Retrieval Structures

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Cognitive Psychology 44, 132 (2002)

doi:10.1006/cogp.2001.0759, available online at http://www.idealibrary.com on

The Role of Retrieval Structures in Memorizing Music

Aaron Williamon

Royal College of Music, London, United Kingdom

and

Elizabeth Valentine

Royal Holloway, University of London, United Kingdom

This article explores the use of structure in the encoding and retrieval of musicand its relation to level of skill. Twenty-two pianists, classified into four levels ofskill, were asked to learn and memorize an assigned composition by J. S. Bach(different for each level). All practice was recorded on cassette tape. At the end ofthe learning process, the pianists performed their assigned composition in a recitalsetting. The performances were subsequently evaluated by three experienced pia-

nists according to a standardized grading system. From the cassette tapes, values forthe frequency with which pianists started and stopped their practice on structural,difficult, and other bars were obtained. Starts and stops on each bar type werecompared across three stages of the learning process. The analyses reveal that allpianists, regardless of level, started and stopped their practice increasingly on struc-tural bars and decreasingly on difficult bars across the learning process. Moreover,the data indicate that starts and stops increased on structural bars and decreasedon difficult bars systematically with increases in level of skill. These findings areinterpreted and discussed so as to elucidate characteristics of the retrieval structures

adopted by musicians in their practice and performance and how the formation anduse of retrieval structures develop as a function of expertise. Finally, the elicitedvalues for starts on structural, difficult, and other bars are examined and discussedaccording to how they relate to the pianists scores on performance quality. 2002Elsevier Science (USA)

The research presented in this article was supported by grants from the Committee of Vice-Chancellors and Principals of the Universities of the United Kingdom and the Rotary Founda-

tion. The authors gratefully acknowledge the help of the participating musicians; the participat-ing music teachers (Florence Creighton, Elaine Goodman, Heli Ignatius-Fleet, Nadia Lasser-son, Murial Levin, and Danielle Salamon); Roger Chaffin, Gabriela Imreh, and Carola Grindeafor guidance in the early stages of this work; John Valentine for statistical advice; SusanHallam, John Sloboda, and the referees for comments on earlier versions of this article; andBen Chaffin for computer programming assistance.

Address correspondence and reprint requests to Aaron Williamon, Royal College of Music,Prince Consort Road, London SW7 2BS, United Kingdom. E-mail: [email protected].

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0010-0285/02 $35.00 2002 Elsevier Science (USA)

All rights reserved.


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2 WILLIAMON AND VALENTINE

Music is one of the richest domains for studying skilled performance. Notonly must musicians recall and execute a wide range of information duringperformance, but they must do so in different environments, under differentlevels of anxiety, and generally while considering and incorporating the ideas

of conductors and other ensemble members. Moreover, they must satisfythese cognitive, perceptual, motor, and social demands with novel musicalinsight and seemingly little technical effort. Music, therefore, provides aunique and fertile testing ground for assessing existing theories of the acqui-sition and performance of skilled behavior.

One major feature which has been highlighted is the use of structure toorganize cognition. Chase and Simons (1973a, 1973b) Chunking Theoryproposes that expertise in a domain is acquired by learning a large databaseof chunksa chunk being a collection of information that forms a meaning-ful unit. These chunks are indexed by a discrimination net, an organized treestructure that enables the accessing of prescribed patterns by providing a setof instructions to the perceptual system for systematically scanning the largerepertoire of patterns stored in long-term memory (LTM). The discriminationnet allows rapid categorization of domain-specific patterns and accounts forthe speed with which experts recognize key elements in problem situations.

Chunks also give access to semantic memory consisting of productions andschemas (Simon, 1989). Each familiar chunk in long-term memory is a con-dition of a production that may be satisfied by the recognition of the percep-tual pattern and evokes an action (Newell & Simon, 1972). This explainsthe rapid solutions typically proposed by experts and offers a theoreticalaccount of intuition (Simon, 1986). Empirical evidence from many do-mains (e.g., physics, see Larkin, McDermott, Simon, & Simon, 1980; andmathematics, see Hinsley, Hayes, & Simon, 1977) reveals that experts, un-like novices, use forward search to solve problems and, therefore, makeheavy use of productions based on pattern recognition.

Chase and Ericssons (1982) Skilled Memory Theory, designed to addresscertain limitations of Chunking Theory, proposes that remarkable displaysof memory result from the creation and efficient use of retrieval structures.In doing so, individuals must associate the encoded information with appro-priate retrieval cues. This association permits the activation of a particular

retrieval cue at a later time and, thus, partially reinstates the conditions ofencoding so that the desired information can be retrieved from LTM. Onlyafter a set of retrieval cues is organized in a stable structure is a retrievalstructure formed, thereby enabling individuals to retrieve stored informa-tion efficiently without lengthy search (Ericsson & Staszewski, 1989,p. 239).

According to Chase and Ericsson, encoded information within the retrievalstructure is organized and retrieved according to both hierarchical and serialprinciples. Hierarchy is the arrangement of components into various levels


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RETRIEVAL STRUCTURES IN MUSIC 3

of complexity and importance. Serial organization refers to the sequential(i.e., linear) organization of encoded material as demonstrated in studies byShaffer (1976) and Palmer and van de Sande (1995). Chase and Ericsson(1981) demonstrated both of these in their subject SF, who acquired excep-

tional digit span. At the lowest hierarchical level, SF used mnemonic associa-tions to running times and other numerical relations in order to group digitsinto units. Then, SF used spatial relationships to encode digit groups intosupergroups. At the time of recall, SF could easily regenerate any of theunique locations of the retrieval structure and use a given location as a cueto retrieve the corresponding digit group. Chase and Ericsson (1981) high-lighted the serial principles that govern retrieval structures through a cued-recall task. They found that SF could access digit groups when descriptionsof their location within the retrieval structure were used as cues.

Hierarchical organization appears to be a cognitive principle of wide gen-erality, applying to the encoding and retrieval of both motoric and symbolicinformation (Johnson, 1970; Rosenbaum, 1987). Rosenbaum, Kenny, andDerr (1983) provided evidence for the hierarchical control of learned se-quences of finger taps, from the pattern of interresponse times in their pro-duction. They applied their tree-traversal modelaccording to which re-

sponse latencies are a function of the length of the node path to betraversedto the explanation of data from Sternberg, Monsell, Knoll, andWright (1978). In a series of studies on the timing of brief bursts of responsesin typing and speaking, these authors showed that (1) the latency of the firstresponse increased linearly with the length of the sequence, (2) the timeto complete a sequence increased quadratically with burst length, and (3)interresponse times were longer in the middle than at the ends of a sequence.Other notable areas in which hierarchical processes have been demonstratedare language (Chomsky, 1957; Wall, 1972) and categorization (Collins &Quillian, 1969, 1970).

More recently, pertinent data have come from the investigation of the ef-fect of temporal accent structure on memory for filmed narratives. Boltz(1992) demonstrated that insertion of commercials at major episode bound-aries (thus highlighting a storys underlying organization) resulted in higherrecall and recognition performance and superior memory for temporal order

information and details from the storys plot than did insertion of commer-cials within episodes (hence obscuring the storys underlying structure). Fur-thermore, this attentional highlighting of episode boundaries enhanced selec-tive recall and recognition of breakpoint scenes compared with those atnonbreakpoints. These findings suggest that episode boundaries are used asreferents for attention and recall.

