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date: 27 June 2022

Music Performancefree

Music Performancefree

  • Peter PfordresherPeter PfordresherDeparment of Psychology, University at Buffalo, State University of New York

Summary

Music performance involves precise motor control that is coordinated with higher order planning to convey complex structural information. In addition, music performance usually involves motor tasks that are not learned spontaneously (as in the use of the vocal apparatus), the reproduction of preestablished sequences (notated or from memory), and synchronized joint performance with one or more other musicians. Music performance also relies on a rich repertoire of musical knowledge that can be used for purposes of expressive variation and improvisation. As such, the study of music performance provides a way to explore learning, motor control, memory, and interpersonal coordination in the context of a real-world behavior. Music performance skills vary considerably in the population and reflect interactions between genetic predispositions and the effect of intensive practice. At the same time, research suggests that most individuals have the capacity to perform music through singing or learning an instrument, and in this sense music performance taps into a universal human propensity for communication and coordination with conspecifics.

Subjects

  • Psychology and Other Disciplines

Introduction

Music performance involves all the processes used in the communication of musical content. Performance can involve the manipulation of an external musical instrument or use of the vocal apparatus in singing. The role of the performer goes beyond serving as a conduit for a composer’s intentions; a good performance features nuances that express a unique interpretation of the performance (Kendall & Carterette, 1990). These nuances involve subtle deviations from a standard performance (in classical music this is typically the notation) that can include alterations to timing, dynamics, timbre, and slight changes to pitch. In other instances, music is composed spontaneously, during improvisation.

There are considerable challenges associated with music performance. The most obvious are that performing a musical instrument involves learning novel forms of motor coordination and the ability to execute action sequences using the instrument that are rapid and precise with respect to timing and pitch sequencing. For this reason, virtuoso performers are typically revered. Although singing involves the use of a motor system used for speech, the precision involved in pitch production for singing outstrips precision in speech (Zatorre & Baum, 2012). The demands of performance go beyond motor control, however. Performers learn hundreds of pieces, often performed from memory, and must be able to start a performance at varying positions within a piece. Most performances involve coordination among musicians, and information that facilitates coordination (e.g., seeing the other performer) can be incomplete or absent. Finally, performing in public can lead to performance anxiety, which may have debilitating effects on a performer and is difficult to treat (Kenny, 2011).

Empirical research on music performance dates back over a century in Europe and in the United States (Gabrielsson, 1999), with the first lab devoted primarily to the study of performance being founded at the University of Iowa (Seashore, 1938). However, a considerable increase in research on performance within the domain of music psychology began when technological developments reduced the considerable challenge of collecting and analyzing the daunting amount of data from even a single music performance. For instance, a review of articles from the leading music psychology journal Music Perception found that in 1984 (the first year surveyed), articles on performance were considerably lower in proportion (approximately 15%) than articles on either melody perception (approximately 30%) or pitch perception (approximately 35%), both of which may be considered in the broad category of “perception” (Tirovolas & Levitin, 2011). By 2008 (the last year surveyed), the relationship reversed, with performance studies occupying a larger proportion than these other areas.

This review summarizes psychological research on music performance, starting with studies of different levels of expertise (including the question of musical “talent” and deficits), the process and effects of music learning and the role of memory, the interplay between perception and action in performance, the use of expression in music performance, and coordination among performers in duets and ensembles. Most existing research concerns performance on the piano or by singing, but where possible this article considers research using other instruments. The focus here is primarily on behavioral studies of music based on Western European conventions, which constitutes the bulk of research to date, although studies that address cultural differences are on the rise (e.g., Fabian et al., 2014; Jacoby et al., 2019).

Individual Differences in Performance Ability

Individuals range widely in their ability to perform music on an instrument or by singing, with some performers achieving almost unimaginable levels of virtuosity and others exhibiting deficient performance. Likewise, whereas some individuals appear to make great strides in music learning with little effort, others appear to toil for years while advancing only negligibly. These observations have perplexed scientists for millennia, as is the case for other abilities associated with wide-ranging cognitive (e.g., chess) and motor (e.g., golfing) abilities.

As for other abilities, explanations of individual differences in music ability have ranged from those that emphasize genetic determinants to those that emphasize environmental factors (the so-called “nature versus nurture” debate). The environmentalist (“nurture”) side was dominant for quite some time, as articulated in the theory that accumulated deliberate practice accounts for most of the variability across individuals (Ericsson et al., 1993; Ericsson & Lehmann, 1996). Deliberate practice is oriented toward improvement of performance that requires considerable use of resources, devotion of time, and self-motivation. These results led to the strong claim that musical talent (as an inherited trait) may be a “myth” (Howe et al., 1998). It is true that practice can have considerable effects on performance as well as neuroanatomy (see section Learning and Memory).

More recent research suggests that these claims about the role of practice may be overstated. A meta-analysis of research concerning various forms of expert performance, including music, found that deliberate practice only accounted for 21% of the variance in musical abilities (Macnamara et al., 2014). A twin study that compared rates of practice with performance on a music ability test found high heritability for both traits, with practice not accounting for performance on the ability test outside of genetic influences (Mosing et al., 2014). These findings have led to models of expertise that emphasize the interaction between genetics and environmental influence (Furuya, 2018; Ullén et al., 2016). For the purposes of the present article, it is worth noting that musical ability tests focus almost exclusively on measures of perception (for a review, see Zentner & Gingras, 2019), whereas genetic studies of performance accuracy are comparatively rarer (Tan et al., 2014). Personality traits also play a considerable role in one’s tendency to maintain practice habits, and these traits likely have a strong genetic basis. Individuals who carry on with music lessons were found to score high on openness to experience in the big-5 personality inventory (Corrigall et al., 2013).

A particularly compelling puzzle concerns the origin of musical prodigies: children who achieve levels of performance comparable to virtuoso adult performers. Whereas most research in this area has focused on individual cases (for a comprehensive review, see McPherson, 2016), a recent study compared a sample of 19 prodigies, now young adults, to age-matched musicians who started training early in life (similar to prodigies) or later in life (Marion-St-Onge et al., 2020). The amount of time spent in deliberate practice was a distinctive characteristic of prodigies, but only when deliberate practice occurred in the early years of training. But practice alone did not predict which musicians were prodigies. The other distinctive characteristic was the source of one’s motivation, with prodigies expressing significantly more external motivation (i.e., desire to attain rewarding outcomes) than nonprodigies. These results were seen as consistent with models based on gene–environment interactions.

In addition to expert performance, considerable attention has been paid to individuals on the lower end of the continuum who exhibit musical deficits. One of the most active areas of research in music cognition has to do with the study of congenital amusia, colloquially referred to as “tone deafness.” Although congenital amusia is diagnosed based on a battery of perceptual tasks (Vuvan et al., 2018), the first detection of this deficit is often through singing, given that congenital amusics are usually (though not always) unable to sing with accurate pitch (Dalla Bella et al., 2009; Hutchins et al., 2010). The understanding of what causes congenital amusia has evolved since its first discovery; at present it is understood to be based on a disruption of explicit awareness for pitch, potentially reflecting poor neural connectivity (Peretz, 2016). Although poor singing is often associated with congenital amusia, many poor singers exhibit no apparent perceptual deficit and may be said to have a deficit of vocal pitch imitation (Pfordresher & Larrouy-Maestri, 2015). Most cases of poor-pitch singing appear to reflect an inability to map perceptual representations onto motor plans, potentially reflecting failed connection between auditory and motor regions of the brain (Loui et al., 2009) that are specific to the vocal motor system (Hutchins & Peretz, 2012). Online audio examples illustrate the imitation of a target melody(Audio Example 1) by an accurate singer( Audio Example 2) and a characteristic poor-pitch singer (Audio Example 3) (from Pfordresher & Brown, 2007).1 An enduring question for the study of performance deficits, as for the study of experts, is the degree to which poor singing reflects a fixed genetic trait or an environmentally based phenotype. Although existing evidence suggests that pitch matching ability has some genetic basis (Park et al., 2013), recent evidence also suggests that pitch accuracy can improve with training (Berglin et al., in press; Blanco et al., 2021; Demorest et al., 2018).

