditions, F(2, 356) = 5.20, p = .01, partial η² = .03. Levene’s test of equality of error variances confirmed univariate normality, and pairwise comparisons using Bonferroni adjustments showed that rhythm accuracy scores under specific priming (M = 90.28, SD = 11.45) and general priming (M = 90.58, SD = 11.14) were significantly lower (p < .001) than under the control condition (M = 92.11, SD = 9.34). Pitch and fluency accuracy scores were not significantly different based on priming condition.

There was no significant main effect for treatment condition, Wilks’ λ = .99, F(3, 176) = .81, p = .49, partial η² = .01. There was also no significant main effect for exposure condition, Wilks’ λ = .99, F(3, 176) = .349, p = .79 partial η² = .01. Finally there was no significant interaction between treatment condition and exposure condition, Wilks’ λ = .99, F(3, 176) = .30, p = .89, partial η² = .01. Multivariate interactions between within-subjects and between-subjects factors were also nonsignificant.

Secondary Analyses

Time and order effects

Using data from participants in only the pretest/posttest condition (n = 90), a two-way repeated-measures MANOVA with priming condition and time (pretest and posttest) as within-subjects factors was calculated. Unlike the main analysis, there was no significant difference in accuracy based on priming condition. There was a significant difference based on time, Wilks’ λ = .85, F(3, 87) = 5.08, p < .01, partial η² = .15. Univariate tests revealed significant differences in both pitch scores, F(1, 89) = 8.317, p < .01, and fluency scores, F(1, 89) = 10.795, p = .001. Posttest pitch and fluency scores were higher than pretest scores.

Participants completed three different tasks as part of the study. Posttest accuracy scores for all participants (N = 182) were compared based on task order. A two-way repeated-measures MANOVA with task order as a within-subject factor and treatment order as between-subjects factor revealed a significant main effect for task order, Wilks’ λ = .87, F(6, 171) = 4.16, p = .001, partial η² = .13. Univariate tests revealed significant differences in pitch accuracy, F(2, 352) = 8.82, p < .001, and fluency accuracy, F(2, 352) = 6.23, p < .01, based on task order. Pitch accuracy significantly increased between Tasks 1 and 2 and between Tasks 2 and 3. Fluency was significantly more accurate in the third task than in the first task. Task order did not significantly change rhythm accuracy. There was no significant effect in accuracy based on treatment order. It is interesting to note that both pitch and fluency scores were highest in the third task regardless of treatment condition, and in a confirmation of the significant main effect of priming condition, rhythm accuracy scores were highest under the control condition in four of the six task orders regardless of treatment order.

School, instrument family, and ensemble level

Through the process of stratified sampling, experimental groups were balanced for the factors of school (1–5), instrument family (woodwind or brass), and ensemble level (top, second, and third). Repeated-measures MANOVAs confirmed no significant difference in group means based on these factors; however, significant differences existed within each of these factors.

Fluency accuracy significantly differed based on school, F(4, 177) = 3.53, p < .01. Participants from School 1 performed more fluently (M = 90.47, SD = 9.64) than participants from School 3 (M = 81.46, SD = 11.04) and School 5 (M = 80.29, SD = 12.85). No other significant differences emerged between schools.

Significant differences based on ensemble level existed in rhythm accuracy, F(2, 179) = 9.49, p < .001; pitch accuracy, F(2, 179) = 8.60, p < .001; and fluency, F(2, 179) = 30.28, p < .001. Pairwise comparisons with Bonferroni adjustments revealed participants in top ensembles had more accurate rhythm (M = 93.88, SD = 7.80) than participants in second ensembles (M = 88.14, SD = 12.38) or third ensembles (M = 87.29, SD = 12.38). Top ensemble participants also had better pitch accuracy (M = 86.25, SD = 14.75) than those in second ensembles (M = 76.31, SD = 18.97) but not those in third ensembles. Finally, participants in top ensembles played with better fluency (M = 89.08, SD = 8.84) than members of second ensembles (M = 77.22, SD = 12.71) or third ensembles (M = 81.69, SD = 11.09). No significant differences in accuracy scores existed between participants in the second and third ensembles.