The Skilled Memory Theory has commonly been accepted as accountingfor exceptional memory (Schneider & Detweiler, 1987; Carpenter & Just,1989; Anderson, 1990; Baddeley, 1990; Newell, 1990; Ericsson & Kintsch,


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1995). Furthermore, the mechanisms based on retrieval structures have beenextended beyond experts memory abilities to account for superior perfor-mance in general (Ericsson & Staszewski, 1989).

Ericsson and Kintsch (1995) extended the Skilled Memory Theory into

the Long-Term Working Memory (LT-WM) Theory to account for doubtsin the theorys generalizability to working memory (see Baddeley, 1990;Schneider & Detweiler, 1987). They proposed that information can be en-coded and retrieved through (1) a hierarchical organization of retrieval cuesassociated with units of encoded information (i.e., a retrieval structure, asin the top half of Fig. 1); (2) a knowledge-based, elaborated structure thatpermits the units of encoded information to be associated to other items inLTM or to the context (i.e., schemas and patterns, as in the lower half ofFig. 1); or (3) both. Schemas and patterns play a key role in the LT-WMTheory. Unfortunately, Ericsson and Kintsch failed to define these terms.Gobet (1998), however, maintained that their usage seems compatible withthe following definitions: A schema is a memory structure that is made bothof fixed patterns and of slots where variable patterns may be stored; a patternis a configuration of parts into a coherent structure (see also Bartlett, 1932;Piaget, 1967; Rumelhart, 1980; Thorndyke & Yekovich, 1980; Alba &

Hasher, 1983).According to Ericsson and Kintsch, the demands a given activity makes

on working memory dictate the type of encoding used so as to attain reliableand rapid storage of and access to the information in LT-WM. For example,a musician who is performing a composition from memory will rely almostentirely on a hierarchically organized set of preformed retrieval cues to en-sure that encoded information is retrieved reliably and efficiently. This re-trieval structure will develop throughout the course of extensive practice onthe piece. Conversely, a performer who is sight reading or improvising musicwill continually draw from previous experience and the surrounding contextbut will not be able to rely on a set of preformed retrieval cues since themusic has not been rehearsed prior to performance. Finally, one performingfrom a notated source will rely on a combination of cues, previous experi-ence, and information drawn from the surrounding content (see RelatedResearch in Music Cognition below for further discussion).

In sum, the LT-WM Theory is consistent with the notion that superiormemory performance is domain-specific and contends that the proposed in-crease in working memory is limited to an individuals respective domainof expertise. Also, the acquired nature of LT-WM implies that differencesmay exist between tasks and that individual differences in the implementa-tion of LT-WM for a given task may potentially emerge (Ericsson &Kintsch, 1995, p. 220). Using their theoretical account, Ericsson and Kintschexplain the empirical findings of studies on digit span memory, memory formenu orders, mental multiplication, mental abacus calculation, chess, medi-cal expertise, and text comprehension.


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FIG. 1. Two different types of encoding of information stored in LT-WM. The top ofthe figure shows the hierarchical organization of retrieval cues associated with units of encodedinformation. The bottom of the figure shows the knowledge-based associations relating units of

encoded information to each other along with patterns and schemas establishing an integratedmemory representation of the presented information in LTM (adapted from Ericsson &Kintsch, 1995).


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Two questions, however, should be addressed before aspects of the LT-WM Theory, or any theory of general expertise for that matter, can be exam-ined within the domain of music. First, is musical expertise comparable toexpertise in other domains? Though the nature of performance in music may

differ fundamentally from that of other domainssuch as chess, closed sportskills (e.g., figure skating, ballet, and gymnastics), and open sport skills (e.g.,tennis, basketball, and field hockey)skilled musicians share some of thesame demands on skill and memory as other experts. Memory, for instance,plays an important role in closed sports because individuals must memorizesequences of movements that constitute a given performance and performthose movements with reference to defined, technical standards. Since tech-nical skills are judged according to ideal forms, performers must make on-line comparisons between actual and ideal techniques. The bases forthese on-line comparisons are drawn from LTM, acquired over many yearsof training (Allard & Starkes, 1991). Concert soloists, like experts of closedsport skills, recall series of actions that constitute a performance; refer todefined, technical standards expected by audiences and judges; and makeon-line comparisons to gauge the quality of their actual performance.

The second question is: Do performing musicians accumulate a vast, do-

main-specific knowledge base, acquired through extensive practice? Youngconcert artists often have 1015 concerti and six or more recital programsready to perform by the start of their professional careers (Chaffin & Imreh,1994). This enormous amount of prepared materialnot to mention theirknowledge of such entities as scales, arpeggios, and etudesdemonstratespossession of a vast knowledge base within the music domain. Also, the10,000 h of deliberate practice, indicated by Ericsson, Krampe, and Tesch-Romer (1993) as a prerequisite to reaching expert levels of musical compe-tence, certainly qualifies as extensive practice (see Williamon & Valentine,2000, for further discussion).

A multitude of additional issues must be addressed before these theoriescan be viewed as accurate explanations of the cognitive processes governingmusical expertise. For example, do musicians index and rapidly categorizemusical information into meaningful units (i.e., chunks)? Do they encodeand retrieve information using retrieval structures and schemas? If so, are

these structures organized according to hierarchical and serial principles?Existing research on musical skill and performance offers insight into thesequestions.

RELATED RESEARCH IN MUSIC COGNITION

Evidence for Chunking in Music

Halpern and Bower (1982) provide evidence that musicians do, indeed,index and rapidly categorize musical information into meaningful chunks.


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They visually presented slides of good, bad, and random melodiesto musicians and nonmusicians for 5 s each. Musicians performance on the5-s written recall task worsened from good to bad to random melodies; how-ever, they recalled significantly more notes than nonmusicians in all three

categories. These findings parallel results from other studies in that highlytrained individuals recalled more of the presented stimuli than those whowere untrained and their ability to perform this task diminished most forrandom stimuli (see de Groot 1946/1978; Chase & Simon 1973a, 1973b;Allard et al., 1980; Starkes et al., 1987). They also reveal that musicianscan, as other experts, encode task-relevant information rapidly and accu-rately, thereby suggesting that the cognitive processes governing skilled mu-sical performance resemble, in some respects, cognitive processes in otherperformance domains. The results, however, contradict those of previousstudies (e.g., Chase & Simon, 1973a, 1973b) in that musicians were signifi-cantly better than nonmusicians at remembering randomly presented notes.Halpern and Bower explained this inconsistency by proposing that musi-cians have had 10 or more years of perceptual differentiation training withnotes as well as note names to aid their encoding and recall (p. 39). Theyalso stressed that almost any sequence or combination of notes may be

judged as good music by some listeners (p. 33).

Evidence for Retrieval Structures and Schemas in Music

At first glance, the nature of the cognitive mechanisms used by skilledmusicians to encode and retrieve information is not clear from an inspectionof existing research. This ambiguity rests on differences in the terminologyused in music compared with that of other areas of skilled performance.Nevertheless, the available research provides initial evidence for the use ofretrieval mechanisms during practice and performance. In a theoretical arti-cle, Clarke (1988) suggested that skilled musicians retrieve and execute com-positions using hierarchically organized knowledge structures constructedfrom information derived from the score and projections from players stylis-tic knowledge. He provided examples of hypothetical knowledge structuresbased on a compositions formal structure. One of these postulated the for-mation and use of knowledge structures in memorized performances.