Audio Example 1. Target.

Audio Example 2: Accurate.

Audio Example 3. Inaccurate.

Learning and Memory

Learning and memory play critical and mutually beneficial roles in music performance. The process of learning occurs at both large time scales (development from novice to expert performers) and at small time scales (learning a specific piece). Here I focus on the latter use of learning, with the section “Individual Differences in Performance Ability” focusing on the former. Learning facilitates the ability to perform from long-term memory, when needed, and to economize the use of working memory. Consolidation of the effects of learning in memory promotes the efficiency of later learning.

Different strategies may promote effective learning of a musical piece. Expert performers ultimately use a variety of strategies to master complex pieces (Chaffin et al., 2005). Evidence from qualitative observations and controlled experimentation suggests that the most efficient practice occurs for strategies that require performers to play through errors and difficult parts, rather than interrupting a performance to correct errors, in order to gain a holistic perspective of the piece (Mishra, 2002, 2011). Practice also appears to benefit from social interactions and is more effective if two performers alternate in performing a piece than when one performer imitates the sequence produced by a coperformer (Schiavio et al., 2020). Learning may also benefit from sleep between practice sessions, particularly for more abstract and conceptual features of music, due to the beneficial effects of memory consolidation during REM sleep (Van Hedger et al., 2015).

Practice may have considerable neurological, physiological, and cognitive effects, leading to claims that expert performances may to a large degree reflect practice rather than genetics (see section “Individual Differences in Performance Ability” for further discussion). Long-term experience on a musical instrument enhances the representation in the motor cortex for effectors used to control that instrument (Bangert & Schlaug, 2006) and neural tracts used for efficient communication between brain regions (Halwani et al., 2011). Even short-term learning of musical sequences on a keyboard can influence neural responses to auditory events, as discussed further in the section “Role of Perception in Music Performance”. The considerable differences between “virtuoso” performers and those at lower levels likely reflects the fact that practice leads to transfer effects, which cause the performer to learn new pieces more rapidly after they learn to play a similar piece (Meyer & Palmer, 2003; Palmer & Meyer, 2000). Critically, transfer effects may be based primarily on abstract features of musical structure (patterns of pitch and time, as represented on music notation) more so than physical motor movements, with abstract features dominating more for performers with higher levels of experience.

The ultimate goal of learning is often the ability to perform error free without the use of music notation. Performers may exhibit considerable memory ability, with expert pianists performing entire concerts with no use of notation. Evidence to date suggests that these memory feats do not reflect abnormal capacity but instead reflect the development of memory strategies that are common in other domains and may interact with memory for music. For instance, performers use chunking as one strategy to reduce memory load. A study of piano performers at a recital found that the highest rated performances were for pianists who were more likely to use structural boundaries (points of closure in music, such as the end of a musical phrase) as segments during practice (Williamon & Valentine, 2002). A related study found that the best performances were associated with practicing longer sections of the piece at early stages of practice (Williamon & Valentine, 2000), similar to the advantage of “holistic” practice mentioned earlier in this section. Another advantage of using structural boundaries in practice is the formation of a content addressable memory representation, which can be accessed flexibly at various structural boundaries (Chaffin et al., 2009). This kind of representation is useful if a performer needs to stop and restart (e.g., upon making an error), and is contrasted with retrieval based on an associative chain, which can only proceed from start to finish. Performers use various types of cues to segment practice and for memory retrieval. Chaffin et al. (2009) identified three basic types: structural cues, which are based on a formal analysis of the piece (e.g., phrase boundaries); expressive cues, which reflect the performer’s interpretation of where areas of tension and relaxation exist; and basic cues, which reflect sections of a piece that are difficult for a performer to execute (e.g., difficult fingering for the piano, cf. Parncutt et al., 1997; Sloboda et al., 1998).

Of course, performance of music does not always rely on long-term memory for a specific piece and can be assisted by the use of music notation or may be improvised (made up on the spot). Long-term memory can still play a role in such circumstances, in the form of memory schemas: abstract rule systems encoded into long-term memory. Schemas facilitate the rapid processing of visual information during performance with notation; occasionally the use of schematic memory information may cause errors in music reading if notation conflicts with these schemas (Sloboda, 1976, 1985, pp. 74–81). Stroop-like interference effects were found when pianists were instructed to produce a finger sequence accompanied by notation that implied a different motor pattern (Stewart et al., 2004). Memory schemas also play a role in musical improvisation, which typically involves constrained creative exploration within a pitch/time space that is governed by stylistic schemas as well as the underlying structure of the improvised piece (Goldman, 2013; Johnson-Laird, 2002; Norgaard, 2014).

Working memory also plays a critical role in planning during performances. Performance of music occurs too rapidly for retrieval to work in an entirely serial fashion (Lashley, 1951). Instead, performers engage in incremental retrieval, whereby subsets of events from a musical piece are retrieved and maintained in working memory so that the performer can plan the execution of events while retrieving further events from the piece. This interaction of working memory with planning is supported by patterns of serial ordering errors in performance, which reflect the active use of phrase structure (Palmer & van de Sande, 1995), voicing (Palmer & Van de Sande, 1993), and meter (Palmer & Pfordresher, 2003) during planning. Working memory capacity can also be observed in sight-reading. When music notation is removed during sight-reading, pianists are able to produce several subsequent events based on predictive eye movements. The number of events producible after notation removal is referred to as the eye–hand span, and the size of the span correlates with musical experience and sight-reading skill (Sloboda, 1974). Studies of eye movements likewise suggest that better sight-readers look ahead to a greater degree than less skilled readers (Goolsby, 1994).

Significant risks may be associated with intense practice of music. In general, problematic practice occurs when individuals attempt to practice at a high rate of intensity and for long period of practice without sufficient breaks. Of particular interest to cognitive neuroscience is the rare but debilitating condition known as musician’s dystonia. Dystonia manifests as a muscle cramp but has no apparent peripheral basis and is apparently caused by the development of a maladaptive representation of motor systems in the brain caused by changes in neural organization that occur too rapidly (Altenmüller et al., 2015). Musician’s dystonia in particular is associated with intense practice early in one’s development and manifests as a cramp in the muscle systems that are significant for a specific form of music making (fingers for piano, embouchure for trumpet, etc.). Musician’s dystonia offers some caution concerning the benefits of deliberate practice.

Role of Perception in Music Performance

Perceptual processes are important for performance in many respects. This section focuses on the interplay between perception and action within the solo performer. The section “Joint Performance” elaborates on the use of perceptual information in coordinating with coperformers.

Audio Example 4.

A great deal of research on this topic focuses on how the use of online perceptual feedback contributes to a performance, particularly auditory feedback. A dominant paradigm found in the literature involves the use of altered auditory feedback. By measuring the ways in which alterations degrade performance, as well as which alterations may not degrade performance, this paradigm can address how perception and action interact online. A particularly robust effect comes from the use of delayed auditory feedback. Effects of this alteration were first identified in speech production in the 1950s (Black, 1951; Lee, 1950) and were inspired by the disruptive effects of feedback delays found in battles during World War II. The effects of delayed feedback are illustrated in an online example (Audio Example 4) with recordings of a pianist playing a Bach excerpt, first with normal auditory feedback and then with a 200-ms delay of feedback for each produced event. Later research suggests that these effects are due to competition between the rhythmic information in the auditory signal and the planned timing of movements, which occurs for the production of any rhythmic pattern including speech and music production (Finney, 1997; Finney & Warren, 2002; Howell et al., 1983; Pfordresher, 2003). Alterations of pitch content in auditory feedback may also disrupt ongoing control of vocal pitch (e.g., Hutchins & Peretz, 2013; Keough & Jones, 2009; Zarate & Zatorre, 2008), and alterations to sequential pitch information may disrupt the sequencing of pitch events in music production when the pitch alterations yield a sequential pattern that conflicts with the planned sequencing of movements (Pfordresher, 2005, 2008). These effects are found in vocal as well as piano production, although vocal production may be more sensitive than piano performance to alterations of pitch due to the demands of controlling vocal pitch (Pfordresher & Mantell, 2012); delayed auditory feedback also alters patterns of vibrato in classical singing (Deutsch & Clarkson, 1959; Shipp et al., 1988).