Instrument family was another source of differences in sight-reading accuracy scores. There was no significant difference in rhythm accuracy or fluency scores; however, pitch accuracy was significantly different, F(1, 180) = 58.27, p < .001, based on instrument family. Participants who played woodwind instruments (M = 88.47, SD = 10.32) performed significantly more accurate pitches than did those who played brass instruments (M = 71.97, SD = 20.76).

Discussion

Treatments in this study were designed to isolate the roles of rhythm and pitch in music reading. Investigators have observed an interaction between pitch accuracy and timing accuracy (Alexander & Henry, 2015; Cassidy et al., 2001; Henry, 2011; Pike & Carter, 2010). Building on those observations, I asked what the effects would be on the accuracy of performing music from notation if participants were primed with rhythm or pitch. It was hypothesized that accuracy would increase because prior experience with one or the other element in isolation would reduce the cognitive load during music reading.

Results suggest priming was not effective. Isolating musical elements did not increase accuracy, and contrary to the hypothesis, pattern recognition in performances did not become more automatic. Perceptual and conceptual priming tasks did not produce significant differences in pitch or fluency accuracy as compared to a control condition. Rhythm accuracy scores, however, were significantly lower after priming treatments than after the control condition. There was also no significant difference in accuracy scores between participants who completed pitch treatments and those who completed rhythm treatments. Finally, playing through a musical selection a second time significantly increased pitch and fluency scores, but posttest scores of participants who played the musical selections twice were not significantly different from posttest scores of participants who played through each selection once.

Results of priming were unexpected, particularly the significantly lower rhythm accuracy scores. The difference in mean rhythm scores was relatively small by practical standards (less than 2 points), but the medium effect size (Lomax & Hahs-Vaughn, 2012) of η_p² = .08 suggests this result may be meaningful. By its very definition, priming is said to have occurred when speed or accuracy of a task increases (Tulving & Schacter, 1990). Given that accuracy scores did not significantly increase after treatments, it would be plausible to argue that no priming actually took place. The absence of a positive priming effect could be related to generally high mean accuracy scores for rhythm (M = 90.28–92.11), pitch (M = 80.80–81.86), and fluency (M = 83.43–83.93). Although pitch and fluency scores significantly increased over time, rhythm scores did not. It is possible that participants performed at or near their full potential for rhythm accuracy and would not have scored any higher no matter what the treatment had been. Then again, the potential ceiling effect in rhythm and generally high scores in pitch and fluency may indicate that the stimulus exercises were suitable for the skills of the students but not challenging enough to produce a priming effect. Another plausible alternative is that conversation during the contact control period positively impacted participants’ performance of rhythm during that task.

Priming tasks for this study were developed as a first attempt to apply principles of priming to the performance of musical notation. Prior priming research in music has focused on priming through listening (Jungers, 2007; Marmel & Tillman, 2009; Poulin-Charronnat, Bigand, & Madurell, 2005). However, in this study, the focus was on the visual perception of musical notation as translated into an aural performance, so exemplars of priming tasks were gathered from cognitive psychology literature. In that literature, priming tasks often focus on text-based information, yet nonverbal, visual stimuli also have been developed and used effectively (Tulving & Schacter, 1990). It seemed, then, that using music notation, a nonverbal stimulus, that maintained a semantic context might be an effective manner to prime a performance-based response.

Information being primed needs to be the focus of attention for participants (Wiggs & Martin, 1998). Although instruction prior to each priming task alerted participants to the fact that they would be playing pitches or rhythms associated with the musical example, posttest survey data indicated that there was no relationship between participants’ reported element of focus during sight-reading (pitch or rhythm) and their assigned experimental group. It is possible, then, that priming was ineffective because of an attentional matter.