He argued that performances of classical music from memory appear tooffer the most deeply embedded generative structures (p. 3). In memorizedperformances, the generative structure is known entirely, or at least to a highlevel, in advance. This includes the highest level of global understanding(the piece as a whole) down to the lowest level (the piece as individual notes;see Fig. 2a). Therefore, it is simply unpacked during a performance (p.5). The description of such structures bears a remarkable resemblance to thatof retrieval structures by Chase and Ericsson (1982) and Ericsson andKintsch (1995). Clarke asserted that the idea of having an entire structuralrepresentation of a composition activated during performance is unlikely,


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FIG. 2. Representation of (a) a complete knowledge structure for a memorised musicalperformance, (b) a partially activated structure (ringed) in midphrase, and (c) a partially acti-vated structure (ringed) approaching a phrase boundary (adapted from Clarke, 1988).

even for a piece of only moderate length. Instead, he proposed that part ofthe structure is activated at any one time and that the active region shifts asthe performer progresses through the music. This shift occurs between re-gions of activated structure which vary in durational extent and generativedepth. As a general rule, the depth to which the generative structure isactivated is directly related to the structural significance of phrase boundarieslying close to, or at, the players current musical location (p. 5). In themiddle of a deeply embedded musical phrase, for instance, a performer mayprimarily be concerned with the detailed structure of connections within the


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phrase itself. Therefore, only a region of low-level generative connectionswould be active, rather than high-level structural information (see Fig. 2b).Conversely, at a phrase boundary, a performer may need to know how theprevious and subsequent phrases relate to one another and to the overall

structure of the piece (see Fig. 2c). At such a moment, a small area of low-level structural connections may be active, sufficient to specify the immedi-ate succession of events to be played, together with a section of the higherlevels of generative structure specifying larger-scale relationships (p. 4).

In elaborating on how knowledge structures are formed for a memorizedperformance, Clarke emphasized that generative structures can be perfectlyrepresented only when all information has become available. In the courseof assembling a representation from the raw data of notation, mistaken as-sumptions and oversights will inevitably result in a flawed structure that mustbe continually reassessed and retrospectively modified in the light of newevidence. Such reevaluations are jeopardized by memory limitations, andmay also lead to constructive rationalizations which distort the true structureof the music (p. 6).

In sum, Clarke asserted that performers retrieve and execute compositionsusing internal representations. He described the hierarchical nature of knowl-

edge structures in detail and proposed how highly developed, or possiblycomplete, knowledge structures are used during memorized performances.

Chaffin and Imreh (1997) systematically observed the practice of a concertpianist (Imreh) to determine whether she used the kind of highly practiced,hierarchical retrieval structure described above to memorize and perform thethird movement, Presto, of J. S. Bachs Italian Concerto. Practice for thispiece was divided into 58 sessions, aggregated into three learning periodsand spread over 10 months. Sessions were videotaped, and cumulative rec-ords were created showing the pianists starting and stopping points in themusicsimilar to that generated for the present study and illustrated in Fig.3. They also examined the pianists concurrent and retrospective commentaryon her practice. At the end of the 10 months, the pianist performed thePresto from memory.

Chaffin and Imreh confirmed Clarkes (1988) proposal that skilled musi-cians use hierarchical retrieval schemes to recall encoded information. More-

over, they found that the pianist organized her practice and subsequent re-trieval of the Presto according to its formal structure. In her commentaryin practice session 17 (of 58), she focused on sections of the formal structurein which the same theme was repeated. Specifically, she spent a considerableamount of time in comparing differences between the various repetitions ofthe A and B themes. She then put the two themes together, remarking thatI think I am going to work on these larger sections (p. 324). Chaffin andImreh used this and other similar comments as the basis for arguing that thepianist used the structure to guide the encoding and retrieval of the music,


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FIG. 3. A sample from the cumulative records showing the segments of the music playedtaken from one of the middle practice sessions of a Level 4 pianist while practicing the Fuguein D Minor. The x axis represents bars of the music and the y axis depicts the cumulative

number of practice segments. The beginning of each horizontal line indicates the point atwhich the pianist started playing the composition. The end of each line denotes the point inthe music at which the pianist stopped playing. Each new line, reading from bottom to top,indicates that the pianist stopped and restarted.

suggesting that in order to monitor transitions between sections con-sciously, [she] needed to retrieve a conceptual representation of the nextsection from memory as she played (p. 325).

To provide empirical support for their argument, they examined the extentto which the pianists practice was actually guided by the formal structure.They compared the number of practice segments that started and stopped atboundaries in the formal structure with the number that started and stoppedat other locations. They found that starts and stops occurred more frequentlyat structural boundaries than in the middle of sections. They used these find-ings as behavioral support for their assertion that the pianist identified theformal structure and then used it to guide the encoding and retrieval of the

piece.In a follow-up study, the pianist was asked without forewarning to write

out the first page of the score from memory 27 months after her originalperformance, during which time she had not practiced or performed the piecefor 24 months. The researchers found that accuracy of recall for notes (dura-tion was disregarded) was significantly better for the bars beginning eachsection than for bars at other locations, confirming, once again, that the hier-


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archical components of the musics formal structure formed an enduringfoundation for the pianists retrieval structure.

THE AIMS OF THIS ARTICLE

Based on the work of Clarke (1988) and Chaffin and Imreh (1997), onemay argue that musicians do form and use hierarchically organized retrievalstructures when memorizing music for performance. Indeed, Chaffin and Im-rehs research is the first to demonstrate that the principles of expert memory(see Chase & Ericsson, 1981; Ericsson & Oliver, 1989) apply to concertsoloists. Several remaining issues, however, must be addressed by subse-quent research so that clearer insight can be gained into how these cognitivemechanisms govern musical skill.

In particular, how does the formation and use of such structures changeas musicians acquire greater levels of overall competence? The concert pia-nist in Chaffin and Imrehs (1997) study used a retrieval scheme based onthe musics formal structure from the early stages of her practice. Furtherresearch must explore whether these results generalize to other performers.

Novice musicians may lack the required skills and experience to identify acompositions formal structure, in which case their inept domain-specificknowledge base would force them to exploit other retrieval schemes or pre-vent them from explicitly using any such schemes. Another important ques-tion is how do retrieval structures change across the practice process as musi-cians progressively learn a given composition for performance? AlthoughClarke asserted that performers will continually reassess and modify theirrepresentations during practice, further empirical evidence must be providedto reveal the precise ways in which these modifications occur.

This article aims to address these questions by examining the practice of22 pianistsspread across four levels of skillas they prepared an assignedcomposition for performance. First, post performance interviews are in-spected to reveal how the pianists segmented and organized their assignedcomposition during practice and performance. Characteristics of these seg-mentations are explored to address whether they were based on the formal

structure or some other division of the music and whether they were hierar-chically ordered. Second, empirical measures from the musicians practiceare explored to determine if the pianists practice was actually guided bytheir segmentation of the music and to reveal possible differences in theextent of this guidance within and between ability levels. These results arediscussed in terms of their contribution to furthering the understanding ofhow the cognitive mechanisms used in musical practice and performancedevelop as individuals strive to achieve musical expertise. Finally, the extentto which performers structured their practice according to hierarchical princi-


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ples of organization is examined in relation to the quality of their resultingfinal performances.

METHOD

Participants

Six piano teachers from southeast England were asked to recommend students capable oflearning and performing from memory a selected piece of music suited to their level of ability.Thirty-seven pianists were recruited for the study. Of those 37, a complete set of data wascollected and analyzed for 22 participants. Of the 15 pianists omitted from the analyses, 8did not follow instructions accurately, 4 felt overwhelmed by the demands of the project, 2

did not wish to participate, and 1 withdrew for other personal reasons. Participation was strictlyvoluntary but encouraged by the piano teachers because the conditions of participation (de-scribed below in Procedure) were seen to contribute to students overall musicianship byproviding invaluable and challenging performance experience.

