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Learning to Play the Violin: Motor Control by Freezing, Not Freeing Degrees of Freedom

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Playing a violin requires precise patterns of limb coordination that are acquired over years of practice. In the present study, the authors investigated how motion at proximal arm joints influenced the precision of bow movements in novice learners and experts. The authors evaluated the performances of 11 children (4-12 years old), 3 beginning-to-advanced level adult players, and 2 adult concert violinists, using a musical work that all had mastered as their first violin piece. The authors found that learning to play the violin was not associated with a release or freeing of joint degrees of freedom. Instead, learning was characterized by an experience-dependent suppression of sagittal shoulder motion, as documented by an observed reduction in joint angular amplitude. This reduction in the amplitude of shoulder flexion-extension correlated highly with a decrease of bow-movement variability. The remaining mechanical degrees of freedom at the elbow and shoulder showed patterns of neither suppression nor freeing. Only violinists with more than 700 practice hr achieved sagittal shoulder range of motion comparable to experts. The findings imply that restricting joint amplitude at selected joint degrees of freedom, while leaving other degrees of freedom unconstrained, constitutes an appropriate strategy for learning complex, high-precision motor patterns in children and adults. The findings also highlight that mastering even seemingly simple bowing movements constitutes a prolonged learning process.
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Learning to Play the Violin: Motor Control by Freezing, Not
Freeing Degrees of Freedom
Jürgen Konczak1, Heidi vander Velden1, Lukas Jaeger1,2
1Human Sensorimotor Control Lab, School of Kinesiology, University of Minnesota, Minneapolis. 2Department of Biology,
Eidgenössische Technische Hochschule Zürich, Switzerland.
ABSTRACT. Playing a violin requires precise patterns of limb
coordination that are acquired over years of practice. In the present
study, the authors investigated how motion at proximal arm joints
influenced the precision of bow movements in novice learners and
experts. The authors evaluated the performances of 11 children
(4–12 years old), 3 beginning-to-advanced level adult players,
and 2 adult concert violinists, using a musical work that all had
mastered as their first violin piece. The authors found that learning
to play the violin was not associated with a release or freeing of
joint degrees of freedom. Instead, learning was characterized by an
experience-dependent suppression of sagittal shoulder motion, as
documented by an observed reduction in joint angular amplitude.
This reduction in the amplitude of shoulder flexion–extension cor-
related highly with a decrease of bow-movement variability. The
remaining mechanical degrees of freedom at the elbow and shoul-
der showed patterns of neither suppression nor freeing. Only vio-
linists with more than 700 practice hr achieved sagittal shoulder
range of motion comparable to experts. The findings imply that
restricting joint amplitude at selected joint degrees of freedom,
while leaving other degrees of freedom unconstrained, constitutes
an appropriate strategy for learning complex, high-precision motor
patterns in children and adults. The findings also highlight that
mastering even seemingly simple bowing movements constitutes
a prolonged learning process.
Keywords: Bernstein, development, expert, motor control, motor
learning, music
laying a violin to perform a music piece, with its ele-
ments of rhythm, syntax, pitch, and emotion, is based
on the production of spatially and temporally precise pat-
terns of bimanual coordination. For experienced violinists,
the synchronization between finger and bow actions varies
approximately 50 ms from perfect simultaneity (Baader,
Kazennikov, & Wiesendanger, 2005), indicating that musi-
cal skills are characterized by high demands on movement
precision. The acquisition of complex motor skills, such
as playing a string instrument, stretches over consider-
able time with the expert levels of performance requiring
approximately 10,000 hr or 10 years of intense practice
(Ericsson, Krampe, & Tesch-Römer, 1993; Ericsson &
Lehmann, 1996). Although an abundance of research on
implicit motor learning is available, the process of how
humans and primates acquire complex motor skills is still
not fully understood.
In reviewing the available literature, two major research
strands may be identified that focus on different but related
issues of skill learning. The first research strand addresses
whether skill learning is governed by common principles
applied by the nervous system to control the large number
of biomechanical degrees of freedom of the body (e.g., the
number of muscles attached to a limb, or the type of pos-
sible joint rotations). The research strives to identify learn-
ing strategies that are common across learners and that are
valid for the acquisition of large classes of skills. A second
body of literature concerns expert performance, asking
questions about the prerequisites of expert performance,
such as innate abilities or the amount and type of practice
necessary to gain expert knowledge.
From a motor control perspective, skill learning has been
regarded as a process through which the neural control
system achieves movement coordination by mastering the
redundant degrees of freedom of the body or limb system
(Bernstein, 1967, 1988). To master the degrees of freedom
at the beginning of a learning process, Bernstein suggested
that the control system initially eliminates redundant
degrees of freedom by fixating or freezing specific joints
in the kinematic chain. In its theoretical, engineering-based
definition, the term freezing implies full locking of the joint
(i.e., no movement; Kim, 1998). However, in the context of
human motion, a small joint angular amplitude or decreasing
joint amplitude during learning has been interpreted as
a sign of joint freezing (Chow, Davids, Button, & Koh,
2007; Higuchi, Imanaka, & Hatayama, 2002; Hodges,
Hayes, Horn, & Williams, 2005; Steenbergen, Marteniuk,
& Kalbfleisch, 1995; Utley, Steenbergen, & Astill, 2007).
Thus, in the framework of human motor control, the locking
of a joint (allowing for no joint movement) would be
considered the extreme form of freezing. Keeping in line
with the established literature, we considered the mere
restriction of joint motion as a form of freezing. Explicitly,
we defined joint freezing as a reduction in joint angular
amplitude in one or more planes of motion.
The rationale for applying joint freezing during learn-
ing is that by combining adjacent limb segments to behave
like a single link, the kinematic chain becomes shorter and
potentially easier to control (Bernstein, 1967). Although this
claim has been widely cited in the motor-learning literature,
there is limited empirical evidence available to support it.
For example, Vereijken, Van Emmerik, Whiting, and New-
ell (1992) tested young adults in a ski-simulator task. Nov-
ice learners initially locked some joints but began to unlock
their stiffened joints as they gained experience. McDonald,
P
Correspondence address: Jürgen Konczak, Human Sensorimo-
tor Control Lab, School of Kinesiology, University of Minnesota,
400 Cooke Hall, 1900 University Avenue, SE, Minneapolis, MN
55455, USA. E-mail address: jkonczak@umn.edu
Journal of Motor Behavior, 2009, Vol. 41, No. 3, 243–252
Copyright © 2009 Heldref Publications
J. Konczak, H. v. Velden, & L. Jaeger
244 Journal of Motor Behavior
Van Emmerik, and Newell (1989) found evidence for freez-
ing and freeing degrees of freedom and proximal-to-distal
progressions in control in a handwriting task involving the
nondominant limb, and Hodges et al. (2005) found that
novice learners implemented a strategy of joint freezing
during the initial phase of practicing a soccer chip shot.