It also might be possible that priming exercises, as developed in this study, did not function as expected. General tasks were intended to serve as conceptual primes, which are driven by the connection of categorical information and exemplars of those categories (Schacter & Buckner, 1998; Tulving & Schacter, 1990). Specific tasks were intended to act as perceptual primes, which have been shown to be most effective when the prime and the test item are identical in format (Schacter & Buckner, 1998) and meaning (Bainbridge, Lewandowsky, & Kirsner, 1993). Although both pitch and rhythm exercises were either literal transcriptions or general representations of the sight-reading stimuli, the isolated state of pitch and rhythm may have changed the semantic context of those elements and therefore the function of the primes unintentionally.

The lack of positive priming effects regardless of treatment condition seems to indicate that performing rhythm alone or pitch alone requires different cognitive processes than does performing both together. Evidence outside the field of music demonstrates compromised priming effects due to cognitive interference (Martens & Wolters, 2002; Slowiaczek & Hamburger, 1992). It is possible that this type of interference may have impacted participants’ sight-reading accuracy, particularly given the significantly lower scores for rhythm. Researchers have observed that accurately playing isolated rhythms does not increase reading accuracy when those rhythms are in the context of a melody (Boyle, 1969; Pierce, 1992). By adding the simultaneous element of pitch, the accuracy of duration is compromised. Additionally, priming participants with pitch in the current study may have focused their attention in a manner that changed the perception and performance of rhythm in different ways than what happened under control conditions. In other words, it may be possible that the normal patterns of processing were disrupted by the dominance of a different focus of attention. This is of practical importance given the frequent use of rhythm and pitch isolation by teachers as a rehearsal strategy. Practitioner-based “priming” exercises are used with the intention of preventing or correcting performance errors. If isolated elements indeed are processed differently than when combined, it is important that practitioners remain aware of the effects, if any, that these strategies actually produce in the classroom.

Brain research is mixed on the conclusion of independent or interdependent processing of pitch and rhythm (Hodges & Nolker, 2011; Lee & Wang, 2011; Neuhaus & Knösche, 2008; Schön & Besson, 2002). Results of the current study not only support findings that recognized the distinct nature of pitch and rhythm but also suggest that rhythm processing may change based on the presence of pitch. With the added complexity of performing from notation, rather than simply completing recognition tasks as employed in previous brain research, it is not possible to draw conclusions as to whether these results support the independent or interdependent theory of processing.

Results of this study indicate that participants performed more accurately over time. Pre- to posttest gains are well established in sight-reading literature, as is seen in Mishra’s (2014) meta-analysis. Not unexpectedly, pitch and fluency were significantly more accurate in posttesting than in pretesting in this study. Perhaps of greater importance is the comparison of posttest scores between those who completed a pretest and those who did not. The experimental design of this study intentionally addressed potential testing threats to validity due to familiarity with stimulus music, with the goal of gaining a better understanding of the limits of what could be considered reading at sight. No significant difference in posttest accuracy scores was found between those who completed pretests and those who did not. Participants who completed the pretest still seemed to perform at the posttest in a manner consistent with sight-reading rather than a rehearsed performance.

Pitch and fluency scores also increased significantly over the course of the three study tasks; however, rhythm scores did not. Given the short time participants were given to scan the material, it is unlikely these gains were due to practice effects, but it is plausible that the tasks constituted an unintended priming experience from one task to the next. Each of the three musical exercises used the same key signature, harmonic structure, and rhythmic units. These underlying musical structures may have served as conceptual primes as each participant worked through the three examples. This may indicate that pitch can be primed in ways rhythm cannot.

The repeated-measures design of this study also revealed insights about the nature of fluency. It is common among most published sight-reading research to include all timing errors in the category of rhythm. Elliott’s (1982a) seminal work in this area identified rhythm, which included all timing errors, as a primary predictor of sight-reading accuracy. McPherson (1994) and Gromko (2004) also came to similar conclusions. However, including all timing errors under the umbrella of “rhythm” fails to account for the distinct roles of durational value knowledge and performance of music in time. Evidence suggests that factors other than knowing how the rhythm goes may be more important to a fluent performance. It has been observed in singers and string playing that students first attend to the pitch (what to play) and then timing (when to play) during sight-reading (Alexander & Henry, 2015; Henry, 2011).

