The ability to discriminate among individuals is crucial in species, such as humans,
that place a premium on kin recognition (Tang-Martinez, 2001). Identity cues used
by humans comprise not only visual cues, including relatively static cues such as
facial features (Carey, 1992) or dynamic displays such as gait and walking (Blake
and Shiffrar, 2007), but also auditory cues such as voices (Belin et al., 2004), clapping
patterns (Repp, 1987), or even tones which follow temporal patterns similar to clapping
(Flach et al., 2004).
Cues to individuality can also be communicated efficiently through music. Indeed,
along with emotion and structural cues, artistic individuality seems to be a key element
conveyed in music performance. Over the last few decades, a growing body of research
has examined issues related to individuality in musical performance (e.g., Repp, 1992;
see Sloboda, 2000 for a review). Yet, the means by which individuality is musically
expressed and perceived have remained poorly elucidated until recently. Hence, the
aim of this Research Topic is to provide a forum for interdisciplinary research broadly
centered on individuality and individual differences in music performance. This goal
was successfully achieved, and the 14 contributed articles illustrate the depth and
breadth of the topic, with themes ranging from personality correlates of flow proneness
among pianists to unique “fingerprints” in the singing voice.
Setting the tone for the Research Topic, Wöllner (2013) emphasized in an opinion piece
the importance of using averaged features, representing the mean of a large sample
of performances by different performers, rather than computer-generated “deadpan”
reproductions as the baseline for quantifying individuality in music performance.
On a related issue, Farbood and Upham (2013) compared listener judgments of musical
tension obtained for a recording of a Schubert song and its computer-generated harmonic
reduction, showing that differences in perceived tension changes between the two excerpts
highlighted interpretive choices in performance.
Historically, a substantial body of music performance research has focused on piano
performance (see Gabrielsson, 2003 for a review), and this trend was maintained here.
Van Vugt et al. (2013) explored the individuality associated with small but systematic
temporal deviations in musical scales played by pianists, showing that although human
listeners were not able to distinguish these “temporal fingerprints” by ear, high
accuracy rates were obtained by classifiers. Bernays and Traube (2014) investigated
individuality in pianists' performance of timbral nuances, and their analysis revealed
that pianists exhibited unique profiles associated with different sonorities, while
at the same time displaying common patterns of dynamics and articulation for each
timbral color. Marin and Bhattacharya (2013) identified emotional intelligence and
amount of daily practice as predictors of individual differences in proneness for
flow among pianists, but did not observe a correlation between flow and high achievement
in piano performance. Their study was the object of a commentary by Srinivasan and
Gingras (2014) exploring the putative role of control and attention in flow states
in music performance.
Two articles focused on the harpsichord, another keyboard instrument that, unlike
the piano, has been relatively neglected so far in music performance research. Gingras
et al. (2013) invited harpsichordists to record three different pieces and identified
global markers of individuality, such as performers consistently using a more detached
articulation across all three pieces, as well as associations between the note-by-note
expressive profiles of different performers that subsisted across pieces or expressive
parameters. In a follow-up to an earlier study on organ performance (Gingras et al.,
2011), Koren and Gingras (2014) investigated whether listeners could reliably identify
harpsichordists playing short excerpts from two different pieces. They found that
musicians were more accurate than non-musicians, and only musicians performed above
chance when matching the two different pieces to the same performer.
Voice production and perception was a major area of interest, with five contributions.
Hutchins and Moreno (2013) proposed a new model to account for the variability between
vocal perception and performance abilities in the general population. Their Linked
Dual Representation model, which posits that vocal information can be encoded either
as a symbolic or as a motoric representation, leads to a series of intriguing predictions
about speech imitation, singing, and response timing. In a similar vein, Yang et al.
(2013) investigated the coexistence of perceptual pitch deficits with pitch production
deficits in music and in Mandarin speech in both amusics and tone agnosics, and their
results suggest that the perception-production relationship for pitch among individuals
may be domain-dependent. Trehub et al. (2013) confirmed the presence of individual
cross-modal signatures in maternal speech and singing which can be discerned by both
adults and infants, enabling listeners to successfully link recordings of unfamiliar
speaking or singing voices to silent videos of the talkers or singers. Two articles
focused more specifically on emotional singing: Quinto et al. (2014) examined the
use of facial movements to communicate emotion, confirming the central role of facial
expressions in vocal emotional communication while at the same time highlighting individual
differences between singers, while Livingstone et al. (2014) analyzed the influence
of vocal training and acting experience on the perception of vocal quality and emotional
genuineness. They reported that acting experience was associated both with a decrease
in voice quality and with an increase in perceived genuineness.
Finally, two studies addressed applied research topics related to individual differences
in music performance. Williamon et al. (2014) designed and tested two simulated performance
environments to help performers cope with issues related to performance anxiety, and
discussed potential implications for performance training. Fritz et al. (2013) showed
that participants' mood during exercise machine workout is enhanced more strongly
with individualized musical feedback modulated by the participants' movements than
with passive music listening.
In summary, this Research Topic both confirms and extends earlier findings, while
at the same time opening up new avenues of research, especially in keyboard and voice
performance. More generally, it highlights the cross-fertilizing potential of applying
a multidisciplinary approach to the study of individuality in music performance, emphasizing
the importance of fostering collaborations among musicologists, computer scientists,
psychologists, neuroscientists, and the performers themselves.
Conflict of interest statement
The author declares that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.