Although these findings support Bernstein’s (1967) claim,
other reports have shown mixed results. Using the same
ski-simulator task, Hong and Newell (2006) found that the
mechanical degrees of freedom of adult learners were both
recruited and suppressed over the course of practice. This
implies that motor-skill learning does not necessarily follow
a freezing–freeing sequence. Other researchers contend that
motor-skill acquisition in humans should be viewed as a
process of selectively recruiting and suppressing mechani-
cal degrees of freedom throughout the learning process
(Buchanan & Kelso, 1999; Buchanan, Kelso, DeGuzman,
& Ding, 1996), which has been shown to be more success-
ful in robot motor learning than the gradual release of previ-
ously frozen degrees of freedom (Lungarella & Berthouze,
2002). Last, the failure to extract a single learning strategy
governing all skill acquisitions could be because each new
motor task constrains the recruitment of biomechanical
degrees of freedom in a unique way (Newell, 1996; Newell
& Vaillancourt, 2001).
With respect to motor-skill learning, childhood is a time
when humans and other primates acquire numerous new
motor skills that form the basis for their adult motor reper-
toire. Although it seems plausible that infants and children
apply the same or similar motor-learning strategies as adults,
there is little research available to support this argument. No
study clearly documents that motor-skill learning in infancy
and childhood is based on freezing selective degrees of free-
dom. Infants are known to show massive cocontractions of
antagonistic muscles during early reaching movements (Kon-
czak & Dichgans, 1997; Thelen et al., 1993), which effec-
tively freeze a joint. However, it is not known whether these
cocontractions are part of a deliberate strategy of the brain or
are the expression of an immature neural circuitry. In addition,
although the term freezing a particular mechanical degrees of
freedom seems to imply increased joint stiffness or rigidity, it
is not clear that reducing joint range of motion (ROM) must
be associated with muscular cocontractions. In the absence of
muscular coactivation, external forces may stabilize a joint.
Violin playing is a motor skill that children do not easily
master because a novice learner must manage several tasks
simultaneously: (a) assuming the right posture, (b) holding
the violin in the correct playing position, and (c) moving
the bow at the right amplitude and tempo while pressing the
fingers of the violin hand at the correct time and location on
the fingerboard. It is obvious that the expressed kinematic
patterns during bowing are dependent on the string played.
Playing on the G string (the string most distal to the bowing
hand) demands more lifting of the right arm and requires
the activation of the shoulder adductors, whereas playing
the E string (the string closest to the bowing hand), can
be largely accomplished by activating elbow flexor and
extensor muscles (Shan & Visentin, 2003). With respect to
the joint coordination of the bowing arm, trained violinists
show consistent patterns of shoulder and elbow motion,
whereas the wrist joint motion of each player appears to be
highly individualized (Shan & Visentin).
To better understand the control problem for any nov-
ice learner, researchers must consider that bowing actions
require the control of the mechanical degrees of freedom of
the hand plus the seven degrees of freedom of the proximal
wrist, elbow, and shoulder joints. However, the degrees of
freedom of the hand are mostly fixed during bowing because
the fingers grasp the bow, which means they are effectively
constrained by the task. In addition, bowing requires no rota-
tion around the radioulnar joint, and no medialateral rotation
of the humerus, because the bow is placed on the string. Con-
sequently, the two-degrees-of-freedom bowing motions (up
or down; left or right) need to be controlled by coordinating
five joint degrees of freedom (flexion–extension at the wrist,
elbow, and shoulder; shoulder abduction or adduction; wrist
ulnar or radial deviation). Thus, correct string bowing can
be characterized as a task in which the novice learner needs
to find a stable solution to coordinating five degrees of free-
dom of the arm to produce two-degrees-of-freedom bowing
motions. This implies that three arm degrees of freedom are
redundant and need to be constrained by the nervous system
to produce consistent bowing kinematics.
The present study addressed how adult and child novice
violinists learn to produce bowing movements and whether
they use common learning strategies to achieve bow con-
trol. Specifically, we analyzed the joint coordination and
precision of bowing-arm movements to examine evidence
for early joint freezing and the successive release of joint
degrees of freedom. By comparing novice performance to
expert performance, we sought to map the time course of
this learning process; that is, to document how long it may
take to achieve a high level of bow control.
Method
Participants
Participants were 14 healthy violinists, including 10
children (6 girls, 4 boys; M age = 6.8 years, SD = 2.7
years) and four adults (2 women, 2 men; M age = 32.6
years, SD = 11.9 years). All students had experienced the
same instructional background through the Suzuki violin
program of the MacPhail School of Music in Minneapolis,
Minnesota. The Suzuki pedagogical approach to teaching
violin is characterized by a set canon of musical pieces that
are taught in a predefined, specific order. Thus, all violin
students underwent the same teaching sequence that started
with learning four variations of the English nursery rhyme
“Twinkle, Twinkle, Little Star.”
The majority of children were considered novice players
with less than 800 hr of total lifetime practice. Moreover,
6 children had received regular instruction for less than 17
Learning to Play the Violin
May 2009, Vol. 41, No. 3 245
months with the first 3–4 months typically spent learning
the right standing posture, holding the violin, and practicing
rhythm, but not actually playing the violin. In addition, 2 chil-
dren were advanced players, with approximately 2,300 hr of
practice. The group of adults contained 1 novice player (280
hr of practice), 1 advanced player (1,488 hr of practice, or 7
years, 11 months), and 2 experts with more than 10,000 hr of
lifetime practice. The detailed characteristics of each partici-
pant are presented in Table 1. The Institutional Review Board
of the University of Minnesota approved the present study,
and all participants signed informed consent forms. Parental
assent was obtained for all children involved in the study.
Apparatus
We recorded movement of the bowing arm, the bow, and
posture of the violin by means of a three-dimensional opto-
electronic motion-capture system (Motus System, Vicon
Peak Inc., Denver, CO). To track arm motion, we placed
several spherical reflective markers on a participant’s bow-
ing arm. We then placed the shoulder marker on the angle of
the acromion, the elbow marker on the lateral epicondyle of
the humerus, the wrist marker on the ulnar styliod process,
and the wrist marker on each participant’s metacarpal joint
of the third finger (proximal to digit D3). To detect displace-
ments of the bow and violin during play, we marked the tip
and frog of the bow and the chinrest and scroll of the violin
with light-reflecting tape (see Figure 1A). We placed the
cameras in a semicircle around the participant and captured
time-position data of each marker for the length of the play.
In addition, we recorded marker movements at a sampling
rate of 120 Hz. We reconstructed the three-dimensional
coordinate time-series data of each marker offline, using
routines provided by the Peak Motus software.