Although altered feedback effects are reliable and robust, it is important to note that these effects may not arise from a dependency of action planning on auditory feedback. In contrast to altered feedback effects, the removal of auditory feedback has negligible effects on the accuracy of production (Finney, 1997; Pfordresher, 2005) and only modest effects on expressive timing (Repp, 1999). Stronger effects have been found on singing intonation when feedback is masked (Ward & Burns, 1978), although these effects are not as powerful as effects of altered auditory feedback. When auditory feedback is absent, performers draw on auditory imagery and motor planning resources to accommodate (Brown & Palmer, 2013; Highben & Palmer, 2004). Instead, effects of altered auditory feedback may arise because action planning and feedback perception compete within a common hierarchical representation of musical structure (MacKay, 1987; Pfordresher, 2019).

Other studies have focused on the use of somatosensory feedback in performance. All musical instruments rely to some extent on tactile feedback. In a study that involved analyses of movement using motion capture, Goebl and Palmer (2008) found that temporal accuracy correlated with movement patterns that are likely to maximize the salience of tactile input, which may account for the fact that pianists use larger finger movements when performing at more challenging rapid tempos than they do at slower tempos (Dalla Bella & Palmer, 2011). These effects are not likely to be limited to the piano, as a similar correlation between use of tactile feedback and temporal accuracy has been found for clarinet performance (Palmer et al., 2009). Although singing does not rely on tactile feedback, somatosensory feedback from the vocal folds may play a significant role in the control of vocal pitch. In a neuroimaging study, singers who had their vocal folds anesthetized adapted to this alteration of feedback through enhanced activity in the insular cortex and increased subglottal pressure while singing (Kleber et al., 2013).

Visual feedback can play an important role in performance on many instruments, although performers are generally trained to avoid watching their own actions as they perform. An early study showed that blocking visual feedback during sight-reading during piano performance had a stronger impact on produced errors than blocking auditory feedback (Banton, 1995). A more recent study used motion capture to present visual feedback from the hand used for performance to delay visual and/or auditory feedback (Kulpa & Pfordresher, 2013). This study revealed independent effects for each alteration on performance rate, with larger effects of auditory feedback (i.e., greater slowing as a result of a delay) than visual. However, only visual feedback disrupted the precision of timing in that study. A more complex use of visual information occurs when one processes the facial expressions of a performer, which can be used to convey emotion. When singers prepare to repeat sung phrases based on an audiovisual recording, they mimic the facial expression of the singer even when doing so is incidental to their task (Thompson & Russo, 2007; Thompson et al., 2008; Livingstone et al., 2009).

The role of auditory feedback also changes with learning (see also, section “Learning and Memory”). Neural plasticity appears to play a critical role here. During initial learning, performers rapidly form associations between actions and auditory feedback. This leads to increased neural responses in motor planning areas when performers hear musical pitches (Bangert & Altenmüller, 2003; Lahav et al., 2007) and associations between pitch height and space that influence the rapidity of information processing (Lidji et al., 2007; Rusconi et al., 2006; Taylor & Witt, 2015). These associations allow rapid and efficient planning of actions and adaptive responses to alterations of feedback. Skilled performers exhibit neural responses to performance errors in advance of auditory feedback, suggesting highly efficient associations between execution and anticipated outcomes (Herrojo-Ruiz et al., 2009; Maidhof et al., 2010, 2013). In certain situations, however, these associations can cause limitations that reflect the trade-offs in the sensorimotor system between flexibility and automaticity. For instance, trained pianists experience greater disruption to alterations of pitch than untrained pianists (Pfordresher, 2012), and pianists experience greater difficulty adapting to inverted mapping of pitch on a keyboard than those without training (Pfordresher & Chow, 2019; Pfordresher et al., 2021). At the same time, expert pianists are better able to ignore isolated temporal perturbations in auditory feedback than novices (van der Steen et al., 2014). Beyond these results, it is important to note that even untrained musicians exhibit sensitivity to whether spatial transitions (e.g., on a keyboard) are mapped to proportional changes in pitch height (Keller & Koch, 2008; Pfordresher et al., 2011).

Although performance of previously learned melodies may not be dependent on the presence of auditory feedback, other research suggests that auditory feedback significantly enhances the effectiveness of learning. For instance, when pianists learn melodies by simply listening to them, recall at a later performance is more accurate than when pianists learn melodies on a silent keyboard with no auditory feedback (Brown & Palmer, 2013; Brown & Penhune, 2018). This is particularly significant given that accuracy of recall is measured using motor actions. Another study varied the presence of auditory feedback at learning and at test and found that auditory feedback had a facilitating effect specific to the learning phase (Finney & Palmer, 2003).

Whereas results so far show how perceptual input influences motor production, motor actions may also influence perception. When pianists produce pitch intervals on a keyboard, transitions in space on the keyboard (left to right versus right to left) influence the perception of changes in pitch height when auditory feedback is designed to make pitch height ambiguous through the use of Shepard tones (Repp & Knoblich, 2007). Motor movements can also enhance perceptual precision for timing. When listeners tap the beat used to judge the timing of a probe tone, performance is better than when the beat is established solely through perceptual input (Manning & Schutz, 2013, 2015).

Expression in Performance

Much of the earliest work in music performance focused on expression (Gabrielsson, 1999), and this trend continued with the upsurge in this line of research in the 1980s when researchers focused primarily on expressive timing in piano performance (Clarke, 1988; Palmer, 1989; Repp, 1990; Todd, 1985). Expressive timing refers to the way in which a performer may deviate in subtle ways from a prototypical representation of musical timing, in ways that convey the performer’s interpretation of musical structure. Although this way of conceptualizing expression is limited (Doğantan-Dack, 2014), it remains standard within experimental psychology. For classical music, the form of music in which expressive timing has most often been studied and may be most relevant (Temperley, 2004), the prototypical representation is the notated score. The performer’s interpretation may or may not reflect the composer’s interpretation and may alter the meaning a listener derives from the music (Leech-Wilkinson, 2012). In this way the performer takes an active role in the creation of music (Palmer, 1989). It is important to note that expressive timing does not involve temporal deviations that disrupt the rhythm or meter of a piece. It is also important to note that expressive timing is conceptually distinct from random variability in timing that reflect noise in the motor system or difficulty in controlling movements (cf. Vorberg & Wing, 1996).

A critical unit of measurement in expressive timing is the interonset interval, or IOI. This is the elapsed time between the onsets of two successive events. An event can be considered a note or group of notes that are notated as occurring simultaneously. The timing of offsets receives little attention in the literature because they do not appear to play a strong role in the communication of rhythm.

Expressive timing is guided in part by the phrase structure of a piece of music. Performers slow down at the ends of phrases, called phrase-final lengthening, with the degree of slowing being roughly proportional to the degree of tonal closure conveyed by the phrase (Todd, 1985). Figure 1 provides an example of the association between phrase structure and expressive timing. Because expressive timing is informed by an individual’s interpretation, this general pattern of timing varies considerably across individuals (Palmer, 1989; Repp, 1990). Likewise, expressive timing varies considerably across musical styles, being most pronounced in Western art music from the Romantic period (particularly Chopin). Despite the variability found across performers, there is evidence that performers find certain standard patterns of timing more accessible to performance than others. When asked to imitate the expressive timing of another performer, pianists have an easier time imitating typical patterns of expressive timing, as opposed to a performance in which temporal deviations are distributed randomly across musical events (Clarke & Baker-Short, 1987; but see also Repp, 2000). Variations in the magnitude of these expressive deviations scales with the emotional responses given by listeners (Bhatara et al., 2011).

Figure 1. Association between phrase structure (top) and expressive timing (lower panel). Notation is from Mozart’s Sonata in A Major, K. 331, which is segmented into phrases and subphrases. Lower panel presents timing data from a representative pianist, expressed as percent deviation from mechanical timing based on notation. Two performances are shown, one in which the performer intends to play mechanically (as notated), and the other expressively. Positions where phrase-final lengthening occurs are illustrated.