Results in this study revealed that fluency, when considered independently, mirrored pitch accuracy, not rhythm accuracy. As pitch accuracy increased, so did fluency. This supports previous observations and suggests that pitch plays a far more important role in instrumental sight-reading than has been acknowledged previously. Researchers and practitioners alike have focused on the best ways to teach students to “count” or perform rhythms, yet it is not the counting that appears to be the issue. More likely it is the need to identify and prepare to play pitches while simultaneously processing when and for how long to play those notes. Music educators then may be served best by engaging their students in experiences that support these simultaneous processes—experiences that build automaticity in pitch recognition and experiences in rhythm reading that, instead of isolating rhythm, also use pitch to enhance students’ abilities to perform more fluently when faced with novel pieces of music.

Results based on the stratified sampling techniques employed revealed important differences. Mean pitch scores for woodwind players were 16.5 points higher than for brass players. This was both statistically significant and musically meaningful. Based on findings by Elliot (1982a), it was hypothesized that the issue of “right-partials” might influence the scores of brass players. Observations made during the current study indicated that brass players were challenged by the partial issue in one of two ways: (a) Students played through large sections of the examples on the wrong partial, or (b) students missed relatively isolated notes, usually in large leaps that crossed partials. Most participants who made these mistakes seemed unaware that they were playing the wrong notes given the lack of correction, or attempt at correction, in their performances.

These errors point to a critical lack of awareness of the sound of each note on the page. Without a clear, preconceived aural representation of the sound and of the intervals represented in melodies, brass participants, who must negotiate the challenges of controlling pitch based on the harmonic series, were significantly less accurate than their woodwind counterparts. This supports findings that more skilled music readers seem to be able to transform visual notation into an auditory representation (Hayward & Gromko, 2009; Waters, Townsend, & Underwood, 1998). Additionally, Mishra (2014) found in her meta-analysis that aural training was one of few treatments that significantly affected sight-reading scores. This suggests that the development of an aural representation of the sound is important for sight-reading activities and seems to be essential for brass players, given current results.

Music reading, defined as a process of creating sound from the perception of a visual symbol, is a complex task. As of yet, no definitive theory of music reading learning has been developed, and most research exists in isolated studies rather than sustained lines of inquiry. Results from this study suggest that there is much work still to do. The relationships between the multiple factors involved in music reading are still unclear and deserve our attention as we continue to refine best practices for developing independent musicians. Authors of future research might continue to explore priming in sight-reading tasks, fluency as a factor independent from rhythm, or durational value, and the relationship of aural representations and reading music at sight.

Supplemental Material

Russell_Priming_SightReading_Accuracy_Supplemental_Materials – Supplemental material for Effects of Pitch and Rhythm Priming Tasks on Accuracy and Fluency During Sight-Reading

Supplemental material, Russell_Priming_SightReading_Accuracy_Supplemental_Materials for Effects of Pitch and Rhythm Priming Tasks on Accuracy and Fluency During Sight-Reading by Christine R. Russell in Journal of Research in Music Education

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Supplemental Material

The supplemental figures are available in the online version of the article at

Author Biography

Christine R. Russell is an assistant professor of music at The University of Akron. Her research interests are related to music cognition and literacy.

References

Alexander

M. L.

Henry

(2012). The development of a string sight-reading pitch skill hierarchy. Journal of Research in Music Education, 60, 201–216. doi:10.1177/0022429412446375

Alexander

M. L.

Henry

M. L.

(2015). The effect of pitch and rhythm difficulty on high school string sight-reading performance. String Research Journal, 6, 71–83. doi:10.1177/194849921500600005

Bainbridge

J. V.

Lewandowsky

Kirsner

(1993). Context effects in repetition priming are sense effects. Memory and Cognition, 21, 619–626. doi:10.3758/BF03197194

Bebeau

M. J.

(1982). Effects of traditional and simplified methods of rhythm-reading instruction. Journal of Research in Music Education, 30, 107–119. doi:10.2307/3345042

Boyle

J. D.

(1969). Rhythm sight reading: The key to music sight reading. Instrumentalist, 24(2), 42–43.