Procedure
Prior to the data collection, the following arm anthropo-
metrics were measured: forearm length, length of the hand
to the base of the third finger, width of the hand, and cir-
cumference of the arm and forearm. In addition, each par-
ticipant (or parent) indicated the amount of hours practiced
per week, changes in practice regime, volume over time,
and the age when the player began with violin instruction.
We recorded bowing movements while the participants
played the “Twinkle, Twinkle, Little Star” Variation A by
Suzuki (see Figure 1D).1 The song is a well-known 18th
century English nursery rhyme based on the 1761 French
melody Ah! Vous dirai-je, Maman.” We chose Variation
A because it is the first piece of music that all participants
of the present study had to learn, which meant that kine-
matic performance across participants could be compared
with playing experience. We recorded three trials. No time
restrictions were set for completing the piece because the
playing level of the young novice players was not sufficient
to follow a metronome. All participants played their own
violins and bows in the traditional standing performance
stance. Participants used violins appropriately scaled to
their arm length. Violin sizes varied from size one-sixteenth
used by the young children, to full size.
Measurements
We processed the reconstructed three-dimensional time-
coordinate data offline using a fourth-order Butterworth filter
at a cutoff frequency of 6 Hz. We performed all subsequent
kinematic analyses using customized software routines based
on Matlab Technical Programming Language. To obtain a
comprehensive profile of bowing performance, we computed
three different joint angles and one measure to indicate the
angular position of the bow with respect to the string. On
the basis of the coordinate time-data of the various markers,
we computed the following planar joint angles: (a) the angle
between shoulder, elbow, and wrist marker, which defined the
elbow joint angle and (b) two planar shoulder joint angles,
each indicating one possible degree of shoulder motion. The
shoulder angle X (mostly indicating flexion or extension)
was derived from the elbow marker, shoulder marker, and
the calibrated x axis. The shoulder angle Y (mostly indicat-
ing abduction or adduction) was derived from the elbow and
shoulder marker and the y axis (see Figure 1C).
To obtain a measure of the spatial accuracy of moving the
bow on the strings, we computed a bowing angle (the angle
between the bow and the violin strings) as the angle between
the two intersecting straight lines connecting chinrest and
scroll of the violin and frog and tip of the bow, respectively
TABLE 1. Participant Characteristics
Accumulated
Participant Age Practice time lifetime
number Gender (months) (hr/week) practice hours
Adult
1 m 384 30.00 24,356a
2 m 564 5.00 280
3 f 216 4.00 1,488
4 f 403 18.00 19,340a
Child
5 f 54 5.25 336
6 f 87 3.50 658
7 m 129 7.00 2,380
8 f 96 4.00 720
9 f 53 4.00 272
10 m 60 2.00 104
11 f 66 3.00 180
12 m 143 6.50 2,314
13 m 48 3.00 96
14 f 75 3.00 192
Note. Values computed on the basis of self or parent report. Accu-
mulated lifetime practice hours refers to the total amount of hours
participants have been practicing the violin throughout their lives.
f = female; m = male.
aFor the two experts, practice time was estimated on the basis of
their reports that up to the age of 12 years they practiced approxi-
mately 7–10 hr/week and 10–15 hr/week between the ages of 12
and 18 years.
J. Konczak, H. v. Velden, & L. Jaeger
246 Journal of Motor Behavior
(see Figure 1B). On the basis of the self or parental reports,
we computed the total accumulated practice time of each par-
ticipant, which served as an indicator of playing experience.
Results
To understand how changes in the coordination of proximal
arm joints affected the precision manipulating a distal tool,
such as a violin bow, we analyzed the bow kinematics and
the associated joint angular displacements at the shoulder and
elbow. We originally intended to also compute the wrist angle.
However, during the recording, we intermittently lost the hand
marker in several young children and could not compute the
wrist angle for these novice players. Thus, we were unable to
obtain a complete and consistent record of wrist angle data.
Beginner violin students are instructed to move the bow
FIGURE 1. Experimental setup, angular measures, and exemplar joint kinematics. (A)
Marker placement on participant and violin. (B) Bowing angle was computed as the angle
between the two intersecting straight lines connecting chinrest, scroll of the violin, and frog
and tip of the bow, respectively. (C) Diagram depicting the three joint angles used in the
study. (D) Section of the musical score and the associated joint angular kinematics of an
expert performer. Note that the shoulder angle was nearly constant and only changed when
the bow shifted to another string. Playing individual notes was essentially accomplished by
moving the elbow.
Y
Shoulder
A B
Elbow
Tip
Chinrest
Scroll
Base of
index finger Frog
Wrist
Elbow angle
Shoulder X angle Shoulder Y angle Media (Y)
Cranial (Z)
Anterior (X)
X
C
D 0 3 2 1
A string E string E string E string
Shoulder
Elbow
1.2 s 10˚
Bowing angle
Learning to Play the Violin
May 2009, Vol. 41, No. 3 247
perpendicular to the strings. That is, a bowing angle of 90°
is considered the target angle to achieve. Therefore, we ana-
lyzed the bowing angle to provide a measure of the spatial
precision in handling this distal tool.
Spatial Precision of Bowing Movements
Examples of how the bow was placed while the participant
played the examination piece are presented in Figure 2. This
figure shows the bow angle time-series data of 4 participants
representing the extremes of performance seen in our sample
of players. The 4 participants exhibited bowing angles close
to the desired target value of 90°, yet the bow-angle variability
showed a clear gradient with the expert players, revealing the
lowest variability. Whereas the adult expert had a mean bow
angle of 93.3° (SD = 0.9°), the adult beginner played the
same piece of music with a mean bowing angle of 93.7° (SD
= 2.9°). The advanced child performed with a mean bowing
angle of 86.7° (SD = 1.7°) and the beginner child had a
mean bowing angle of 97.1° (SD = 4.8°). In addition, the
differences in the bowing action of these 4 participants are
expressed by the frequency histograms shown in Figure 2,
indicating that the range and distribution of bow angles was
much larger in the novice players (adult range = 17.1°; child
range = 27.4°) when compared to the expert and advanced
players (ranges = 6.7° and 10.3°, respectively).
The analysis of the bowing angle of the complete sample
of violin players revealed that all participants, independent
of age and experience, adapted bowing angles of approxi-
mately 90°. The mean bow angle for players with more than
1,400 hr of practice time was 88.1° (SD = 2.2°) for expert
and advanced players and 93.0° (SD = 6.7°) for the begin-
ners (1 adult, 8 children). The range of observed bowing
angles in the beginner group was approximately twice as
high as seen in the expert and advanced players (beginner
range = 20.7°; expert and advanced range = 9.9). In general,
a greater practice experience was associated with a decrease
FIGURE 2. Exemplar bowing angle profiles of 4 participants while playing the “Twinkle, Twinkle, Little Star” Variation A. The
selected players are representative of the observed breadth in performance. Year indicates the number of years played, whereas
hr indicates the number of hours practiced. Movement times were different as no time restrictions were imposed. The jagged line
indicates a bowing angle of 90°, which demonstrates the ideal angle at which the bow should move against the strings. The right
column shows the associated frequency histograms of the bowing angle time series data. The frequency count refers to the number
of angle–time data samples at each bin. Note how the angular amplitude diminished with playing experience.