The participants were classified into four levels of ability based on the grading system setforth by the Associated Board of the Royal Schools of Music (see Harvey, 1994). This systemcontains eight grades, with Grade 1 representing the lowest level of skill and Grade 8 represent-ing the highest. Musicians at Grade 8 are usually considered to possess high performancestandards, though falling just short of expertise. The four levels span all eight grades and were

stratified as follows: pianists of Grades 1 and 2 were placed in Level 1 (2 male and 3 female);of Grades 3 and 4 in Level 2 (3 male and 3 female); of Grades 5 and 6 in Level 3 (2 maleand 4 female); and of Grades 7 and 8 in Level 4 (5 female).

This division of the Associated Boards system was acknowledged as an acceptable stratifi-cation of ability by the six participating piano teachers, all of whom had extensive experiencein preparing musicians for Associated Board grade examinations and five of whom were,themselves, examiners for the Board. The classification system was strictly upheld, except inone instance when the pianist had never taken grade examinations. In this case, the musicianwas placed in the most appropriate level, as deemed by the piano teacher. Means and standard

deviations for general characteristics of pianists who successfully completed the study at eachlevel of ability, including age, years of formal training on the piano, length of time withcurrent piano teacher, and total number of piano teachers have been reported previously (seeWilliamon & Valentine, 2000, p. 359). It was demonstrated that the musicians within eachof these levels were sufficiently comparable in terms of overall musical competence and train-ing and that the four groups were equally representative of their intended level of skill (seepp. 362363).

The Music

The pianists were assigned one piece of music appropriate to their level of ability. Allselected pieces were composed by J. S. Bach. The compositions for Levels 1 to 4 were, respec-tively, the Polonaise in G Minor from the Anna Magdalena Notebook (BWV Anh. 119), theTwo-Part Invention in C Major (BWV 772), the Three-Part Invention in B Minor (BWV 801),and the Fugue in D Minor from the Well-Tempered Clavier I (BWV 851; Level 4 pianistsalso prepared the Prelude in D Minor, but only the results for the Fugue are reported in thisarticle). The compositions were chosen with the following three criteria in mind: (1) consis-tency of style and composer between levels of ability, (2) position within the standard piano

repertoire, and (3) relative difficulty for the respective level (see Williamon & Valentine,2000, for a discussion of how these selected works conformed to the three criteria). Generalcharacteristics of the selected compositions, including time signature, mean tempo, and the


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total number of bars and beats in each piece are listed in Table A in Appendix 1 (see Williamon,1999, for an analysis of each compositions formal structure).

Procedure

Systematic observations of practice. The pianists were asked to record all practice for theirassigned piece on cassette tape. The participants were invited to comment, either on tape orin writing, on any relevant aspect of the learning process. In addition, pianists were asked tonote and describe all practice carried out away from the piano, including singing the musicand analyzing the score. Participants were informed at the outset of the study that they wouldbe required to perform the assigned piece from memory in a recital setting, attended by theirteachers, parents, and fellow music students. The recitals were part of the students regularcurriculum. No restrictions on the amount of time or the number of practice sessions wereplaced on the pianists, except for those normally affixed by themselves or their music teachers.

Performance evaluations and postperformance interviews. The 22 recital performanceswere recorded on videotape and were evaluated by three experienced piano teachers, otherthan those whose students were participating in the study, two of whom were also experiencedexaminers for the Associated Board. Evaluations were made according to the three guidingprinciples set forth by the Associated Board: musical understanding, communicative ability,and technical proficiency. Performers were scored for each of the above performance aspectsand on overall performance quality. Performances were rated on a scale from 1 to 12, with12 as the best rating. Results presented elsewhere (see Williamon & Valentine, 2000, pp. 363364) reveal that scores for overall performance quality were significantly correlated between

these examiners (Evaluators 1 and 2: r .68, p .01; Evaluators 2 and 3: r .84, p .01;Evaluators 1 and 3: r .53, p .05; see Williamon & Valentine, 2000, p. 364, for a list of themean ratings from all three evaluators on overall performance quality, musical understanding,communicative ability, and technical proficiency).

Following each performance, the pianists were interviewed on the practice and memoriza-tion process. All interviews were recorded on cassette tape. One set of interview questionsrequired that participants indicate whether they had thought of their assigned composition ashaving component sections during both practice and performance, and if so, why and howthey partitioned it. In another set of questions, they were asked to identify the bars in which

difficult passages occurred in the music and explain why they were difficult. These were open-ended questions, not intended to lead the pianists into particular answers, such as the identifi-cation of the musics formal structure or the cataloging of difficulties into specific types.Responses to questions were, in general, one to two sentences long. In addition, participantswere asked to mark clearly their identified sections and difficult bars in two colors of ink,respectively, on a photocopy of their score.

Cumulative Records

The recorded practice sessions were transcribed into cumulative records for each pianist.Graphs were plotted for each practice session showing starting and stopping points for thesegments of music played by each pianist. Figure 3 displays a graph taken from one of themiddle practice sessions of a pianist at Level 4 practicing the Fugue in D Minor. Stutters,correcting one or two notes while continuing to play through the music, were not includedin the cumulative records. All graphs were transcribed from the cassette tapes by the firstauthor.

To establish the accuracy of these transcriptions, one expert musician, nave to the aims ofthe study and unaware of the structural boundaries indicated by each pianist, transcribed one

practice session for one pianist from each level of ability (the session was selected randomlyfrom the middle of the practice process for each pianist). This amounted to 50 min of practicetime and totaled 203 practice segments. The original transcriptions of these practice sessions


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by the first author contained 210 segments (one of the missing segments was from the practiceof the Level 1 pianist, four from Level 2, and two from Level 3). The beat on which eachsegment started and stopped (counted cumulatively from the beginning of each composition)was obtained from the original transcription and that of the independent rater. A coefficientof repeatability (see Bland & Altman, 1996) was calculated across both transcriptions for starts

and stops (with seven missing values). The resulting coefficients were, respectively, 0.34 (w0.12) and 0.38 (w 0.14), thus predicting with 95% confidence that, had all practice sessionsbeen transcribed by these two raters, their transcriptions would have deviated by no more than0.34 and 0.38 beats.

RESULTS

Segmenting the Assigned Compositions: Evidence from the Interviews

All 22 participants reported segmenting their assigned piece while learningit and using this segmentation during their memorized performances. Four-teen pianists indicated that their segmentation had been pointed out andstressed by their piano teachers (Level 1: n 4 of 5; Level 2: n 5 of 6;Level 3: n 3 of 6; Level 4: n 2 of 5). These participants were studentsof three of the six teachers. As might be expected, their divisions of themusic were highly consistent within ability levels and congruent with the

musics formal structure. However, only three of these pianists (one in Level3 and two in Level 4) reported that they were aware of this (all participantswere asked explicitly if they knew of the formal structure if they had notyet volunteered the information).

The remaining eight participants remarked that they had partitioned theirassigned composition themselves. The reasons behind these segmentationswere varied, ranging from the visual layout of the page (e.g., page breaks)to harmonic progressions (e.g., cadences). For example, one pianist in Level1, whose segmentation was not influenced by her teacher, reported that itwas based on the change in dynamics throughout the piece, salient visualaspects of the score and repeated patterns in the music. This segmentationof the Polonaise was not the result of a formal analysis of the musics har-monic or rhythmic structure, but the sections identified by this pianist weremusically sensible, coinciding exactly with the formal structure at major sec-tion boundaries. Still, not all of the pianists segmentations agreed wholly

with a formal analysis of the music. One musician at Level 3 who dividedthe Three Part Invention into five major sections commented that in gen-eral, the music seemed to resolve best at the end of bars that had fast notesin both hands. Although some sections identified by this pianist coincidedwith components of the formal structure, there were still discrepancies be-tween the formal structure and the identified segmentation.