Adapted with permission from Palmer (1989).

There are various explanations for phrase-final lengthening. A long-standing suggestion is that this form of expressive timing creates an analogy to physical movement and the pattern of slowing seen in behaviors like walking and running (Truislitt’s 1938 work, translated by Repp, 1993; Friberg & Sundberg, 1999). However, others have pointed out that more basic properties may play a role in expressive timing, such as the precision of time perception (Honing, 2005) and kinematic properties of human movement (Todd, 1995). Perhaps the most widely held view of phrase-final lengthening is that it is used as a way to communicate the structural boundaries of a musical piece to the listener (Palmer, 1997).

Other musical dimensions are used expressively by performers; expressive deviations of timing are the most widely studied in part because of the ease of measuring it and the fact that the piano (the most commonly studied instrument) is limited with respect to other acoustic variables (particularly pitch and timbre). In piano performance, differences in loudness (dynamics) are used expressively and have been found to covary with timing (Todd, 1995). Beyond the piano, performers use variations of timbre and pitch to convey expressive intentions (Friberg et al., 2006). Expressive variations in pitch (e.g., vibrato) may come at the expense of precise tuning in singing (Larrouy-Maestri & Morsomme, 2014).

These manipulations play a considerable role in the communication of musical emotion to the listener and have some overlap to the way emotion is communicated in speech (Juslin & Laukka, 2003). As in speech, music performers vary the rate of performance to communicate anger or happiness (faster tempo) versus sadness or tenderness (slower tempo). Distinctly musical expressive cues also exist. For instance, musicians use staccato (brief and disconnected notes) articulation to convey anger, fear and happiness, with legato (long, connected notes) or sadness and tenderness. Timing variability dissociates happiness (low variability) from other emotions.

Other deviations in timing serve different expressive intentions. For instance, voice leading involves a slight asynchrony among a set of simultaneous notes with the note corresponding to the melody occurring slightly before the others. Pianists use voice leading to highlight the melody to the listener (Palmer, 1989, 1996). The use of this feature is related to the process of auditory stream segregation, whereby slight asynchronies cause the listener to interpret the asynchronous events as belonging to a distinct auditory “gestalt” from other events in near temporal proximity (Bregman, 1990; cf. article on Perceptual Organization). Although voice leading is most obviously a phenomenon based on timing, it is likely also influenced by the greater loudness with which the melody voice is generally played on the keyboard, given the mechanical dynamics of the hammer mechanism in the piano (Goebl, 2001). Voice leading on the piano mimics patterns found in group performances (see section “Joint Performance”), including voice leading found in singing quartets (D’Amario et al., 2020).

Researchers have also addressed the role of body movement in performance. Although some movements are necessary for execution of the acoustic product (e.g., hitting piano keys), other movements are not strictly necessary for this purpose and are termed ancillary movements. These movements may help the performer and/or may play a role in the visual expressiveness of the performance conveyed to an audience. Early research into body movements addressed swaying movements of the torso during piano performance (Davidson, 1993). Research using motion capture has shown that the ancillary movements of a performer influence listeners’ emotional evaluations (Vines et al., 2006) and may even affect basic perceptual processing of variables like duration (Schutz & Lipscomb, 2007). Singers and performers of instruments with adjustable pitch use fluctuations in tuning and vibrato for expressive purposes (Larrouy-Maestri & Morsomme, 2014).

Joint Performance

Whereas earlier studies of music performance focused on solo performance, research on coordination among duos and groups of instrumentalists and singers have become increasingly common. The focus on ensembles is important for reasons of external validity and scientific interest. With respect to the former, most music performance is participatory, and it has been argued that the origins of music may be based on the social bonds made by coperformers (Savage et al., 2021). With respect to the latter, ensemble performance provides a unique way to explore highly precise coordination of time and frequency for highly complex and diverse forms of production.

Performance among individuals can be based on dyads (e.g., piano duets), small groups (e.g., string quartets), or large groups (e.g., orchestras). Coperformers may use the same or distinct instruments and may play identical or distinct parts in the musical piece, thus adopting different hierarchical roles (e.g., the distinction between the “soloist” and an “accompanist”). Although some common principles may apply across these different configurations, such diversity brings considerable complexity to the study of joint performance.

A particularly critical element of any joint performance is the ability of performers to synchronize with each other. The difficulty of doing so varies according to the complexity of the performance configuration as described above. Parts may be produced in complete synchrony, if they share the same rhythm, but more often must be synchronized to a common stable time referent, usually a prevailing beat. There are two dimensions on which synchronization is defined (Palmer, 2013). Being synchronized in phase refers to having onsets co-occurring with approximate simultaneity. Being synchronized in period refers to the maintenance of a common tempo across joint performers. Quite often phase and period synchronization go hand in hand; if one performer inappropriately speeds up the tempo relative to another performer in a duet, synchronization of phase will suffer. But phase and period can be distinguished in many cases. A musical polyrhythm is formed when two simple (e.g., isochronous) rhythms are produced at different tempos, but are synchronized in phase when those tempos lead to co-occurrences. Conversely, some jazz performers systematically lag in phase behind the prevailing beat while maintaining a synchronized tempo with coperformers (e.g., Billie Holliday; Ashley, 2002). The critical scientific question for joint performance has to do with the factors that promote synchronization of phase and/or period.

Auditory input from other performers provides arguably the most obvious source of information for maintaining synchrony. Studies of duet synchronization suggests that synchronization degrades in proportion to the availability of feedback from all performers, including oneself. Goebl and Palmer (2009) investigated the role of auditory feedback in piano duet performances by having each performer hear feedback from both parts, only from themselves, or only from the “leader” (primo) part. Synchronization accuracy degraded as the amount of available feedback was reduced. Somewhat surprisingly, no effects of synchronization were reducible to the leader/follower role; however, the part with greater note density (a more rapid rhythm) tended to lead the other part and did so more when auditory feedback was reduced. Zamm et al. (2015) addressed feedback in the case of rounds, where the repetition of one’s own part in the coperformer’s part may provide an additional source of confusion in the planning of serial order (cf. Pfordresher, 2019). Similar to Goebl and Palmer (2009), feedback was manipulated so that performers could hear both parts, or only feedback from the coperformer. Reduced feedback degraded synchronization, but only for the production of rounds. During unison performances, hearing oneself versus the other performer did not make a difference.

Visual information plays a particularly critical role in joint performance. In many situations, coperformers provide visual information through body movements, including those that are ancillary to the performance itself. In the aforementioned study by Goebl and Palmer (2009; see also Bishop & Goebl, 2018), performers used periodic head-bobbing movements to convey the beat, with movements from performers who adopted the leader role preceding the other performer. Visual information was used to compensate for deficient auditory information and was exaggerated for conditions with less auditory information. Body sway in the first violinist of a strong quartet may also anticipate movements of others, as shown using Grainger causality metrics (Chang et al., 2017). In addition, violinists may use the velocity of the bowing motion preceding the first note onset as a cue for critical synchronization on the initial note (Timmers et al., 2014). Lack of access to visual information from coperformers has been found to disrupt synchronization in piano (Kawase, 2014) and singing duets (D’Amario et al., 2018; Palmer et al., 2019). The use of body movements for coordination in a duet is illustrated in an online example (Video Example 1) comprising a motion capture recording of two pianists playing a duet while facing each other (provided by Peter Keller).

Video Example 1. Duet.

Credit: Peter Keller.

Large music ensembles often assign the role of timekeeper reference to a conductor, who guides synchronization and expressivity for the whole group without performing any musical part. In Western classical music, there is an established repertoire of movements used by a conductor to keep time, executed by periodic movements of a baton that is typically held in the dominant hand. Luck and Toiviainen (2006) analyzed the kinematics of baton movements and correlated these with the prevailing beat exhibited by the performance (as evidenced by spectral fluctuations). Results indicated that performance timing was best predicted by points of maximal deceleration in baton movements, where the baton reaches the end point of its current trajectory (e.g., the downward motion that indicates the timing of the first beat of a measure), similar to aforementioned head-bobbing motions.