Time (s)
5
Bowing angle (deg)
120
Bowing angle (deg)
110
100
90
80
110
100
90
80
110
100
90
80
110
100
90
80
10 15 20 25 30 35 40 45 50 80 85 90 95 110100 105
Child novice player (1.42 years / 272 hr)
Adult novice player (1.16 years / 280 hr)
Child advanced player (3.75 years / 720 hr)
Adult expert player (23.5 years / 24,356 hr)
1000
500
0
1000
500
0
1000
500
0
1000
500
Frequency
J. Konczak, H. v. Velden, & L. Jaeger
248 Journal of Motor Behavior
in the range and variability of the observed bow angles. This
was revealed when expressing bowing-angle variability as
a function of total lifetime practice time. A linear regres-
sion with bow angle standard deviation as a response and
hours of lifetime practice as a predictor variable yielded a
negative correlation of r = –.77 (p = .0012), indicating that
variability of bowing precision decreased as practice hours
increased. Because motor-learning processes are nonlinear
and customarily captured by exponential or power func-
tions, we also fitted an exponential decay function of the
form SDbow = Y0 + A × e(–x/t1) + B × e(–x/t1), a second-order
polynomial function of the form SDbow = A + Bx + Bx2, and
a power function of the form SDbow = AxB (where x = total
lifetime practice hours; e = Euler’s number; Y0, A, B, and C
= constants). All three functions yielded comparable coef-
ficients of determination (exponential: r2 = .67; polynomial:
r2 = .65; power function: r2 = .65), documenting that bow-
ing angle variability was largely explained by total practice
time (see Figure 3).
Joint Kinematics of Bowing Arm
We examined the range and variability of shoulder and
elbow joint motion. An example of how the coordination
of these two joint motions in leading the bow changed
with practice is demonstrated in Figure 4. To relate arm
movements to bow motion, these figures show exemplar
angle–angle diagrams of the shoulder and elbow motion
of three violin players, whose bowing angles were also
shown in Figure 2. Comparing the amount of shoulder
flexion–extension seen in the beginner with the advanced
and expert player revealed that the range of motion at the
shoulder was smaller in the expert. In contrast, the range
of elbow motion did not show a consistent trend in this
sample of participants. Although the expert and child nov-
ice players showed the similar amplitudes of elbow ROM,
FIGURE 3. Development of bowing precision as a function
of practice time. Data points represent the standard deviation
of each participant’s bowing angle when playing “Twinkle,
Twinkle, Little Star” Variation A. Data points measure how
variable the bow was during movement across the strings
and are expressed as a function of each player’s lifetime
practice hours. The abscissa has a log10 scale. The dashed
line represents a polynomial function of the form SDbow =
12.47 5.04x + 0.57p2 fitted to the data set (p = practice
time). The coefficient of determination was R2 = .65.
Accumulated Practice (hr)
SD of Bowing Angle (deg)
7
100
6
5
4
3
2
1
0
1,000 10,000 25,000
R2 = .65 FIGURE 4. Interjoint movement patterns of the 3 exemplar
participants when performing the test piece. Shown are
angle–angle diagrams of the shoulder angle X and the elbow
angle. Note the differences in shoulder range of motion
between child beginner and adult expert. The dashed lines
indicate the approximate slope of the angle–angle data. A
steeper slope indicates a tighter coupling between shoulder
and elbow motion (i.e., a slope of 45° implies that elbow
and shoulder angle changed at the same rate; a flatter slope
indicates that changes in shoulder motion are smaller than
in elbow motion). The slope between shoulder and elbow
motion was steepest in the beginner when compared with
the advanced and expert player, which means that shoulder
and elbow moved together. Note that the corresponding bow
angle data of these participants is presented in Figure 2.
Elbow Angle (deg)
Shoulder X Angle (deg)
80
100 35.78˚
Child beginner
Child advanced
Adult expert
90
80
70
60
50
110
100
90
80
70
60
140
130
120
110
100
90
16.88˚
12.7˚
90 100 110 120
90 100 110 120 130
60 70 80 90 100
Learning to Play the Violin
May 2009, Vol. 41, No. 3 249
the advanced players generated larger amplitude motion.
Note that the elbow–shoulder X interjoint patterns of the
expert and the advanced player resembled a crescent or
a squeezed horseshoe shape (see Figure 4). This pattern
arose because the music piece required a bow shift from
the A string to E string, which each player accomplished
mostly by shoulder adduction or abduction (i.e., lifting the
whole arm slightly up and down). Naturally, the beginner
also performed string crossings, but these crossings lacked
the consistency and low variability observed in the two
more experienced players.
The analysis of the shoulder motion of all players in our
sample revealed that the reduction in bowing-angle variability
was strongly associated with a reduction of shoulder-angle
X ROM (r = .63; see Figure 5A) and shoulder-angle X
variability (r = .71). In contrast, the Pearson product-moment
correlations between elbow-angle ROM or elbow-angle
standard deviation and bowing-angle variability were not
significant (p > .05; see Figure 5B). Attempts to fit a second-
order polynomial or exponential function explained less than
2% of variance in elbow ROM and less than 25% of the
variance in shoulder-angle X ROM (r = .499), both indicating
a poor fit to the data.
The correlation between bowing-angle standard devia-
tion and the shoulder-angle Y ROM was small but sig-
nificant (r = .21), whereas the corresponding correlation
between the standard deviation of the shoulder-angle Y and
bowing-angle standard deviation was not significant (p >
.05). Because the shoulder-angle Y was primarily indica-
tive of abduction and adduction, this meant that changes in
shoulder abduction and adduction were not strongly related
to improvements in bowing precision.
Relating the reduction in the range of shoulder flexion
and extension movements to the amount of accumulated
lifetime practice revealed that increased practice was
associated with a systematic reduction of the ROM of
the shoulder-angle X. This relation was best expressed by
fitting an exponential decay function of the form shoulder-
angle X = A × exp(–p/t1) + Y0, where p denotes lifetime
practice hours (r2 = .59; see Figure 6A). In comparison,
fitting a second-order polynomial function or a power
FIGURE 5. Relation between joint range of motion (ROM)
and bowing precision. Shown are the individual ROM val-
ues for the elbow and shoulder X angles as a function of the
bow-angle variability.