An answer to the question of whether participants based their segmenta-tion of their composition on the formal structure is not clear-cut from theinterview data. Some of the pianists did so knowingly. Others did so unknow-ingly, and still others divided their composition by additional measures. Nev-


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ertheless, these interviews show that all pianists segmented their assignedpiece into sections that were meaningful to each of them. Like the concertsoloist in Chaffin and Imrehs (1997) study, they reported using these sec-tions to learn and recall the compositions. Therefore, the important issue is

not whether the formal structure guided the rehearsal and retrieval of musicalinformation, but whether the meaningful sections identified by each pianistguided rehearsal and retrieval. An empirical evaluation of whether this actu-ally occurred is presented below.

The interviews also provide insight into the question of whether the musi-cians segmentations were hierarchically organized. In many instances, thepianists reported several subsections within larger sections. An example froma Level 1 pianist demonstrates such hierarchy. She reported major sectionsacross bars 14, 510, and 1116 and subsections at bars 79, 1314, and1516. Similar hierarchical content was apparent in the segmentations of allpianists, especially at higher levels of ability, where the music was longerand more complex. For example, one Level 3 pianist partitioned the Three-Part Invention into four major sections that corresponded to the overall for-mal structure (bars 113, 1425, 2632, and 33 38). Within these sections,he identified a total of seven subsections (bars 13, 46, 710, 1113, 14

16, 1719, and 2025). Since the majority of pianists in Levels 1 and 2 wereinfluenced by their teachers in forming their segmentations, no between-levelcomparisons were made with regard to the use of hierarchy.

Between-level differences, however, did emerge in terms of hierarchywhen the participants were asked to identify difficult bars in the music andstate why they were difficult. The answers were varied, ranging from thosedealing with the physical execution of the piece (e.g., large intervals, fastnotes, accidentals, articulations, and tricky fingerings) to musical aspects ofperformance (e.g., bringing out the fugue subject and playing the piecestylistically correct). Regardless of specific classifications of difficulty, pi-anists in Levels 3 and 4 systematically identified difficult bars section bysection. For example, one Level 3 pianist commented that there were nodifficulties in the first section of the music. In the second section, only bar19 was tricky in getting the hands coordinated, but the third section wasmost difficult, especially bars 26, 27, and 28. It took me a while to figure

out how to play them, both musically and technically. Bar 36 in the lastsection was difficult too. In general, Level 3 and 4 pianists acknowledgedthat they were thinking of a particular section of the piece and then com-menced to name the relevant bars within that section (the mean proportionof difficult bars spontaneously mentioned, together with their segment loca-tion, to the total number of difficult bars was 0.96 for Level 3 and 1.00 forLevel 4). This was unsolicited by the interviewer and therefore suggests that,for these musicians, the identification of specific material within a composi-tion (e.g., difficult bars) was related to how that material fit into more globallevels of understanding (e.g., the major sections of their segmentations).


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Although this occurred to some extent for pianists at Levels 1 and 2, they,by and large, referred to difficult bars independently from their segmenta-tions (the mean proportion of difficult bars spontaneously mentioned, to-gether with their segment location, to the total number of difficult bars was

0.72 in Level 1 and 0.85 for Level 2). This implies that either (1) they didnot do so because the instructions for answering did not require it or (2) theidentification of local detailsuch as that required when describing difficultbarswas independent from the identification of broader, more global levelsof understanding. Behavioral data presented by Williamon (1999) lend sup-port to the second of these explanations. He showed that pianists at higherlevels in this sample interspersed short and long practice segments more thanless skilled pianists throughout the entire learning process, suggesting thatthey were more flexible at shifting focus between hierarchical levels of un-derstanding. Considering that the less skilled pianists did not intersperseshort and long practice segments as much and, hence, may have been lesspracticed at shifting focus between levels of understanding, the finding thatthey did not rely exclusively on global, hierarchical levels to evoke localinformation in their interviews is not particularly surprising.

In sum, the findings presented above reveal that participants segmented

their assigned composition into various hierarchical organizations, not al-ways coincident with the formal structure. Moreover, the data indicate thatthe more skilled musicians demonstrated an extended use of hierarchy whenreporting information about local detail in their assigned composition (i.e.,difficult bars). Nevertheless, further analyses must be performed to revealwhether empirical findings from the recorded practice actually support thepianists claims that meaningful sections in the music guided rehearsal andretrieval.

The Role of Segmentation in Practice

To determine the extent to which segmentation played a role in guidingpractice, bars of the assigned compositions were categorized as structural,difficult, or other. Unlike Chaffin and Imrehs (1997) study, the formalstructure of the music was not used as the basis of this categorization becauseonly three pianists (one in Level 3 and two in Level 4) reported that knowl-

edge of the formal structure influenced their segmentation of the assignedpiece. Instead, the categorization system was based on the pianists individ-ual-specific segmentation of the music and identification of difficult bars.

Bars were classified as structural if they were the first bar in each ofthe identified sections and subsections. They were labeled as difficult ifthey had been named as such by the pianists. No differentiation was madebetween types of difficulty. All remaining bars were placed into the othergroup. In four cases, two pianists in Level 2, one in Level 3, and one inLevel 4, labeled one bar in their composition as both structural and difficult.In these instances, the bars were omitted from subsequent analyses. Also,


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the first bar of each composition was excluded from all analyses. Obviously,the first bar of a piece may play a guiding role in any hierarchical retrievalscheme; however, it was excluded because of the multitude of reasons as towhy musicians may decide to start their practice at the beginning of a piece.

Two of these are that musical information is organized linearly and that anyattempt at a complete performance is likely to begin on the first bar. Thestructural and difficult bars identified by each pianist are listed in Table Bof Appendix 1.

Using this classification system, the frequency with which pianists startedtheir practice on structural, difficult, and other bars was obtained for eachpractice session. In order to compare these frequencies both within and be-tween ability levels a number of difficulties had first to be overcome. Interms of within-level comparisons, the number of structural, difficult, andother bars identified by each pianist varied considerably. Consequently, theresulting frequencies may have increased or decreased based on the numberof each type of bar. As for between-level comparisons, the findings of Wil-liamon and Valentine (2000) revealed that pianists at higher levels of abilityin this sample spent more time practicing in each practice session. As a result,they may have started practice on structural, difficult, and other bars more

often than pianists at lower levels of ability. Also, the number of bars ineach assigned piece was different. Therefore, in the hypothetical situationthat all bars were equally important in terms of encoding and retrieving musi-cal information, the probability of the pianists starting their practice on anyone bar would decrease with an increase in the length of the piece.

To account for these within- and between-level inconsistencies, a scorewas calculated reflecting the deviation between (1) the observed frequenciesof starts on structural, difficult, and other bars and (2) the expected frequen-cies based on the number of each type of bar identified and the number ofbars in the assigned piece. The equations by which these scores were calcu-lated were derived from that used to calculate expected frequencies in thechi-squared test (see Goodman, 1957; Kendall & Stuart, 1963). The calcu-lated valuesreferred to from hereon as s-starts for structural bars, d-starts for difficult bars, and o-starts for other barsgive an equivalentof z scores, where positive integers indicate more starts on a specific bar

type than would be expected and negative integers indicate fewer starts ona specific bar type than would be expected. The equations and calculationsused to obtain s-starts, d-starts, and o-starts for each pianist in each prac-tice session are shown in Appendix 2. The means and standard deviationsfor s-starts, d-starts, and o-starts across all practice sessions for each levelof ability are listed in the top half of Table 1.