Boyle

J. D.

(1970). The effect of prescribed rhythmical movements on the ability to read music at sight. Journal of Research in Music Education, 18, 307–318. doi:10.2307/3344498

Cassidy

J. W.

(1993). Effects of various sightsinging strategies on nonmusic majors’ pitch accuracy. Journal of Research in Music Education, 41, 293–302. doi:10.2307/3345505

Cassidy

J. W.

Betts

Hanberry

M. A.

(2001). The effect of structured left hand practice on piano performance accuracy among undergraduate music majors. Bulletin of the Council for Research in Music Education, 148, 31–36. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/40319075

Chaffin

(2011). An examination of the cognitive workload associated with conducting in an instrumental music context: A review of literature. Bulletin of the Council for Research in Music Education, 189, 73–87. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/10.5406/bulcouresmused.189.0073

10.

Colley

(1987). A comparison of syllabic methods for improving rhythm literacy. Journal of Research in Music Education, 35, 221–235. doi:10.2307/3345075

11.

Crider

P. A.

(1989). Sight-reading: Is it a lost art? Instrumentalist, 43(10), 29–35.

12.

Dell

G. S.

Ratcliff

McKoon

(1981). Study and test repetition effects in item recognition priming. American Journal of Psychology, 94, 497–511. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/1422259

13.

Drake

Palmer

(2000). Skill acquisition in music performance: Relations between planning and temporal control. Cognition, 74, 1–32. doi:10.1016/S0010-0277(99)00061-x

14.

Duke

R. A.

Simmons

A. L.

Cash

C. D.

(2009). It’s not how much; it’s how: Characteristics of practice behavior and retention of performance skills. Journal of Research in Music Education, 56, 310–321. doi:10.1177/0022429408328851

15.

Elliot

C. A.

(1982a). The identification and classification of instrumental performance sight-reading errors. Journal of Band Research, 18(1), 36–42. Retrieved from https://search.proquest.com/docview/1312121443

16.

Elliot

C. A.

(1982b). The relationships among instrumental sight-reading ability and seven selected predictor variables. Journal of Research in Music Education, 30, 5–14. doi:10.2307/3344862

17.

Fuchs

L. S.

Fuchs

Hosp

M. K.

Jenkins

J. R.

(2001). Oral reading fluency as an indicator of reading competence: A theoretical, empirical, and historical analysis. Scientific Studies of Reading, 5, 239–250. doi:10.1207/s1532799xssr0503_3

18.

Goolsby

T. W.

(1994). Profiles of processing: Eye movements during sightreading. Music Perception: An Interdisciplinary Journal, 12, 97–123. Retrieved from http://jstor.org/stable/40285757

19.

Gromko

J. E.

(2004). Predictors of music sight-reading ability in high school wind players. Journal of Research in Music Education, 52, 6–15. doi:10.2307/3345521

20.

Grutzmacher

P. A.

(1987). The effect of tonal pattern training on the aural perception, reading recognition, and melodic sight-reading achievement of first-year instrumental students. Journal of Research in Music Education, 35, 171–181. doi:10.2307/3344959

21.

Gudmundsdottir

H. R.

(2010). Pitch error analysis of young piano students’ music reading performances. International Journal of Music Education, 28, 61–70. doi:10.1177/0255761409351342

22.

Hahn

(1987). Music reading and language reading: Correlations in processes and instruction. Bulletin of the Council for Research in Music Education, 93, 41–48. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/40318164

23.

Hanberry

(2004). Effects of practice strategies, metronome use, meter, hand, and musical function on dual-staved piano performance accuracy and practice time usage of undergraduate non-keyboard music majors (Doctoral dissertation). Retrieved from http://etd.lsu.edu/docs/available/etd-04082004-223228/

24.