A major point of interest in joint performance concerns the degree to which performers represent the intentions of other performers in a joint representation. Several studies suggest that performers do represent the intentions of coperformers, although not with the same priority as their own. Measurements of event related potentials (ERPs) during duet performances suggest that individuals respond to manipulations of auditory feedback that deviate from their own as well as their coperformer’s goals (Loehr et al., 2013). Another study used transcranial magnetic stimulation to elicit involuntary hand movements in pianists, and showed that movements associated with the coperformer could be elicited in addition to one’s own part (Novembre et al., 2012). Both of these studies suggest that the part of one’s coperformer in a duet is highly accessible and may even be integrated within one’s own performance plan. The degree to which such accessibility extends to larger performance ensembles must diminish, but the degree to which this happens is not yet known.

It is important also to note an alternative to the shared representation perspective. A recent computational model based on dynamical systems principles suggests that it may be possible to account for adaptation among duet performers based simply on mutual entrainment without any shared representation (Demos et al., 2019). Further support for a dynamical systems view comes from studies investigating the role of spontaneous tempo. Performers who spontaneously perform at a similar solo tempo exhibit closer synchronization in duet performances than other paired performers (Zamm et al., 2015). These results suggest that one’s spontaneous tempo may reflect a self-sustaining internal (endogenous) rhythm that may limit in subtle ways how well one may entrain to another performer.

Even if performers represent the intentions of their coperformers, access to one’s own part would certainly have privileged status given that performers need to maintain an internal distinction of one’s self versus other performers (Keller, 2001; Keller et al., 2016). Support for this view comes from a study in which pianists recognized fine-grained temporal properties of their own performance as opposed to those of other performers. Pianists were highly accurate at recognizing these fine details, even when other features of a performance, such as global tempo, were manipulated (Repp & Knoblich, 2004). Likewise, pianists are better at synchronizing with their own performance as opposed to the recording of another pianist playing the same piece (Keller et al., 2007). These findings are not limited to piano; individuals are also better at recognizing and imitating their own sung performances as opposed to performances by other individuals (Pfordresher & Mantell, 2014).

Joint performances may also be influenced by the social relationships formed among members. These can be influenced in part by music-specific factors. Different performers take on different roles, with these roles often establishing a hierarchy. In keeping with this, performers taking the lead part of a duet engage in movements that anticipate the second part (Goebl & Palmer, 2009), and swaying movements of the first violin in a string quartet predict movements of other players (Chang et al., 2017). Coperformance may also influence social relationships outside the context of the performance itself. For instance, tapping in synchrony increases reported liking among coperformers (Hove & Risen, 2009), and young children have been found to exhibit more prosocial behaviors toward one another after coperformance than after engaging in nonmusical interactions (Cirelli et al., 2014; Kirschner & Tomasello, 2009, 2010). Social relationships with the audience may also have some impact on performance. Low-voiced adolescent boys in a choir have been found to enhance the prominence of their vocal spectra (the “singer’s formant”; Sundberg, 1974) in the presence of female audience members around their age (Keller et al., 2017).

Conclusions

Music performance is a complex, multifaceted task that sheds light on many features of skilled performance. The performance of music involves the coordination of several cognitive, perceptual, social, and motor skills in real time with high temporal precision. The study of music performance offers an ideal system in which to test effects of learning, development, and genetics. Research to date has illustrated how performers use expressive variations to convey structural and emotional interpretation, the bases for individual differences in performance ability, changes that occur during music learning, different uses of memory in performance, and how performers use perceptual feedback from themselves and in coordinating with others. The scope of the present review focused on cognitive and behavioral studies, with passing references to the neuroscientific literature. Other limitations of scope reflect limitations in research to date and opportunities for future research. In particular, future research would do well to expand the present dominant focus on piano and singing to other instruments. In addition, future research should focus to a greater extent on performance in non–Western cultures for which group music performance is common.

Acknowledgments

This work was supported in part by NSF grant BCS-1848930. I am grateful to Peter Keller and Caroline Palmer for sharing examples, and to Chihiro Honda and Nicole Coleman for helpful comments on an earlier draft of this article.