These values for the deviation of the observed from expected frequenciesfor each bar type (i.e., s-starts, d-starts, and o-starts) were compared usinga two-factor mixed analysis of covariance (ANCOVA) with deviation as thedependent variable, bar type (i.e., structural, difficult, and other) as the


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TABLE 1Means and Standard Deviations (SD) for s-Starts, d-Starts, o-

Starts, s-Stops, d-Stops, and o-Stops for Each Level of Ability

Level 1 Level 2 Level 3 Level 4

s-starts 2.48 6.21 15.64 21.97SD 0.99 1.92 1.82 2.19

d-starts 0.13 1.12 3.39 3.33SD 0.15 0.39 0.23 0.41

o-starts 1.52 2.88 4.18 6.43SD 0.61 0.91 0.89 1.02

s-stops 0.24 1.74 2.75 3.45SD 0.14 0.32 0.22 0.28

d

-stops 2.37 1.37 0.27 0.92SD 0.35 0.63 0.32 1.26

o-stops 1.02 0.33 0.58 0.72SD 0.32 0.49 0.25 0.31

within-subjects independent variable, and level as the between-subjects inde-pendent variable. Williamon and Valentine (2000) found that pianists at the

various levels of skill in this sample differed significantly in age; therefore,age was entered as a covariate. The analysis revealed that there was a highlysignificant effect of bar type [F(2, 34) 262.25, p .001]. The values fors-starts (mean 11.58) were higher than those for d-starts (mean 1.93), which in turn were higher than those for o-starts (mean 3.75).Practice was much more likely to start on structural bars than would be ex-pected by chance, whereas it was less likely to start on difficult or other barsthan would be expected by chance. Planned comparisons revealed that s-starts were greater than d-starts [t(17) 225.16, p .001] and s-startsand d-starts combined were greater than o-starts [t(17) 373.91, p .001].The effect of level was highly significant [F(3, 17) 58.88, p .001].Overall, values increased with level. However, there was also a highly sig-nificant interaction between bar type and level [F(6, 34) 265.22, p .001]; s-starts increased as a function of level, whereas d-starts and o-starts decreased as a function of level. Planned contrasts revealed that the

difference between s-starts and

d-starts was greater for pianists at higherlevels of ability [t(17) 263.33, p .001].

Chaffin and Imreh (1997) found that their participant also stopped herpractice more frequently at structural boundaries than at other locations inthe music. Therefore, the deviations of the observed from expected stops onstructural, difficult, and other bars were calculated to determine whether thiswas the case for the pianists in this study. These calculationsreferred tofrom hereon as s-stops for structural bars, d-stops for difficult bars,and o-stops for other barswere obtained in the same manner as shownin Appendix 2. The bottom half of Table 1 lists the means and standard


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deviations for s-stops, d-stops, and o-stops over all practice sessions foreach ability level.

The corresponding ANCOVA for -stops indicated a highly significanteffect of bar type [F(2, 34) 37.03, p .001]. Values for s-stops (mean

2.05) were higher than those for d-stops (mean 0.64) and

o-stops(mean 0.66). Planned comparisons indicated that s-stops and d-stopscombined were higher than o-stops [t (17) 62.79, p .001]. There wasa significant effect of level [F(3, 17) 3.23, p .05]; the means for Levels1, 2, 3, and 4 were 1.21, 1.15, 1.02, and 1.08, respectively. The interactionbetween bar type and level was highly significant [F(6, 34) 41.35, p .001]; the values for s-stops increased as a function of level, whereas thosefor d-stops decreased and those for o-stops remained fairly constant.Planned contrasts revealed that the difference between s-stops and d-stopswas greater at higher levels of ability [t(17) 63.64, p .001] as was thecontrast between s-stops and d-stops combined with o-stops [t(17) 25.45, p .001].

Starts and Stops on Structural Bars

To explore the extent to which structural bars guided the pianists practice

throughout the learning process, s-starts and

s-stops were examined at threediscrete stages of practice and averaged within each level of ability. Stage1 included values for each pianists first three practice sessions, Stage 2 in-cluded values for the middle three practice sessions, and Stage 3 includedvalues for the last three practice sessions. Three stages, spread evenly acrossthe practice process, were chosen to provide comparable extracts from eachpianists practice. Three sessions were included in each stage to permit themaximum number of sessions per stage without exceeding the total numberof sessions elicited by any participant. The mean values for s-starts at thethree stages for each ability level are displayed in Fig. 4 and those for s-stops are shown in Fig. 5.

FIG. 4. Mean values for s-starts at Stages 1, 2, and 3 for each level of ability.


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FIG. 5. Mean values for s-stops at Stages 1, 2, and 3 for each level of ability.

A two-factor mixed ANCOVA with s-starts as the dependent variable,stage as the within-subjects independent variable, level as the between-sub-jects independent variable, and age as the covariate revealed highly signifi-cant effects of stage [F (2, 34) 346.70, p .001] and level [F(3, 17) 529.87, p .001] and the interaction between stage and level [F(6, 34)

114.90, p .001]. s-starts increased as a function of stage and level, and

the increase across the practice process was greater for higher levels of ability(see Fig. 4). The corresponding analysis for s-stops also revealed highlysignificant effects of stage [F(2, 34) 63.23, p .001] and level [F(3,17) 857.07, p .001] and the interaction between stage and level [F(6,34) 37.48, p .001]. s-stops increased as a function of stage and level,and the increase across the practice process was greater for higher levels ofability (see Fig. 5).

Starts and Stops on Difficult Bars

The analyses presented above in The Role of Segmentation in Practicereveal that difficult bars were somewhat influential in guiding practicealthough not as influential as structural bars. To explore the extent towhich difficult bars guided the pianists practice throughout the learningprocess, d-starts and d-stops were examined at the three stages of practice.

The mean values for d-starts for each ability are displayed in Fig. 6 and

those for d-stops are shown in Fig. 7. The values for d-starts were analyzedby a two-factor mixed ANCOVA with d-starts as the dependent variable,stage as the within-subjects independent variable, level as the between-sub-jects independent variable, and age as the covariate. The analyses revealedhighly significant effects of stage [F(2, 34) 64.79, p .001] and level[F (3, 17) 62.44, p .001] and the interaction between stage and level[F(6, 34) 78.21, p .001]. d-starts decreased as a function of stage andlevel, the difference between Stage 1 and Stage 2 increasing as a functionof level of ability (see Fig. 6). Similarly, the analyses for d-stops revealed


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FIG. 6. Mean values for d-starts at Stages 1, 2, and 3 for each level of ability.

highly significant effects of stage [F(2, 34) 87.37, p .001] and level[F (3, 17) 72.84, p .001] and the interaction between stage and level[F(6, 34) 73.28, p .001]. d-stops decreased as a function of stage andlevel, the difference between Stages 1 and 2 increasing as a function of levelof ability (see Fig. 7).

Structural Bars and Quality of PerformanceConsidering the extent to which the use of structural bars to guide practice

has been shown to increase as a function of skill, one might predict that thesooner musicians begin using them to guide their practice on a piece themore likely they will be to produce higher quality performances. To testthis prediction, partial correlations were obtained between (1) the calculatedvalues for s-starts and s-stops at Stages 1, 2, and 3 (see Figs. 4 and 5) and

(2) the mean ratings on overall performance quality, musical understanding,communicative ability, and technical proficiency of the pianists perfor-mances (see Williamon & Valentine, 2000, p. 364). For these analyses, corre-lation coefficients were obtained across all pianists. Possible differences in

FIG. 7. Mean values for d-stops at Stages 1, 2, and 3 for each level of ability.