SD of Bow Angle (deg)
Shoulder X ROM (deg)
7
60
r = .07
Elbow ROM (deg)
50
40
30
20
10
0
60
50
40
30
20
10
6543210
r = .63
Nonexpert
Expert
A
B
FIGURE 6. Elbow and shoulder angular range of motion
(ROM) as a function of practice time. Practice time is
expressed as accumulated hours of practice during lifetime.
(A) Shoulder angular ROM decreased with practice time.
The dashed line represents an exponential decay function of
the form ROM = 41.3 × exp(–p/158.7) + 19.5 fitted to the
data (p = practice time). The coefficient of determination was
R2 = .59. (B) Elbow angular ROM was variable across play-
ers and did not change systematically with practice time.
Shoulder X ROM (deg)
50
45
25
20
15
10
5
Shoulder X angle
30
35
40
10,000
Accumulated Practice (hr)
1,000100
10,0001,000100 25,000
55
50
25
20
15
5
0
35
40
45
Elbow ROM (deg)
30
10
A
B
R2 = .59
Elbow angle
J. Konczak, H. v. Velden, & L. Jaeger
250 Journal of Motor Behavior
function of the form shoulder-angle X = A × pB yielded
coefficients of determination, r2 = .56 and .55, respectively.
A corresponding linear regression procedure was also
significant, r2 = .48 (p = .006). A reduction in the ROM was
not observed for shoulder adduction or abduction, which
was expressed primarily by the shoulder angle Y. We found
no significant relation between the shoulder angle Y and
accumulated lifetime practice (r2 = .01).
Elbow-angle ROM varied across participants and did
not change systematically with practice time. We found the
range of elbow-joint motion was not necessarily reduced
or increased as a function of learning. In fact, the two
expert players exhibited quite different ranges of elbow
motion (17.3° vs. 42.1°), whereas the elbow ROM of the
novice players ranged from 15.6° to 50.8°. The reason for
this experience-independent expression of elbow motion is
likely because playing this music piece did not constrain
elbow motion but allowed violinists to express their indi-
vidual preferences of moving the bow (i.e., using less or
more bow to play a particular note). This assessment is
underlined by the data presented in Figure 6B that show no
systematic relation between the expressed range of elbow-
angular motion and practice time. The corresponding linear
regression was not significant, and a second-order polyno-
mial function fit yielded r2 = .009, also indicating a poor
goodness of fit.
Discussion
Understanding the Nature of the Task and a Unique
Sample of Violinists
Playing a string instrument such as the violin requires the
asymmetrical interlimb coordination of both arms. The fin-
gers of the left hand assume a unique posture and need to be
pressed against the fingerboard in a precise spatio-temporal
sequence that is determined by the musical score. The bow-
ing motions of the right arm are essential for sound produc-
tion and expert performers can use an array of kinematically
sophisticated bowing techniques to control rhythm, sound,
and volume, and to express the emotional quality of music.
From a biomechanical perspective, the bow is a tool repre-
senting the distal part of a kinematic chain originating at the
shoulder. Consequently, the spatial and temporal precision
of bowing movements is tightly coupled to the movements
of the proximal limb segments.
The central questions of the present study were how
changes in the intralimb coordination of proximal joints
affected the accuracy of moving a distal tool and how such
intralimb coordination was acquired. Specifically, we ana-
lyzed how specific patterns of proximal joint coordination
determined the spatial accuracy of distal bowing actions in
a group of novice learners and expert players. Our sample
of violinists represented a unique group because all vio-
linists were active or former students of a Suzuki violin
program. The Suzuki program characterizes itself by a set
canon of musical scores that are acquired in a predefined
order by each student. For the present study, it meant that
all students went through the same learning sequence and
all started with exactly the same piece, a variation of the
nursery rhyme “Twinkle, Twinkle, Little Star.” Thus, it
enabled us to assess the effect of long-term practice because
the learning trajectory of each violinist was known and they
all had the same starting point.
Learning to Control the Violin Bow as an Example of
Selective Joint Freezing
The notion of how novice learners acquire new skills and
whether all learners apply similar learning techniques to
control a musculoskeletal system with partially redundant
degrees of freedom have been matters of debate among
researchers for decades. Within this framework, the claim
by Bernstein (1967, 1988) that early phases of motor-skill
learning can be understood as a process in which initially
frozen degrees of freedom are successively released has
found widespread attention in the literature. Solid empiri-
cal evidence to support this claim has been sparse (e.g.,
Vereijken et al., 1992). More recent research on adult motor
learning has modified this notion and suggested that skill
learning may be understood as a process of suppressing
and releasing selective joint degrees of freedom that do not
necessarily follow a predetermined sequence from freez-
ing to unfreezing (Buchanan & Kelso, 1999; Buchanan
et al., 1996). That is, the task constraints may determine
what mechanical degrees of freedom are initially free or
suppressed. This claim has been supported by researchers,
showing that the nervous system may arrive at unique move-
ment solutions by tightly controlling a specific task-relevant
biomechanical variable (i.e., the body’s center of mass).
The other degrees of freedom can be left uncontrolled as
long as they lead to those joint configurations that conform
to the same set of values of the putative controlled variables
(e.g., Kang, Shinohara, Zatsiorsky, & Latash, 2004; Scholz
& Schöner, 1999).
Common to all these studies is their use of adult learners.
To our knowledge, there are no studies available that spe-
cifically investigated the issue of joint freezing or freeing
during motor-skill learning in children. This is especially
noteworthy given that Bernstein (1967, 1988) explicitly
referred to the motor learning of children and infants when
he formulated his joint-freezing hypothesis. It is this knowl-
edge gap that the present study attempted to close.
We selected violin playing because it constitutes a highly
complex skill with unusual arm postures that are based on
asymmetrical patterns of bimanual coordination. Although
this task allows many aspects of coordination to be studied,
in the present article we concentrated on the bowing motion
of the right arm. The main finding of our study was that
when young children acquire the skill of playing the violin,
the learning of placing and moving the bow more precisely
on the strings was not characterized by a successive release
of previously suppressed degrees of freedom. Instead, it was
the opposite: Joint freezing of the shoulder increased with
Learning to Play the Violin
May 2009, Vol. 41, No. 3 251
learning, as documented by the reduction in the amplitude
of sagittal shoulder motion. It is necessary to point out that
joint freezing did not affect all three degrees of freedom at
the shoulder, but was selective. Only the degree of shoulder
motion allowing shoulder flexion and extension became
restricted because such movements, if not compensated by
distal elbow or wrist motion, could have led to unwanted
sideways slipping of the bow on the string.
Our finding highlights the importance of understanding
how a given motor task imposes constraints on a novice
learner. For violin bowing, the learner has to potentially
control seven degrees of freedom at the shoulder, elbow,
and wrist joints. Yet, two of those are constrained by the
task demands and two are needed to control the bow, leav-
ing the learner to find a stable solution to coordinate the
remaining three degrees of freedom. In theory, the learner
has the opportunity to freeze all shoulder motion and only
bow using elbow and wrist motion. However, this is not a
pattern that we observed. Novice learners freely generated
motion at all joints, and learning to place and move the bow
more precisely was associated with a selective suppression
of shoulder flexion or extension because this suppression
led to a decrease in the unwanted spatial variability of the
bowing motion.