References

  • Altenmüller, E. O., Ioannou, C. I., & Lee, A. (2015). Apollo’s curse: Neurological causes of motor impairments in musicians. In E. Altenmüller, S. Finger, & F. Boller (Eds.), Progress in Brain Research (Vol. 217, pp. 89–106). Elsevier.
  • Ashley, R. (2002). Do [[n’t]] change a hair for me: The art of jazz rubato. Music Perception, 19, 311–332.
  • Bangert, M., & Altenmüller, E. O. (2003). Mapping perception to action in piano practice: A longitudinal DC-EEG study. BMC Neuroscience, 4, 26.
  • Bangert, M., & Schlaug, G. (2006). Specialization of the specialized in features of external human brain morphology. European Journal of Neuroscience, 24, 1832–1834.
  • Banton, L. J. (1995). The role of visual and auditory feedback during the sight-reading of music. Psychology of Music, 23, 3–16.
  • Berglin, J., Pfordresher, P. Q., & Demorest, S. M. (in press). The effect of visual and auditory feedback on adult poor-pitch remediation. Psychology of Music.
  • Bhatara, A., Tirovolas, A. K., Duan, L. M., Levy, B., & Levitin, D. J. (2011). Perception of emotional expression in musical performance. Journal of Experimental Psychology: Human Perception and Performance, 37, 921–934.
  • Bishop, L., & Goebl, W. (2018). Beating time: How ensemble musicians’ cueing gestures communicate beat position and tempo. Psychology of Music, 46, 84–106
  • Black, J. W. (1951). The effect of delayed side-tone upon vocal rate and intensity. Journal of Speech and Hearing Disorders, 16, 56–60.
  • Blanco, A. D., Tassani, S., & Ramirez, R. (2021). Effects of visual and auditory feedback in violin and singing voice pitch matching tasks. Frontiers in Psychology, 12, 2578.
  • Bregman, A. S. (1990). Auditory scene analysis: The perceptual organization of sound. The MIT Press.
  • Brown, R. M., & Palmer, C. (2012). Auditory-motor learning influences auditory memory for music. Memory & Cognition, 40, 567–578.
  • Brown, R. M., & Palmer, C. (2013). Auditory and motor imagery modulate learning in music performance. Frontiers in Human Neuroscience, 7, 320.
  • Brown, R. M., & Penhune, V. B. (2018). Efficacy of auditory versus motor learning for skilled and novice performers. Journal of Cognitive Neuroscience, 30, 1657–1682.
  • Chaffin, R., Imreh, G., & Crawford, M. (2005). Practicing perfection: Memory and piano performance. Psychology Press.
  • Chaffin, R., Logan, T. R., & Begosh, K. T. (2009). Performing from memory. In S. Hallam, I. Cross, & M. Thaut (Eds.), The Oxford handbook of music psychology (pp. 352–363). Oxford University Press.
  • Chang, A., Livingstone, S. R., Bosnyak, D. J., & Trainor, L. J. (2017). Body sway reflects leadership in joint music performance. Proceedings of the National Academy of Sciences, 114, E4134–E4141.
  • Cirelli, L. K., Einarson, K. M., & Trainor, L. J. (2014). Interpersonal synchrony increases prosocial behavior in infants. Developmental Science, 17, 1003–1011.
  • Clarke, E. F. (1988). Generative principles in music performance. In J. A. Sloboda (Ed.), Generative processes in music: The psychology of performance, improvisation, and composition (pp. 1–26). Clarendon Press.
  • Clarke, E. F., & Baker-Short, C. (1987). The imitation of perceived rubato: A preliminary study. Psychology of Music, 15, 58–75.
  • Corrigall, K., Schellenberg, E. G., & Misura, N. (2013). Music training, cognition, and personality. Frontiers in Psychology, 4, 222.
  • Dalla Bella, S., Giguère, J. F., & Peretz, I. (2009). Singing in congenital amusia. Journal of the Acoustical Society of America, 126, 414–424.
  • Dalla Bella, S., & Palmer, C. (2011). Tempo effects of timing, tone intensity and finger movements in piano performance. PLoS ONE, 6, e20518.
  • D’Amario, S., Daffern, H., & Bailes, F. (2018). Synchronization in singing duo performances: The roles of visual contact and leadership instruction. Frontiers in Psychology, 9, 1208.
  • D’Amario, S., Daffern, H., & Bailes, F. (2020). A longitudinal study investigating synchronization in a singing quintet. Journal of Voice, 34, 159-e1.
  • Davidson, J. W. (1993). Visual perception of performance manner in the movements of solo musicians. Psychology of Music, 21, 103–113.
  • Demorest, S. M., Nichols, B., & Pfordresher, P. Q. (2018). The effect of focused instruction on young children’s singing accuracy. Psychology of Music, 46, 488–499.
  • Demos, A. P., Layeghi, H., Wanderley, M. M., & Palmer, C. (2019). Staying together: A bidirectional delay–coupled approach to joint action. Cognitive Science, 43, e12766.
  • Deutsch, J. A., & Clarkson, J. K. (1959). Nature of the vibrato and the control loop in singing. Nature, 183, 167–168.
  • Doğantan-Dack, M. (2014). Philosophical reflections on music performance. In D. Fabian, R. Timmers, & E. Schubert (Eds.), Expressiveness in music performance: Empirical approaches across styles and cultures. Oxford University Press.
  • Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363–406.
  • Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of maximal adaptation to task constraints. Annual Review of Psychology, 47, 273–305.
  • Fabian, D., Timmers, R., & Schubert, E. (2014). Expressiveness in music performance: Empirical approaches across styles and cultures. Oxford University Press.
  • Finney, S. A. (1997). Auditory feedback and musical keyboard performance. Music Perception, 15, 153–174.
  • Finney, S. A., & Palmer, C. (2003). Auditory feedback and memory for music performance: Sound evidence for an encoding effect. Memory & Cognition, 31, 51–64.
  • Finney, S. A., & Warren, W. H. (2002). Delayed auditory feedback and rhythmic tapping: Evidence for a critical interval shift. Perception & Psychophysics, 64, 896–908.
  • Friberg, A., Bresin, R., & Sundberg, J. (2006). Overview of the KTH rule system for musical performance. Advances in Cognitive Psychology, 2, 145–161.
  • Friberg, A., & Sundberg, J. (1999). Does music performance allude to locomotion? A model of final ritardandi derived from measurements of stopping runners. The Journal of the Acoustical Society of America, 105, 1469–1484.
  • Furuya, S. (2018). Individual differences in sensorimotor skills among musicians. Current Opinion in Behavioral Sciences, 20, 61–66.
  • Gabrielsson, A. (1999). The performance of music. In D. Deutsch (Ed.), The psychology of music (Vol. 2, pp. 501–602). Academic Press.
  • Goebl, W. (2001). Melody lead in piano performance: Expressive device or artifact? Journal of the Acoustical Society of America, 110, 563–572.
  • Goebl, W., & Palmer, C. (2008). Tactile feedback and timing accuracy in piano performance. Experimental Brain Research, 186, 471–479.
  • Goebl, W., & Palmer, C. (2009). Synchronization of timing and motion among performing musicians. Music Perception, 26, 427–438.
  • Goldman, A. (2013). Towards a cognitive–scientific research program for improvisation: Theory and an experiment. Psychomusicology: Music, Mind and Brain, 23, 210–221.
  • Goolsby, T. W. (1994). Profiles of processing: Eye movements during sightreading. Music Perception, 12, 97–123.
  • Halwani, G. F., Loui, P., Rueber, T., & Schlaug, G. (2011). Effects of practice and experience on the arcuate fasciculus: comparing singers, instrumentalists, and non-musicians. Frontiers in Psychology, 2, 156.
  • Herrojo-Ruiz, M. H., Jabusch, H. C., & Altenmüller, E. (2009). Detecting wrong notes in advance: Neuronal correlates of error monitoring in pianists. Cerebral Cortex, 19, 2625–2639.
  • Highben, Z., & Palmer, C. (2004). Effects of auditory and motor mental practice in memorized piano performance. Bulletin of the Council for Research in Music Education, 159, 58–65.
  • Hommel, B. (2015). The theory of event coding (TEC) as embodied-cognition framework. Frontiers in Psychology, 6, 1318.
  • Honing, H. (2005). Is there a perception-based alternative to kinematic models of tempo rubato? Music Perception, 23, 79–85.
  • Hove, M. J., & Risen, J. L. (2009). It’s all in the timing: Interpersonal synchrony increases affiliation. Social Cognition, 27, 949–960.
  • Howe, M. J., Davidson, J. W., & Sloboda, J. A. (1998). Innate talents: Reality or myth? Behavioral and Brain Sciences, 21, 399–407; discussion 407–342.
  • Howell, P., Powell, D. J., & Khan, I. (1983). Amplitude contour of the delayed signal and interference in delayed auditory feedback tasks. Journal of Experimental Psychology: Human Perception and Performance, 9, 772–784.
  • Hutchins, S., & Peretz, I. (2012). A frog in your throat or in your ear? Searching for the causes of poor singing. Journal of Experimental Psychology: General, 141, 76–97.
  • Hutchins, S., & Peretz, I. (2013). Vocal pitch shift in congenital amusia (pitch deafness). Brain & Language, 125, 106–117.