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FIG. 8. Scatterplot ofs-starts in Stage 2 and mean evaluations of overall quality of perfor-

mance.

the relationship between these values and quality of performance for eachlevel of ability were controlled for by partialing out level. (Controlling forlevel of skill in this way amounts to an assumption that the relationshipbetween these variables is the same for the four levels; the small sample sizeprevented an analysis at each level, but the scatterplot shown in Fig. 8 is

suggestive of this relationship.) The resulting correlation coefficients arelisted in Table 2. The analyses indicate that s-starts was significantly corre-

TABLE 2Partial Correlations between s-Starts and s-Stops at Stages 1, 2, and 3 and

Ratings of Pianists Performances

Musical Communicative Technical

Overall quality understanding ability proficiency

s-startsStage 1 0.39 0.44* 0.45* 0.27Stage 2 0.45* 0.52* 0.46* 0.41Stage 3 0.33 0.37 0.39 0.23

s-stopsStage 1 0.21 0.32 0.26 0.13Stage 2 0.08 0.03 0.05 0.11

Stage 3 0.26 0.23 0.28 0.23

* Correlation is significant at the .05 level (two-tailed).


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lated with musical understanding and communicative ability in Stage 1 (r.44, p .05; and r .45, p .05, respectively) and overall performancequality, musical understanding, and communicative ability in Stage 2 (r.45, p .05; r .52, p .05; and r .46, p .05, respectively). No

significant correlations emerged for s-stops.

DISCUSSION

The findings presented above indicate that the use of hierarchical struc-tures to organize practice and to function as retrieval cues is related to levelof skill as follows: (1) the overall use of structural bars in starting and stop-ping practice segments increased with ability level, (2) the use of structuralbars in starting and stopping practice increased with stage of practice (fur-thermore, this increase was a function of level), and (3) the early use ofstructural bars to guide practice was correlated with quality of performance.These results suggest that the identification and use of musical structure inguiding practice is a salient characteristic of skill and becomes increasinglyso as a function of expertise. They confirm and extend the findings of Erics-son and Kintsch (1995), on the importance of retrieval structures in skilled

performance, to the musical domain. Thus, music practice appears to parallelthe development of memory structures that support the execution of skilledperformance in other domains. The present results also go beyond Chaffinand Imrehs (1994, 1997) case study of an expert by examining the acquisi-tion of skill in a group of pianists at differing levels of expertise and bydemonstrating a continuity between the memory strategies of experts andnovices.

Stage and Level

The results reveal that the pianists employed structural bars to guide theirpractice increasingly throughout the learning process (i.e., across Stages 1,2, and 3) and that this increase was greatest for those at higher levels ofability. In fact, the data suggest that the higher level pianists began usingstructural bars to guide their practice in the early stages of the learning pro-cess (Level 4 pianists elicited positive deviations from expected starts by

Stage 2 and positive deviations from expected stops from Stage 1). This mayhave occurred because the more skilled musicians were able to recognizestructural bars in those early stages and continue to use them throughout thecourse of practice.

Why did the less skilled musicians use structural bars less in the earlystages of practice? They may have needed more time to decide upon or iden-tify structural bars, possibly resulting from a lack of experience and skill atfocusing their practice in that way. Alternatively, they may have been tooencumbered with the sheer difficulty of physically executing the piece inthese stages to make efficient use of structural bars, instead depending more


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on the local note-to-note detail to guide their rehearsals than on more globallevels of understanding. Clearly, both reasons may explain why less skilledpianists employed structural bars to guide their practice less frequently thanthose at higher ability levels.

As for difficult bars, all of the recruited pianists focused less on difficultbars as practice progressed. Specifically, they started and stopped their prac-tice on these bars less frequently from Stage 1 to 3, suggesting that difficultbars became less influential in directing the course of practice as the pianistsapproached the final performance. Clearly, these results, when viewed inconjunction with those on structure, demonstrate that the influence of diffi-cult bars in directing practice was increasingly replaced by the use of struc-tural bars to guide rehearsal (i.e., d-starts and d-stops significantly de-creased across the three stages; s-starts and s-stops significantly increasedacross the three stages).

The analyses of this article go beyond the findings of existing researchby exposing between-level differences in practice on difficult bars (cf. thework of Miklaszewski, 1989, who examined a pianists first four practicesessions on Debussys Prelude Feux dArtifice). In fact, the significant maineffects of level and interactions between stage and level for both d-starts

and d-stops suggest that the difficult bars were not only less important for

those at higher levels of skill from the onset of practice but became practi-cally negligible as they drew nearer to the time of performance. Again, whenviewed in conjunction with the findings on structural bars, these results re-veal important insight into music cognition. The highly skilled pianists usedstructural bars more frequently than those at lower levels to guide their prac-tice in the early stages of the learning process and increasingly did so aspractice progressed. Hence, one may argue that, although the practice ofhigher level pianists was influenced by difficulty to some extent (d-stopswas positive in Stage 1 for pianists at all levels of ability), they were ableto set difficulty aside earlier when deciding where to start and stop theirpractice and they increasingly chose bars of structural importance whendoing so.

One may also argue that the higher level pianists started and stopped theirpractice more on structural bars and less on difficult bars because they were

working to smooth out the performance of difficult passages and integratethose passages into run-throughs of larger sections of music. Such attemptsto smooth performance are similar to those observed by Miklaszewski (1989)and can be evidenced in this study by exploring the comments made bypianists during practice. One pianist in Level 3 in his second practice session,after having spent considerable time rehearsing two difficult bars, remarkedthat he needed to practice leading into those bars. He then started thenext practice segment on one of his identified structural bars and stopped atthe beginning of another structural bar. Although such comments were madeby pianists in Levels 1 and 2, they were infrequent (only two comments of


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this type were made, one by a Level 1 pianist and one by a Level 2 pianist)and did not occur until the final stage of practice.

Implicit/Explicit Knowledge

Considering the significant findings for starting and stopping practice oncertain types of bars presented here, some discussion must be directed atinterpreting decisions to start and stop practice. Certainly, starting practiceon a specific bar may suggest intention on behalf of the musician. For exam-ple, the Level 3 pianist mentioned above started a considerable number ofpractice segments on two difficult bars so that he could acquire greater facil-ity in executing them. He then intentionally started his practice on a structuralbar so as to practice leading into the difficult bars.

Stopping practice on a particular bar, however, does not always signifyintention. Instead, stops may result from a wide range of occurrences duringpracticesuch as reflexive reactions to errors, a desire to end a practicesession, or the interruption of practice by an outside source. Still, the dataconcerning structural stops have been interpreted as being used to guide prac-tice. This is supported by two points. First, the identified structural bars didnot coincide with identifications of difficulty (in the four cases in which they

did, these bars were omitted from subsequent calculations). Second, stopson structural bars increased significantly for all pianists across the practiceprocess. Therefore, one may argue that stops on such bars were not the resultof a breakdown in the physical execution of the piece or sheer difficultybut, rather, were intentionalor at least they became more so as practiceprogressed. On the other hand, difficult bars were extremely challenging fora variety of reasons, and unlike structural bars, stops on those bars decreasedsignificantly from Stage 1 to 3 for all pianists. These findings suggest thatsuch stops resulted from a breakdown in the physical execution and thatthey, consequently, decreased in number as the pianists gradually overcamethat difficulty.