Time Course of Learning
A second part of our study concerned the length of the
motor-learning process. The majority of studies on motor
learning use tasks that are acquired in hours or days. Yet,
researchers of expert performance have documented that it
may take years of practice to achieve the highest skill level in
a particular sensorimotor task (Ericsson et. al, 1993). Unfor-
tunately, few studies were able to follow skill acquisition in
children longitudinally over prolonged periods of time (e.g.,
McGraw, 1935), and even fewer provided a biomechanical
analysis of the performance changes (e.g., Konczak & Dich-
gans, 1997; Roberton & Halverson, 1988). Given that all of
our participants had the same starting point and followed the
same learning sequence, our cross-sectional study design
allowed us to compare performance across age groups.
The comparison of joint angular variability between nov-
ice learners and experts documented that novices needed in
excess of 700 practice hr to achieve a variability in their sag-
ittal shoulder motion that was comparable to experts. This
finding is even more remarkable given that novice learners
required considerably less time to learn the required finger
sequence of the violin hand and to acquire the temporal
patterns of bimanual coordination, which allowed them to
play the music piece at a basic level. We also documented
that elbow motion remained highly variable among indi-
viduals across all levels of performance, indicating that this
degree of freedom was not constrained by the task, a find-
ing consistent with the notion of the uncontrolled manifold
hypothesis, which states that the nervous system leaves
certain degrees of freedom uncontrolled as long as the
associated joint configurations lead to acceptable endpoint
motion (Scholz & Schöner, 1999). In our case, this meant
the bow placement on the strings. This uncontrolled degree
of freedom was used by our expert violinists to express their
artistic freedom; one expert performed short bow strokes
with minimal elbow joint excursion, whereas the other
played with longer bow strokes that were associated with
larger amplitudes of elbow joint motion (both exhibited
high spatial precision in moving the bow perpendicular to
the strings; see Figures 5 and 6).
When drawing inferences about the time course of motor-
skill learning, researchers need to be cognizant of the inher-
ent limitations of any cross-sectional design that makes
inferences about a developmental process without having
followed learners longitudinally. With respect to the pres-
ent study, this means that we cannot exclude the possibility
that individual learners followed a different learning trajec-
tory than the one derived from the one-time observation of
several violin players at different ages. It also implies that
the learning curves shown in Figures 3 and 6 should not be
overinterpreted. These curves chart the general course of
kinematic change that can be expected with years of accu-
mulated practice time. In the strict sense, these functions
do not represent learning per se, but rather indicate a level
of skill performance as a function of the extent of training.
This is different from short-term learning studies in which
all participants acquire a skill over hours or days and in
which exponential or power-law function may be indicative
of whether learning has been complete or not (Liu, Mayer-
Kress, & Newell, 2003).
Summary
In contrast to most everyday manual actions, the goal of
music performance lies in its auditory perception, with all
of its components of rhythm, syntax, and emotional feel-
ings. This makes the acquisition of musical skills especially
difficult to master for many beginners. Our findings indicate
that child or adult novice violinists used similar strategies
to constrain the mechanical degrees of freedom of the arm
during skill learning. This implies that the employed neural
constraints are likely not age-dependent, but are rather task-
specific. In addition, the acquisition of complex, fine motor
skills likely does not follow a simple freezing–freeing strat-
egy to control redundant mechanical degrees of freedom, as
our findings demonstrate. Learning to play the violin was
not associated with a release of degrees of freedom, but was
characterized by an experience-dependent suppression of
sagittal shoulder motion. This does not negate Bernstein’s
(1967, 1988) original notion that children may use joint
freezing as a strategy to simplify motor control. However,
as Bernstein pointed out, a freezing–freeing strategy is
primitive and likely only to be used in the initial stages of
motor learning (Bernstein, 1967, 1988 Hodges et al., 2005).
The strategy may be useful for learning in dynamically
unstable environments, such as acquiring locomotor or
postural tasks, in which joint freezing can help to maintain
equilibrium. Alternative strategies such as the successive
J. Konczak, H. v. Velden, & L. Jaeger
252 Journal of Motor Behavior
suppression of joint motion may be more useful for learning
high precision arm-motor patterns. Last, the study provides
further evidence that optimization of joint coordination pat-
terns for complex fine motor skills is a prolonged learning
process that may last years.
NOTE
1. To watch video images of some of the participants in the
study, visit http://hsc.umn.edu/hsc-project4.html
ACKNOWLEDGMENTS
The authors thank all of the children, parents, and adult violin-
ists who participated in this study. Gratitude is extended to Kristen
Pickett and Jehong Jeon from the Human Sensorimotor Control
Lab at the University of Minnesota, who helped with the data
collection and analysis. The authors dedicate this work to Mario
Wiesendanger, the renowned Swiss movement scientist who is
also an accomplished string player and musician.
REFERENCES
Baader, A. P., Kazennikov, O., & Wiesendanger, M. (2005). Coor-
dination of bowing and fingering in violin playing. Cognitive
Brain Research, 23, 436–443.
Bernstein, N. A. (1967). The co-ordination and regulation of
movements. Oxford, England: Pergamon Press.
Bernstein, N. A. (1988). Einige heranreifende probleme der regulation
der bewegungsakte [Few arising problems about the regulation of
motor acts]. In L. Pickenhain & G. Schnabel (Eds.), Bewegungs-
physiologie (pp. 173–192). Leipzig, Germany: Barth Verlag.
Buchanan, J. J., & Kelso, J. A. S. (1999). To switch or not to
switch: Recruitment of degrees of freedom stabilizes biological
coordination. Journal of Motor Behavior, 31, 126–144.
Buchanan, J. J., Kelso, J. A. S., DeGuzman, G. C., & Ding,
M. (1996). The spontaneous recruitment and suppression of
degrees of freedom in rhythmic hand movements. Human
Movement Science, 16, 1–32.
Chow, J. Y., Davids, K., Button, C., & Koh, M. (2007). Variation in
coordination of a discrete multiarticular action as a function of
skill level. Journal of Motor Behavior, 39, 463–479.
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The
role of deliberate practice in the acquisition of expert perfor-
mance. Psychological Review, 3, 363–406.
Ericsson, K. A., & Lehmann, A. C. (1996). Export and exceptional
performance: Evidence of maximal adaption to task constraints.
Annual Review of Psychology, 47, 273–305.
Higuchi, T., Imanaka, K., & Hatayama, T. (2002). Freezing
degrees of freedom under stress: Kinematic evidence of con-
strained movement strategies. Human Movement Science, 21,
831–846.
