https://doi.org/10.20965/jaciii.2011.p0942
Kudo, K. et al.

Paper:

Neurophysiological and Dynamical Control Principles
Underlying Variable and Stereotyped Movement Patterns
During Motor Skill Acquisition
Kazutoshi Kudo∗1 , Makoto Miyazaki∗2 , Hirofumi Sekiguchi∗3 , Hiroshi Kadota∗4 ,Shinya Fujii∗4,∗5 , Akito Miura∗1,∗5 , Michiko Yoshie∗1,∗5,∗6 , and Hiroki Nakata∗7∗1

Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo
3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
E-mail: kudo@idaten.c.u-tokyo.ac.jp∗2 Research Institute of Kochi University of Technology
185 Miyanokuchi, Tosayamada, Kochi 782-8502, Japan∗3 Faculty of Business and Information Sciences, Jobu University∗4 Division of Physical and Health Education, Graduate School of Education, The University of Tokyo∗5 Japan Society for the Promotion of Science∗6 Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, University of Sussex∗7 Faculty of Sport Sciences, Waseda University[Received February 20, 2011; accepted June 21, 2011]

While novices who are unfamiliar to a new motor
skill typically show variable and unstable movements,
highly skilled experts show a stable and accurate performance. These distinct differences in motor control
between experts and novices have led researchers tohypothesize that neuromotor noise is reduced in theprocess of motor skill acquisition. On the other hand,
it should be noted that novices’ movements have othercharacteristics; they are habituated and stereotyped.
In this review, we discuss the principles governing spatiotemporal organization of movements in novices andexperts while solving specific motor problems undervaried conditions, by introducing experimental andtheoretical studies that use neurophysiological techniques such as electromyography, functional magnetic
resonance imaging, and transcranial magnetic stimulation, and mathematical models such as stochastic
and dynamical models. On the basis of the findings
from a variety of perceptual-motor skills (e.g., ballthrowing, badminton smash, long-distance running,
piano and drum performance, street dance, a popular
hand game of rock-paper-scissors, and temporal orderjudgement task), we argue that the novices’ characteristic movement patterns were organized under specific constraints and typical strategy, without which
the variability would increase even more, while experts’ movements were organized with functional andcompensatory variability that can drive out erroneous
noise variability. We also showed that in a particular
type of interlimb coordination, skilled and unskilled
movement patterns could be seamlessly described as
the time evolution of nonlinear and self-organized dynamical systems, suggesting that the dynamical systems approach is a major candidate for understanding
the principle underlying organization of experts’ andnovices’ movements.
942

Keywords: human motor skill, learning, neuroscience,
dynamical systems approach, novices and experts

1. Introduction
Highly skilled athletes, musicians, or craftspersons
who have acquired specific perceptual-motor skills
through long-term practice can show stable, precise, andaccurate performances that even cutting-edge artificial intelligent machines or robots cannot do. For example, toplevel golfers can perform an approach shot directed toland the ball on the green from various distances and under various conditions (e.g., on rainy or windy days, fromhazard areas such as bunker or rough). On the other hand,novices who are quite unfamiliar with a new motor skill
typically show highly unstable, variable, and erroneous
performances that seem to be clumsy. For example, beginners of golf can rarely hit a ball with a golf club, andeven if the club happens to strike the ball, it almost always
flies or rolls in an unintended direction.
These distinct differences in motor control between experts and novices have led researchers to hypothesize thatneuromotor noise arising from random firing in the sensorimotor pathways, including the central and peripheral
nervous systems, should be reduced for motor skill acquisition [1, 2]. However, it should be noted that the movements of novices are characterized as not only unstable,variable, and erroneous but also consistent, habituated,and stereotyped. For example, Bernstein [3] pointed outthat during cycling, when the bicycle inclines to the left,a beginner typically and instinctively turns the handlebars
to the right (i.e., wrong and opposite direction). Novicesof street dance have difficulty in producing variable andflexible movement patterns, that is, the coordination of
joint movements are fixed [4]. On the other hand, experts

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.15 No.8, 2011

© Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of
the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/).