Retrieval

The assumption is that using structural boundaries as starting places pro-vides practice in their use as retrieval cues. Existing research suggests that

if individuals use a retrieval scheme during performance, their retrieval maybe enhanced if they use the same scheme to encode the information (Tulv-ing & Pearlstone, 1967; Baddeley, 1990) and specifically practice using itto guide retrieval (Ericsson & Kintsch, 1995). Considering that (1) the identi-fication of structural bars was based on the pianists reports of sections inthe music that were important in both practice and performance and (2) thesebars were increasingly exploited across the practice process, it can be arguedthat this exploitation was not only important for the encoding of musicalinformation but also for its retrieval. The findings of Williamon (1999) sup-port this argument. He showed that two expert pianists did, indeed, use hier-


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archically organized retrieval structures to guide their performances of a setpiece.

Theoretical vs Idiosyncratic Structure

The definition of structure in this study differed from that of previouswork. Chaffin and Imreh (1997) found that their concert soloist used themusics formal structure to guide her practice. The postperformance inter-views from the present study revealed that only three of the pianists reportedexplicit knowledge of the formal structure. Consequently, the participantsindividual-specific segmentations were used as the basis for identifyingstructural bars. This emergence of idiosyncratic structure supports Ericssonand Kintschs (1995) prediction that individual differences in the implemen-tation of retrieval schemes are likely to emerge in the skilled performanceof a given task and be more apparent at higher levels. Future work mightexamine the relation of idiosyncratic to formal structure (of obvious interestto musicologists) as a function of skill and contrast use of these two typesof structure as predictors of final quality of performance. Moreover, it mightalso explore the generalizability of these findings to other types of music(e.g., atonal), in which structure may not be easily distinguishable.

Pedagogical Recommendations

The pattern of results obtained indicates that the use of structural bound-aries to organize practice and form the basis of retrieval structures is inti-mately bound up with level of skill, either as cause or effect. In this article,a causal relationship has been argued in that use of such strategies resultedin better quality performances. However, Hallam (1997) has suggested, onthe basis of observations of student practice, that the causality may lie inthe opposite direction, in that ability to use a particular practice strategy maydepend on having attained a particular level of skill.

Regardless, the less skilled musicians in this study seemed less able toidentify structural bars and/or overcome the technical difficulties of cer-tain musical passages. Music teachers, therefore, could possibly assist theirstudents in achieving more efficient practice and ensure that musical informa-tion is learned thoroughly by striving to help identify musically meaningful

structural bars for their less skilled pupils, instructing them to use suchbars to guide practice and helping them to acquire the required technicalfacility to overcome difficulties. Similarly, teachers should further stress theimportance of structural bars in guiding practice to their highly skilledstudents, work with them extensively to identify why certain bars are diffi-cult, and emphasize the strategy of smoothing out performance on difficultbars by integrating them into run-throughs of larger sections of music. Nodoubt, many teachers do stress these points to their students. Still, an in-creased use of such strategies could improve the effectiveness and efficiency


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of practice. Certainly, the significant correlations between increased use ofstructural bars and higher quality performances in this article support thisnotion. Those who started their practice on structural bars in Stage 1 of thepractice process received higher ratings on musical understanding and com-

municative ability. This positive relationship persisted in Stage 2, suggestingthat the use of structural bars in practice is, indeed, an important componentof high-quality performances.

Summary and Conclusions

The data presented in this article reveal that the pianists segmented theirassigned composition into meaningful sections and reported using those sec-tions in both practice and performance. Empirical examinations of the pian-ists practice confirmed the pianists reports in that they, like the concertsoloist in Chaffin and Imrehs (1997) study, used structural bars morethan difficult and other bars to guide their practice in preparing forthe required memorized performance. Despite individual differences in thepianists identification of structure, the findings were strongest for those athigher levels of skill, increased over the practice process, and their use corre-lated with quality of performance. Therefore, the identification and continued

use of meaningful structure in practiceregardless of what that structuremay beappears to be an ability that develops with musical competence.

These findings contribute to an understanding of music cognition in gen-eral by providing evidence for the use of retrieval structures as a prominentcharacteristic of musical skill and by extending previous research to examinethis issue further at several levels of ability. Moreover, they support the argu-ments of Chase and Ericsson (1982) and Ericsson and Kintsch (1995) bydemonstrating that musical performers appear to implement hierarchical re-trieval structures in practice so that they may use them to guide retrievalduring performance.

APPENDIX 1

TABLE AGeneral Characteristics of the Assigned Compositions

Level 1 Level 2 Level 3 Level 4

Time signature 3/4 4/4 9/16 3/4Mean tempo 76 70 76 66

(beats/minute)Number of bars 16 22 38 44

(without repeats)Number of beats 48 88 114 132

(without repeats)


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TABLE BThe Structural and Difficult Bars Identified by Each Pianist

Structural bars Difficult bars

Level 11 5, 11 3, 6, 102 5, 11 3, 6, 10, 153 5, 7, 11, 13, 15 3, 6, 104 5, 11 2, 3, 6, 7, 9, 105 5, 11 3, 6, 7, 9, 10

Level 26 7, 15 11, 13, 14, 19, 217 7, 15 5, 11, 13, 14, 218 5, 7, 12, 19 11, 13, 14, 15, 219 7, 15 3, 5, 11, 13, 14, 2110 7, 15 5, 11, 13, 14, 2111 7, 15 11, 13, 14, 21

Level 312 7, 14, 20, 29 19, 26, 27, 28, 3613 14, 26, 33 11, 13, 16, 27, 2814 6, 14, 29, 35 26, 27, 2815 14, 26, 33 13, 27, 28, 3616 14, 26, 33 13, 16, 27, 28, 3717 4, 7, 11, 14, 17, 20, 26, 33 27, 28, 32

Level 418 21 9, 10, 11, 36, 37, 38, 4319 6, 13, 17, 25, 33, 36, 39 21, 28, 30, 31, 3220 21 9, 15, 16, 25, 2621 13, 21, 30, 36 15, 16, 37, 38, 4222 6, 17, 21, 36 16, 28, 40, 42

Note. The first bar of each piece and all bars labeled as both structural

and difficult were excluded.


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APPENDIX 2

The Equations and Calculations Used to Obtain the Values of Deviation of

the Observed from Expected Starts on Structural, Difficult, and Other

Bars (s-Starts, d-Starts, and o-Starts, Respectively)

EquationsMeasure of deviation of observed structural starts from expected structural starts

s-starts fs es

es

Measure of deviation of observed difficult starts from expected difficult starts

d-starts fd ed

ed

Measure of deviation of observed other starts from expected other starts

o-starts fo eo

eo

CalculationsStep 1: The proportion of structural, difficult, and other bars to the total num-

ber of bars

nsi

number of structural bars identified by pianist i ndi number of difficult bars identified by pianist i noi number of other bars identified by pianist iNi the total number of bars (nsi ndi noi )The proportion of structural, difficult and other bars to the total number of bars:

psi nsi

Ni(proportion structural)

pdi ndi

Ni(proportion difficult)

poi noi

Ni(proportion other)

Step 2: The number of actual starts on structural, difficult and other bars

fsi number of observed starts on structural bars for pianist i fdi number of observed starts on difficult bars for pianist i foi number of observed starts on other bars for pianist iMi number of total starts (fsi fdi foi )

Step 3: The number of expected starts on structural, difficult, and other bars

esi psi Mi (number of expected structural starts)edi pdi Mi (number of expected difficult starts)eoi poi Mi (number of expected other starts)


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