Hodges, N. J., Hayes, S., Horn, R. R., & Williams, A. M. (2005).
Changes in coordination, control and outcome as a result of
extended practice on a novel motor skill. Ergonomics, 48,
1672–1685.
Hong, S. L., & Newell, K. M. (2006). Change in the organization
of degrees of freedom with learning. Journal of Motor Behavior,
38, 88–100.
Kang, N., Shinohara, M., Zatsiorsky, V. M., & Latash, M. L.
(2004). Learning multi-finger synergies: An uncontrolled mani-
fold analysis. Experimental Brain Research, 157, 336–350.
Kim, S. (1998). Adjustable manipulability of closed-chain mecha-
nisms through joint freezing and joint unactuation. Proceedings
of the 1998 IEEE International Conference on Robotics & Auto-
mation, Leuven, Belgium, 2627–2643.
Konczak, J., & Dichgans, J. (1997). Goal-directed reaching: Devel-
opment toward stereotypic arm kinematics in the first three
years of life. Experimental Brain Research, 117, 346–354.
Liu, Y. T., Mayer-Kress, G., & Newell, K. M. (2003). Beyond curve
fitting: A dynamical systems account of exponential learning in a
discrete timing task. Journal of Motor Behavior, 35, 197–207.
Lungarella, M., & Berthouze, L. (2002). Adaptivity via alternate
freeing and freezing of degrees of freedom. Proceedings of the
9th International Conference on Neural Information Process-
ing, Singapore, 482–487.
McDonald, P. V., Van Emmerik, R. E.A., & Newell, K. M. (1989).
The effects of practice on limb kinematics in a throwing task.
Journal of Motor Behavior, 21, 245–264.
McGraw, M. B. (1935). Growth. A study of Johnny and Jimmy.
NY: Appleton Century.
Newell, K. M. (1996). Change in movement and skill: Learning,
retention, and transfer. In M. Latash & M. Turvey (Eds.), Dexter-
ity and its development (pp. 393–432). Hillsdale, NJ: Erlbaum.
Newell, K. M., & Vaillancourt, D. E. (2001). Dimensional change
in motor learning. Human Movement Science, 20, 695–715.
Roberton, M. A., & Halverson, L. E. (1988). The development of
locomotor coordination: Longitudinal change and invariance.
Journal of Motor Behavior, 20, 197–241.
Scholz, J. P., & Schöner, G. (1999). The uncontrolled manifold
concept: Identifying control variables for a functional task.
Experimental Brain Research, 126, 289–306.
Shan, G., & Visentin, P. (2003). A quantitative three-dimensional
analysis of arm kinematics in violin performance. Medical
Problems in Performing Arts, 18, 3–10.
Steenbergen, B., Marteniuk, R. G., & Kalbfleisch, L. E. (1995).
Achieving coordination in prehension: Joint freezing and pos-
tural contributions. Journal Motor Behavior, 27, 333–348.
Thelen, E., Corbetta, D., Kamm, K., Spencer, J. P., Schneider,
K., & Zernicke, R. F. (1993). The transition to reaching: Map-
ping intention and intrinsic dynamics. Child Development, 64,
1058–1098.
Utley, A., Steenbergen, B., & Astill, S. L. (2007). Ball catching in
children with developmental coordination disorder: Control of
degrees of freedom. Developmental Medicine and Child Neu-
rology, 49, 34–38.
Vereijken, B., Van Emmerik, R. E. A., Whiting, H. T. A., & New-
ell, K. M. (1992). Free(z)ing degrees of freedom in skill acquisi-
tion. Journal of Motor Behavior, 24, 133–142.
Submitted February 15, 2008
Revised May 28, 2008
Second revision October 9, 2008
Accepted October 17, 2008
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Exoskeletons can enhance human mobility, but we still know little about why they are effective. For example, we do not know the relative importance of training, how much is required, or what type is most effective; how people adapt with the device; or the relative benefits of customizing assistance. We conducted experiments in which naïve users learned to walk with ankle exoskeletons under one of three training regimens characterized by different levels of variation in device behavior. Assistance was also customized for one group. Following moderate-variation training, the benefits of customized assistance were large; metabolic rate was reduced by 39% compared to walking with the exoskeleton turned off. Training contributed about half of this benefit and customization about one quarter; a generic controller reduced energy cost by 10% before training and 31% afterwards. Training required much more exposure than typical of exoskeleton studies, about 109 minutes of assisted walking. Type of training also had a strong effect; the low-variation group required twice as long as the moderate-variation group to become expert, while the high-variation group never acquired this level of expertise. Curiously, all users adapted in a way that resulted in less mechanical power from the exoskeleton as they gained expertise. Customizing assistance required less time than training for all parameters except peak torque magnitude, which grew slowly over the study, suggesting a longer time-scale adaptation in the person. These results underscore the importance of training to the benefits of exoskeleton assistance and suggest the topic deserves more attention.
Article
Exoskeletons can enhance human mobility, but we still know little about why they are effective. For example, we do not know the relative importance of training, how much is required, or what type is most effective; how people adapt with the device; or the relative benefits of customizing assistance. We conducted experiments in which naïve users learned to walk with ankle exoskeletons under one of three training regimens characterized by different levels of variation in device behavior. Assistance was also customized for one group. After moderate-variation training, the benefits of customized assistance were large; metabolic rate was reduced by 39% compared with walking with the exoskeleton turned off. Training contributed about half of this benefit and customization about one-quarter; a generic controller reduced energy cost by 10% before training and 31% afterward. Training required much more exposure than typical of exoskeleton studies, about 109 minutes of assisted walking. Type of training also had a strong effect; the low-variation group required twice as long as the moderate-variation group to become expert, and the high-variation group never acquired this level of expertise. Curiously, all users adapted in a way that resulted in less mechanical power from the exoskeleton as they gained expertise. Customizing assistance required less time than training for all parameters except peak torque magnitude, which grew slowly over the study, suggesting a longer time scale adaptation in the person. These results underscore the importance of training to the benefits of exoskeleton assistance and suggest the topic deserves more attention.
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We recorded reaching movements from nine infants longitudinally from the onset of reaching (5th postnatal month) up to the age of 3 years. Here we analyze hand and proximal joint trajectories and examine the emerging temporal coordination between arm segments. The present investigation seeks (a) to determine when infants acquire consistent, adult-like patterns of multijoint coordination within that 3-year period, and (b) to relate their hand trajectory formation to underlying patterns of proximal joint motion (shoulder, elbow). Our results show: First, most kinematic parameters do not assume adult-like levels before the age of 2 years. At this time, 75% of the trials reveal a single peaked velocity profile of the hand. Between the 2nd and 3rd year of life, “improvements” of hand- or joint-related movement units are only marginal. Second, infant motor systems strive to obtain velocity patterns with as few force reversals as possible (uni- or bimodal) at all three limb segments. Third, the formation of a consistent interjoint synergy between shoulder and elbow motion is not achieved within the 1st year of life. Stable patterns of temporal coordination across arm segments begin to emerge at 12–15 months of age and continue to develop up to the 3rd year. In summary, the development toward adult forms of multijoint coordination in goal-directed reaching requires more time than previously assumed. Although infants reliably grasp for objects within their workspace 3–4 months after the onset of reaching, stereotypic kinematic motor patterns are not expressed before the 2nd year of life.