Hayward

C. M.

Gromko

J. E.

(2009). Relationships among music sight-reading and technical proficiency, spatial visualization, and aural discrimination. Journal of Research in Music Education, 57, 26–36. doi:10.1177/0022429409332677

25.

Henry

(2001). The development of a vocal sight-reading inventory. Bulletin of the Council for Research in Music Education, 150, 21–35. Retrieved from http://www.jstor.ord/stable/40319097

26.

Henry

(2003). A comparison of testing formats for vocal sight-reading. Texas Music Education Research. Retrieved from http://www.tmea.org/assets/pdf/research/Hen2003.pdf

27.

Henry

(2004). The use of targeted pitch skills for sight-singing instruction in the choral rehearsal. Journal of Research in Music Education, 52, 206–217. doi:10.2307/3345855

28.

Henry

(2011). The effect of pitch and rhythm difficulty on vocal sight-reading performance. Journal of Research in Music Education, 59, 72–84. doi:10.1177/0022429410397199

29.

Henry

(2013). The effect of key on vocal sight-reading achievement. Texas Music Education Research. Retrieved from http://www.tmea.org/assets/pdf/research/Hen2013.pdf

30.

Henry

Demorest

(1994). Individual sight-singing achievement in successful choral ensembles. Update: Applications of Research in Music Education, 13(1), 4–8. doi:10.1177/875512339401300102

31.

Hodges

D. A.

(1992). The acquisition of music reading skills. In Colwell

(Ed.), Handbook of research on music teaching and learning: A project of the Music Educators National Conference (pp. 466–471). New York, NY: Schirmer Books.

32.

Hodges

D. A.

Nolker

D. B.

(2011). The acquisition of music reading skills. In Colwell

Webster

P. R.

(Eds.), MENC handbook of research on music learning: Volume 2. Applications (pp. 61–91). Oxford, UK: Oxford University Press.

33.

Jungers

(2007). Performance priming in music. Music Perception: An Interdisciplinary Journal, 24, 395–399. doi:10.1525/mp.2007.24.4.395

34.

Keller

P. E.

(2001). Attentional resource allocation in musical ensemble performance. Psychology of Music, 29, 20–38. doi:10.1177/0305735601291003

35.

Kopiez

Lee

J. I.

(2006). Towards a dynamic model of skills involved in sight reading music. Music Education Research, 8, 97–120. doi:10.1080/14613800600570785

36.

Kostka

M. J.

(2000). The effects of error-detection practice on keyboard sight-reading achievement of undergraduate music majors. Journal of Research in Music Education, 48, 114–122. doi:10.2307/3345570

37.

Lambrecht

(2008). Sightreading, a year-long approach. Instrumentalist, 62(6), 32–37.

38.

Lee

H. Y.

Wang

Y. S.

(2011). Visual processing of music notation: A study of event-related potentials. Perceptual and Motor Skills, 112, 525–535. doi:10.2466/11.22.24.27.PMS.112.2.525-535

39.

Lehmann

A. C.

Ericsson

K. A.

(1993). Sight-reading ability of expert pianists in the context of piano accompanying. Psychomusicology, 12, 182–195. doi:10.1037/h0094108

40.

Lomax

R. G.

Hahs-Vaughn

D. L.

(2012). Statistical concepts: A second course. New York, NY: Routledge.

41.

MacKnight

C. B.

(1975). Music reading ability of beginning wind instrumentalists after melodic instruction. Journal of Research in Music Education, 23, 23–34. doi:10.2307/3345200

42.

Marmel

Tillmann

(2009). Tonal priming beyond tonics. Music Perception: An Interdisciplinary Journal, 26, 211–221. doi:10.1525/mp.2009.26.3.211

43.

Martens

Wolters

(2002). Interference in implicit memory caused by processing of interpolated material. American Journal of Psychology, 115, 169–185. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/1423433

44.

McPherson

G. E.

(1994). Factors and abilities influencing sightreading skill in music. Journal of Research in Music Education, 42, 217–231. doi:10.2307/3345701

45.