  • Hutchins, S., Zarate, J. M., Zatorre, R. J., & Peretz, I. (2010). An acoustical study of vocal pitch matching in congenital amusia. The Journal of the Acoustical Society of America, 127, 504–512.
  • Jacoby, N., Undurraga, E. A., McPherson, M. J., Valdés, J., Ossandón, T., & McDermott, J. H. (2019). Universal and non-universal features of musical pitch perception revealed by singing. Current Biology, 29, 3229–3243.e3212.
  • Johnson-Laird, P. N. (2002). How jazz musicians improvise. Music Perception, 19, 415–442.
  • Juslin, P. N., & Laukka, P. (2003). Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological Bulletin, 129, 770–814.
  • Kawase, S. (2014). Gazing behavior and coordination during piano duo performance. Attention, Perception, & Psychophysics, 76, 527–540.
  • Keller, P. E. (2001). Attentional resource allocation in musical ensemble performance. Psychology of Music, 29, 20–38.
  • Keller, P. E., Knoblich, G., & Repp, B. H. (2007). Pianists duet better when they play with themselves: On the possible role of action simulation in synchronization. Consciousness and Cognition, 16, 102–111.
  • Keller, P. E., & Koch, I. (2008). Action planning in sequential skills: Relations to music performance. The Quarterly Journal of Experimental Psychology, 61, 275–291.
  • Keller, P. E., König, R., & Novembre, G. (2017). Simultaneous cooperation and competition in the evolution of musical behavior: Sex-related modulations of the singer’s formant in human chorusing. Frontiers in Psychology, 8, 1559.
  • Keller, P. E., Novembre, G., & Loehr, J. D. (2016). Musical ensemble performance: Representing self, other, and joint action outcomes. In S. S. Obhi & E. S. Cross (Eds.), Shared representations: Sensorimotor foundations of social life (pp. 280–310). Cambridge University Press.
  • Kendall, R. A., & Carterette, E. C. (1990). The communication of musical expression. Music Perception, 8, 129–163.
  • Kenny, D. (2011). The psychology of music performance anxiety. Oxford University Press.
  • Keough, D., & Jones, J. A. (2009). The sensitivity of auditory-motor representations to subtle changes in auditory feedback while singing. Journal of the Acoustical Society of America, 126, 837–846.
  • Kirschner, S., & Tomasello, M. (2009). Joint drumming: Social context facilitates synchronization in preschool children. Journal of Experimental Child Psychology, 102, 299–314.
  • Kirschner, S., & Tomasello, M. (2010). Joint music making promotes prosocial behavior in 4-year-old children. Evolution and Human Behavior, 31, 354–364.
  • Kleber, B., Zeitouni, A. G., Friberg, A., & Zatorre, R. J. (2013). Experience-dependent modulation of feedback integration during singing: Role of the right anterior insula. The Journal of Neuroscience, 33, 6070–6080.
  • Kulpa, J. D., & Pfordresher, P. Q. (2013). Effects of delayed visual and auditory feedback on sequence production. Experimental Brain Research, 224, 69–77.
  • Lahav, A., Saltzman, E., & Schlaug, G. (2007). Action representation of sound: Audiomotor recognition network while listening to newly acquired actions. The Journal of Neuroscience, 27, 308–314.
  • Larrouy-Maestri, P., & Morsomme, D. (2014). Criteria and tools for objectively analysing the vocal accuracy of a popular song. Logopedics Phoniatrics Vocology, 39, 11–18.
  • Lashley, K. (1951). The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior (pp. 112–136). Wiley.
  • Lee, B. S. (1950). Effects of delayed speech feedback. Journal of the Acoustical Society of America, 22, 824–826.
  • Leech-Wilkinson, D. (2012). Compositions, scores, performances, meanings. Music Theory Online, 18(1), 1–17.
  • Lidji, P., Kolinsky, R., Lochy, A., & Morais, J. (2007). Spatial associations for musical stimuli: A piano in the head? Journal of Experimental Psychology: Human Perception and Performance, 33, 1189–1207.
  • Livingstone, S. R., Thompson, W. F., & Russo, F. A. (2009). Facial expressions and emotional singing: A study of perception and production with motion capture and electromyography. Music Perception, 26, 475–488.
  • Loehr, J. D., Kourtis, D., Vesper, C., Sebanz, N., & Knoblich, G. (2013). Monitoring individual and joint action outcomes in duet music performance. Journal of Cognitive Neuroscience, 25, 1049–1061.
  • Loui, P., Alsop, D., & Schlaug, G. (2009). Tone deafness: A new disconnection syndrome? The Journal of Neuroscience, 29, 10,215–10,220.
  • Luck, G., & Toiviainen, P. (2006). Ensemble musicians’ synchronization with conductors’ gestures: An automated feature-extraction analysis. Music Perception, 24, 189–200.
  • MacKay, D. G. (1987). The organization of perception and action: A theory for language and other cognitive skills. Springer-Verlag.
  • Macnamara, B. N., Hambrick, D. Z., & Oswald, F. L. (2014). Deliberate practice and performance in music, games, sports, education, and professions: A meta-analysis. Psychological Science, 25, 1608–1618.
  • Maidhof, C., Pitkäniemi, A., & Tervaniemi, M. (2013). Predictive error detection in pianists: A combined ERP and motion capture study. Frontiers in Human Neuroscience, 7, 587.
  • Maidhof, C., Vavatzanidis, N., Prinz, W., Rieger, M., & Koelsch, S. (2010). Processing expectancy violations during music performance and perception: An ERP study. Journal of Cognitive Neuroscience, 22, 2401–2413.
  • Manning, F., & Schutz, M. (2013). “Moving to the beat” improves timing perception. Psychonomic Bulletin & Review, 20, 1133–1139.
  • Manning, F. C., & Schutz, M. (2015). Movement enhances perceived timing in the absence of auditory feedback. Timing & Time Perception, 3, 3–12.
  • Marion-St-Onge, C., Weiss, M. W., Sharda, M., & Peretz, I. (2020). What makes musical prodigies? Frontiers in Psychology, 11, 3244.
  • McAngus Todd, N. P. (1992). The dynamics of dynamics: A model of musical expression. The Journal of the Acoustical Society of America, 91, 3540–3550.
  • McPherson, G. (Ed.). (2016). Musical prodigies: Interpretations from psychology, education, musicology, and ethnomusicology. Oxford University Press.
  • Meyer, R. K., & Palmer, C. (2003). Temporal and motor transfer in music performance. Music Perception, 21, 81–104.
  • Mishra, J. (2002). A qualitative analysis of strategies employed in efficient and inefficient memorization. Bulletin of the Council for Research in Music Education, 152, 74–86.
  • Mishra, J. (2011). Influence of strategy on memorization efficiency. Music Performance Research, 4, 60–71.
  • Mosing, M. A., Madison, G., Pedersen, N. L., Kuja-Halkola, R., & Ullén, F. (2014). Practice does not make perfect: No causal effect of music practice on music ability. Psychological Science, 25, 1795–1803.
  • Norgaard, M. (2014). How jazz musicians improvise: The central role of auditory and motor patterns. Music Perception, 31, 71–287.
  • Novembre, G., Ticini, L. F., Schütz-Bosbach, S., & Keller, P. E. (2012). Distinguishing self and other in joint action. Evidence from a musical paradigm. Cerebral Cortex, 22, 22,894–22,903.
  • Palmer, C. (1989). Mapping musical thought to musical performance. Journal of Experimental Psychology: Human Perception and Performance, 15, 331–346.
  • Palmer, C. (1996). On the assignment of structure in music performance. Music Perception, 14, 23–56.
  • Palmer, C. (1997). Music performance. Annual Review of Psychology, 48, 115–138.
  • Palmer, C. (2013). Music performance: Movement and coordination. In D. Deutsch (Ed.), The psychology of music (3rd ed., pp. 405–422). Elsevier.
  • Palmer, C., Koopmans, E., Loehr, J. D., & Carter, C. (2009). Movement-related feedback and temporal accuracy in clarinet performance. Music Perception, 26, 439–449.
  • Palmer, C., & Meyer, R. K. (2000). Conceptual and motor learning in music performance. Psychological Science, 11, 63–68.
  • Palmer, C., & Pfordresher, P. Q. (2003). Incremental planning in sequence production. Psychological Review, 110, 683–712.
  • Palmer, C., Spidle, F., Koopmans, E., & Schubert, P. (2019). Ears, heads, and eyes: When singers synchronise. Quarterly Journal of Experimental Psychology, 72, 2272–2287.
  • Palmer, C., & van de Sande, C. (1993). Units of knowledge in music performance. Journal of Experimental Psychology: Learning Memory and Cognition, 19, 457–470.
  • Palmer, C., & van de Sande, C. (1995). Range of planning in music performance. Journal of Experimental Psychology: Human Perception and Performance, 21, 947–962.
  • Park, H., Lee, S., Kim, H.-J., Ju, Y. S., Shin, J.-Y., Hong, D., von Gotthuss, M., Lee, D.-S., Park, C., Kim, J. H., Kim, B., Yoo, Y. J., & Cho, S.-I. (2013). Comprehensive genomic analysis associate UGT8 variants with musical ability in a Mongolian population. Journal of Medical Genetics, 49, 747–752.
  • Parncutt, R., Sloboda, J. A., Clarke, E. F., Raekallio, M., & Desain, P. (1997). An ergonomic model of keyboard fingering for melodic fragments. Music Perception, 14, 341–382.