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The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 yrs. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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The degrees of freedom problem is often posed by asking which of the many possible degrees of freedom does the nervous system control? By implication, other degrees of freedom are not controlled. We give an operational meaning to "controlled" and "uncontrolled" and describe a method of analysis through which hypotheses about controlled and uncontrolled degrees of freedom can be tested. In this conception, control refers to stabilization, so that lack of control implies reduced stability. The method was used to analyze an experiment on the sit-to-stand transition. By testing different hypotheses about the controlled variables, we systematically approximated the structure of control in joint space. We found that, for the task of sit-to-stand, the position of the center of mass in the sagittal plane was controlled. The horizontal head position and the position of the hand were controlled less stably, while vertical head position appears to be no more controlled than joint motions.
Article
The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.
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Overuse Syndrome (OS) resulting from repetitive motion affects a significant percentage of performing musicians. Particularly susceptible to OS, violinists use different kinds of muscle control patterns in the right and left limbs and must assume a complex asymmetrical posture to hold and play the instrument. There is a clear need for developing efficient and effective strategies to prevent OS in violinists, keeping biological loads under physiological limits and focusing on physical economy during training. The first step in developing such strategies requires quantitative kinematic description of the motions involved in violin performance. This study supplies such information for the arms and violin bow. The motions of eight professional violinists and three advanced university music students were captured using a nine-camera VICON V8i motion capture system. Each performed a fundamental control skill employing all four strings of the violin. The data was analyzed using quantitative model comparison and statistical analysis. The results of this study show parameters such as elbow height normalized by body height and shoulder and elbow joint motion to have highly consistent patterns between the subjects. Wrist control patterns varied widely. Playing on different strings influences right arm patterns significantly, but not left. This is the first study providing quantitative 3-D kinematic data on shoulders, elbows, wrists and bow. It provides a foundation for further exploration of the kinematic characteristics of violin performance, for the examination of the potential causes of OS, and for an evaluation of practices that might minimize injuries.
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This study reports an empirical investigation into Bernstein's (1967) ideas that in the early stages of the acquisition of a movement skill the coordination problem is reduced by an initial freezing out of degrees of freedom, followed later in the learning process by the release of these degrees of freedom and their incorporation into a dynamic, controllable system. “Freezing” degrees of freedom was made operational both as a rigid fixation of individual degrees of freedom and as the formation of rigid couplings between multiple degrees of freedom. Five subjects practiced slalom-like ski movements on a ski apparatus for 7 consecutive days. Results showed that at the early phases of learning, the joint angles of the lower limbs and torso displayed little movement, as expressed by their standard deviations and ranges of angular motion, whereas joint couplings were high, as shown by the relatively high cross correlations between joint angles. Over practice, angular movement significantly increased in all joint angles of the lower limbs and torso, although the cross correlations decreased. Support for the processes of freezing and releasing degrees of freedom was thus given at both levels of operationalization. In addition, a consistent change from laterally symmetric to laterally asymmetric cross-correlation patterns were observed as a function of practice. Overall, the findings provide empirical support for Bernstein's ideas regarding the mastery of redundant degrees of freedom in the acquisition of coordination.
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The onset of directed reaching demarks the emergence of a qualitatively new skill. In this study we asked how intentional reaching arises from infants' ongoing, intrinsic movement dynamics, and how first reaches become successively adapted to the task. We observed 4 infants weekly in a standard reaching task and identified the week of first arm-extended reach, and the 2 weeks before and after onset. The infants first reached at ages ranging from 12 to 22 weeks, and they used different strategies to get the toy. 2 infants, whose spontaneous movements were large and vigorous, damped down their fast, forceful movements. The 2 quieter infants generated faster and more energetic movements to lift their arms. The infants modulated reaches in task-appropriate ways in the weeks following onset. Reaching emerges when infants can intentionally adjust the force and compliance of the arm, often using muscle coactivation. These results suggest that the infant central nervous system does not contain programs that detail hand trajectory, joint coordination, and muscle activation patterns. Rather, these patterns are the consequences of the natural dynamics of the system and the active exploration of the match between those dynamics and the task.
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In most studies examining pattern switching in biological coordination, emphasis is placed on identifying the mechanisms underlying bifurcations in an already active set of components. Less well understood are the processes by which quiescent degrees of freedom (df) are recruited and active df suppressed. To examine such behavior, we studied four bimanual and two unimanual coordination patterns. Subjects produced the patterns in time with an auditory metronome whose frequency increased from 1.5 to 4.25 Hz in 0.25 Hz steps. Interlimb transitions from asymmetric to symmetric patterns in a motion plane occurred at critical cycling frequencies, f1. Spatial transitions, characterized by recruitment of y-(vertical) and suppression of x-(horizontal) motion, also occurred at critical cycling frequencies, f2. This recruitment-suppression process was either abrupt (2–3 cycles) or gradual (1 to 6 plateaus) where the finger-tips traversed an elliptical orbit in x, y space. Similar spatial transitions were observed in unimanual conditions. The results are discussed in reference to the problem of how task-specific coordination patterns are modified. The Hopf bifurcation is presented as a generic mechanism underlying the recruitment and suppression of df. Similarities between the four component bimanual pattern dynamics and the coordination dynamics of four limb patterns (e.g., in quadrupeds) are discussed.
Article
The onset of directed reaching demarks the emergence of a qualitatively new skill. In this study we asked how intentional reaching arises from infants' ongoing, intrinsic movement dynamics, and how first reaches become successively adapted to the task. We observed 4 infants weekly in a standard reaching task and identified the week of first arm-extended reach, and the 2 weeks before and after onset. The infants first reached at ages ranging from 12 to 22 weeks, and they used different strategies to get the toy. 2 infants, whose spontaneous movements were large and vigorous, damped down their fast, forceful movements. The 2 quieter infants generated faster and more energetic movements to lift their arms. The infants modulated reaches in task-appropriate ways in the weeks following onset. Reaching emerges when infants can intentionally adjust the force and compliance of the arm, often using muscle coactivation. These results suggest that the infant central nervous system does not contain programs that detail hand trajectory, joint coordination, and muscle activation patterns. Rather, these patterns are the consequences of the natural dynamics of the system and the active exploration of the match between those dynamics and the task.