Meinz

E. J.

Hambrick

D. Z.

(2010). Deliberate practice is necessary but not sufficient to explain individual differences in piano sight-reading skill: The role of working memory capacity. Psychological Science, 21, 914–919. doi:10.1177/0956797610373933

46.

Mishra

(2014). Improving sightreading accuracy: A meta-analysis. Psychology of Music, 42, 131–156. doi:10.1177/0305735612463770

47.

Neuhaus

Knӧsche

T. R.

(2008). Processing of pitch and time sequences in music. Neuroscience Letters, 441, 11–15. doi:10.1016/j.neulet.2008.05.101

48.

Paas

F. G. W. C.

van Merriënboer

J. J. G.

(1994). Instructional control of cognitive load in the training of complex cognitive tasks. Educational Psychology Review, 6, 351–371. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/23359294

49.

Palmer

(1976). Relative effectiveness of two approaches to rhythm reading for fourth-grade students. Journal of Research in Music Education, 24, 110–118. doi:10.2307/3345154

50.

Pierce

M. A.

(1992). The effects of learning procedure, tempo, and performance condition on transfer of rhythm skills in instrumental music. Journal of Research in Music Education, 40, 295–305. doi:10.2307/3345837

51.

Pike

P. D.

Carter

(2010). Employing cognitive chunking techniques to enhance sight-reading performance of undergraduate group-piano students. International Journal of Music Education, 28, 231–246. doi:10.1177/0255761410373886

52.

Poulin-Charronnat

Bigand

Madurell

(2005). The influence of voice leading on harmonic priming. Music Perception: An Interdisciplinary Journal, 22, 613–627. doi:10.1525/mp.2005.22.4.613

53.

Price

H. E.

Blanton

Parrish

R. T.

(1998). Effects of two instructional methods on high school band students’ sight-reading proficiency, music performance, and attitude. Update: Applications of Research in Music Education, 17(1), 14–20. doi:10.1177/875512339801700104

54.

Random.org. (2015). Random sequence generator. Retrieved from https://www.random.org/sequences/

55.

Reifinger

J. L.

(2012). The acquisition of sight-singing skills in second-grade general music: Effects of using solfège and of relating tonal patterns to songs. Journal of Research in Music Education, 60, 26–42. doi:10.1177/0022429411435683

56.

Salzberg

R. S.

Wang

C. C.

(1989). A comparison of prompts to aid rhythmic sight-reading of string students. Psychology of Music, 17, 123–131. doi:10.1177/0305735689172003

57.

Schacter

D. L.

Buckner

R. L.

(1998). Priming and the brain. Neuron, 20, 185–195. doi:10.1016/S0896-6273(00)80448-1

58.

Schӧn

Besson

(2002). Processing pitch and duration in music reading: A RT-ERP study. Neuropsychologia, 40, 868-878. doi:10.1016/S0028-3932(01)00170-1

59.

Sight-reading. (n.d.). In The Oxford Dictionary of Music. Retrieved from http://www.oxfordmusiconline.com:80/subscriber/article/opr/t237/e9422

60.

Slowiaczek

L.M.

Hamburger

(1992). Prelexical facilitation and lexical interference in auditory word recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18, 1239–1250. doi:10.1037/0278-7393.18.6.1239

61.

Tulving

Schacter

D. L.

(1990). Priming and human memory systems. Science, 247, 301–306. Retrieved from https://www-jstor-org.web.bisu.edu.cn/stable/2873625

62.

Waters

A. J.

Townsend

Underwood

(1998). Expertise in music sight reading: A study of pianists. British Journal of Psychology, 89, 123–149. doi:10.1111/j.2044p-8295.1998.tb02676.x

63.

Watkins

J. G.

Farnum

S. E.

(1954/1962) The Watkins-Farnum Performance Scale. Winona, MN: Hal Leonard.

64.

Wiggs

C. L.

Martin

(1998). Properties and mechanisms of perceptual priming. Current Opinion in Neurobiology, 8, 227–233. Retrieved from http://biomednet.com/elecref/0959438800800227

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