  • Peretz, I. (2016). Neurobiology of congenital amusia. Trends in Cognitive Sciences, 20, 857–867.
  • Pfordresher, P. Q. (2003). Auditory feedback in music performance: Evidence for a dissociation of sequencing and timing. Journal of Experimental Psychology: Human Perception and Performance, 29, 949–964.
  • Pfordresher, P. Q. (2005). Auditory feedback in music performance: The role of melodic structure and musical skill. Journal of Experimental Psychology: Human Perception and Performance, 31, 1331–1345.
  • Pfordresher, P. Q. (2008). Auditory feedback in music performance: The role of transition-based similarity. Journal of Experimental Psychology: Human Perception and Performance, 34, 708–725.
  • Pfordresher, P. Q. (2012). Musical training and the role of auditory feedback during performance. Annals of the New York Academy of Sciences, 1252, 171–178.
  • Pfordresher, P. Q. (2019). Sound and action in music performance. Academic Press.
  • Pfordresher, P. Q., & Brown, S. (2007). Poor-pitch singing in the absence of “tone deafness.” Music Perception, 25, 95–115.
  • Pfordresher, P. Q., & Chow, K. (2019). A cost of musical training? Sensorimotor flexibility in musical sequence learning. Psychonomic Bulletin & Review, 26, 967–973.
  • Pfordresher, P. Q., Honda, C., Greenspon, E. B., & Chow, K. (2021). Generalization of novel sensorimotor associations among pianists and non-pianists: More evidence that musical training effects are constrained. Psychological Research, 85, 1932–1942.
  • Pfordresher, P. Q., Keller, P. E., Koch, I., Palmer, C., & Yildirim, E. (2011). Activation of learned action sequences by auditory feedback. Psychonomic Bulletin & Review, 18, 544–549.
  • Pfordresher, P. Q., & Larrouy-Maestri, P. (2015). On drawing a line through the spectrogram: How do we understand deficits of vocal pitch imitation? Frontiers in Human Neuroscience, 9, 271.
  • Pfordresher, P. Q., & Mantell, J. T. (2012). Effects of altered auditory feedback across effector systems: Production of melodies by keyboard and singing. Acta Psychologica, 139, 166–177.
  • Pfordresher, P. Q., & Mantell, J. T. (2014). Singing with yourself: Evidence for an inverse modeling account of poor-pitch singing. Cognitive Psychology, 70, 31–57.
  • Repp, B. H. (1990). Patterns of expressive timing in performances of a Beethoven minuet by nineteen famous pianists. Journal of the Acoustical Society of America, 88, 622–641.
  • Repp, B. H. (1993). Music as motion: A synopsis of Alexander Truslit’s (1938) Gestaltung und Bewegung in der Musik. Psychology of Music, 21, 48–72.
  • Repp, B. H. (1999). Effects of auditory feedback deprivation on expressive piano performance. Music Perception, 16, 409–438.
  • Repp, B. H. (2000). Pattern typicality and dimensional interactions in pianists’ imitation of expressive timing and dynamics. Music Perception, 18, 173–211.
  • Repp, B. H., & Knoblich, G. (2004). Perceiving action identity: How pianists recognize their own performances. Psychological Science, 15, 604–609.
  • Repp, B. H., & Knoblich, G. (2007). Action can affect auditory perception. Psychological Science, 18, 6–7.
  • Rusconi, E., Kwan, B., Giordano, B. L., Umilta, C., & Butterworth, B. (2006). Spatial representation of pitch height: The SMARC effect. Cognition, 99, 113–129.
  • Savage, P. E., Loui, P., Tarr, B., Schachner, A., Glowacki, L., Miithen, S., & Fitch, W. T. (2021). Music as a coevolved system for social bonding. Behavioral and Brain Sciences, 44, E86.
  • Schiavio, A., Stupacher, J., Parncutt, R., & Timmers, R. (2020). Learning music from each other: Synchronization, turn-taking, or imitation? Music Perception, 37, 403–422.
  • Schutz, M., & Lipscomb, S. (2007). Hearing gestures, seeing music: Vision influences perceived tone duration. Perception, 36, 888–897.
  • Seashore, C. E. (1938). Psychology of Music. Dover.
  • Shipp, T., Sundberg, J., & Doherty, E. T. (1988). The effect of delayed auditory feedback on vocal vibrato. Journal of Voice, 2, 195–199.
  • Sloboda, J. A. (1974). The eye-hand span—An approach to the study of sight reading. Psychology of Music, 2, 4–10.
  • Sloboda, J. A. (1976). The effect of item position on the likelihood of identification by inference in prose reading and music reading. Canadian Journal of Psychology, 30, 228–237.
  • Sloboda, J. A. (1985). The musical mind: The cognitive psychology of music. Oxford: Oxford University Press.
  • Sloboda, J. A., Clarke, E. F., Parncutt, R., & Raekallio, M. (1998). Determinants of finger choice in piano sight-reading. Journal of Experimental Psychology: Human Perception and Performance, 24, 185–203.
  • Stewart, L., Walsh, V., & Frith, U. (2004). Reading music modifies spatial mapping in pianists. Perception & Psychophysics, 66, 183–195.
  • Sundberg, J. (1974). Articulatory interpretation of the “singing formant.” Journal of the Acoustical Society of America, 55, 838–844.
  • Tan, Y. T., McPherson, G. E., Peretz, I., Berkovic, S. F., & Wilson, S. J. (2014). The genetic basis of music ability. Frontiers in Psychology, 5, 658.
  • Taylor, J. E., & Witt, J. K. (2015). Listening to music primes space: Pianists, but not novices, simulate heard actions. Psychological Research, 79, 175–182.
  • Temperley, D. (2004). Communicative pressure and the evolution of musical styles. Music Perception, 21, 313–337.
  • Thompson, W. F., & Russo, F. A. (2007). Facing the music. Psychological Science, 18, 754–757.
  • Thompson, W. F., Russo, F., & Quinto, L. (2008). Audio-visual integration of emotional cues in song. Cognition & Emotion, 22, 1457–1470.
  • Timmers, R., Endo, S., Bradbury, A., & Wing, A. M. (2014). Synchronization and leadership in string quartet performance: A case study of auditory and visual cues. Frontiers in Psychology, 5, 645.
  • Tirovolas, A. K., & Levitin, D. J. (2011). Music perception and cognition research from 1983 to 2010: A categorical and bibliometric analysis of empirical articles in music perception. Music Perception, 29, 23–36.
  • Todd, N. (1985). A model of expressive timing in tonal music. Music Perception, 3, 33–57.
  • Todd, N. (1995). The kinematics of musical expression. The Journal of the Acoustical Society of America, 97, 1940–1949.
  • Ullén, F., Hambrick, D. Z., & Mosing, M. A. (2016). Rethinking expertise: A multifactorial gene–environment interaction model of expert performance. Psychological Bulletin, 142, 427.
  • van der Steen, M. C., Molendijk, E. B. D., Altenmüller, E., & Furuya, S. (2014). Expert pianists do not listen: The expertise-dependent influence of temporal perturbation on the production of sequential movements. Neuroscience, 269, 290–298.
  • Van Hedger, S. C., Hogstrom, A., Palmer, C., & Nusbaum, H. C. (2015). Sleep consolidation of musical competence. Music Perception, 33, 163–178.
  • Vines, B. W., Krumhansl, C. L., Wanderley, M. M., & Levitin, D. J. (2006). Cross-modal interactions in the perception of musical performance. Cognition, 101, 80–113.
  • Vorberg, D., & Wing, A. (1996). Modeling variability and dependence in timing. In H. Heuer & S. W. Keel (Eds.), Handbook of perception and action (Vol. 2, pp. 181–262). Academic Press.
  • Vuvan, D. T., Paquette, S., Mignault Goulet, G., Royal, I., Felezeu, M., & Peretz, I. (2018). The Montreal protocol for identification of Amusia. Behavior Research Methods, 50, 662–672.
  • Ward, W. D., & Burns, E. M. (1978). Singing without auditory feedback. Journal of Research in Singing, 1, 24–44.
  • Williamon, A., & Valentine, E. (2000). Quantity and quality of musical practice as predictors of performance quality. British Journal of Psychology, 91, 353–376.
  • Williamon, A., & Valentine, E. (2002). The role of retrieval structures in memorizing music. Cognitive Psychology, 44, 1–32.
  • Zamm, A., Pfordresher, P. Q., & Palmer, C. (2015). Temporal coordination in joint music performance: Effects of endogenous rhythms and auditory feedback. Experimental Brain Research, 233, 607–615.
  • Zamm, A., Wellman, C., & Palmer, C. (2016). Endogenous rhythms influence interpersonal synchrony. Journal of Experimental Psychology: Human Perception and Performance, 42, 611–616.
  • Zarate, J. M., & Zatorre, R. J. (2008). Experience-dependent neural substrates involved in vocal pitch regulation during singing. NeuroImage, 40, 1871–1887.
  • Zatorre, R. J., & Baum, S. R. (2012). Musical melody and speech intonation: Singing a different tune. PLoS Biology, 10, e1001372.
  • Zentner, M., & Gingras, B. (2019). The assessment of musical ability and its determinants. In P. J. Rentfrow & D. J. Levitin (Eds.), Foundations in music psychology: Theory and research (pp. 565–608). MIT Press.

Notes

  • 1. Vocal timbres of singers have been altered to maintain anonymity.