Neurophysiological and Dynamical Control Principles

can perform variable and flexible movements dependingon the context or task requirements. In addition, theoretical development for analyzing and interpreting variable
and complex time-series has enabled researchers to reveal the critical role of variability in motor control andlearning [5, 6]. Therefore, one of the fundamental challenges in the study of human motor control and learning
is to understand the principles that govern the spatiotemporal organization of movements in novices and experts
while trying to solve specific motor problems under variable conditions. In this review, we addressed this issue by
introducing a variety of perceptual-motor skills includingball-throwing, badminton smash, long-distance running,
piano and drum performance, street dance, a popular handgame of rock-paper-scissors (RPS), and temporal orderjudgement (TOJ) task.
In Section 2, we show the characteristics of skilled andunskilled movements from a classical point of view (i.e.,
motor learning for noise reduction). In Section 3, weshow typical electromyographic (EMG) patterns of cocontraction of antagonistic muscle pairs of novices whose
performance was more variable and erroneous than thoseof experts. In Section 4, we introduced studies demonstrating that stable and accurate performances can be produced by functional and compensatory organization ofvariable executing parameters. In Section 5, we introduced a statistical estimation, Bayesian integration, in
which variability plays a critical role in estimating the optimal dependence on prior knowledge (i.e., memory) andsensory information. In Section 6, we showed an example
in which skilled athletes who have played a specific-typeof sport have superior ability to inhibit typical responses.
We also discussed the neural correlates of response inhibition. In Section 7, we argue that the possibility that anonlinear dynamical approach can describe the spatiotemporal organization of movements in novices and experts as
the time evolution of self-organized systems.

2. Reduced Variability in Performance andMovements During Skill Acquisition
Novices who are unfamiliar with a novel motor skill
typically show more variable performances, movements,and movement-related parameters such as EMG activities in a variety of motor skills than experts do. For
example, Sakurai and Ohtsuki [7] examined the characteristics of muscle activities in the forearm, upper arm,and back (trunk) muscles, and performance accuracy ofskilled and unskilled individuals during badminton smash
and found that the novices showed less constant time frompeak EMG amplitude to impact and more variable performances. Using analysis of covariance, Nakayama et al.
[8] found that long-distance runners attained higher running speed and smaller variability in stride intervals than
non-runners. Fujii et al. [9, 10] reported that in repetitive rapid tapping task with a drumstick, professional
drummers showed less variable performance, measured
in terms of inter-tapping intervals, and a lower level of
Vol.15 No.8, 2011

muscle co-contraction than non-drummers. Miura et al.
[4] reported that in the case of a whole-body sensorimotor synchronization task with knee flexion and extension
movements in a standing position to a metronome beatwith various beat rates, expert street dancers showed morestable coordination pattern than non-dancers.
The characteristics found in the above studies havebeen confirmed by not only cross-sectional studies (i.e.,expert–novice comparison studies) but also longitudinalstudies in which changes in learning-related parameters
within the same individuals in the process of motor skill
acquisition were examined. Sakurai and Ohtsuki [7] examined the effect of practice on performance and muscle
activities for badminton smash and found that the peak
amplitude of the wrist flexor EMG tended to appear before the time of impact, which was typically observedin skilled badminton players. In addition, a number ofstudies have suggested that movement outcomes and patterns become more consistent with practice for a variety
of skills such as reaching [11], tracking [12], typing [13],slalom-like skiing task [14], and ball-throwing [15].

3. Stereotyped Co-Contraction of Antagonistic
EMGs in Movements Performed by Novices
or Under Physical/Psychological Perturbations
Novices’ performances are variable on one hand, but
the underlying motor control strategy can be consistentand stereotyped on the other hand. For example, previous
researches assessing EMG activities have consistently indicated that compared to skilled individuals, novices arelikely to show higher levels of co-contraction of antagonistic arm muscles during the execution of unfamiliar motor tasks such as piano performance [16] and drumming
performance [9, 10]. An experiment employing reaching
movement tasks [17] also demonstrated that motor skill
learning led to gradual decreases in co-contraction levelsof both the arm and shoulder muscles, which were highly
correlated with measured joint stiffness. Importantly, although at the very beginning of learning the participantsof this experiment revealed torque profiles that were variable from trial to trial, they soon achieved occasionallysuccessful trials. Osu et al. [17] attributed the reason forthis finding to increased muscle co-contraction levels, insisting that it is an effective strategy used by the central
nervous system (CNS) early during learning to improve
movement accuracy, even in the absence of a fully formedinternal model of the effector, by increasing the viscoelastic resistance of the musculoskeletal system. Moreover, asimulation study by using a driven damped oscillator [18]indicated that viscoelastic resistance led to an output signal that is tightly coupled to the input, helping the CNS totame the perturbing effects of non-muscular forces.
We may also apply this view to particular circumstances in which motor outputs can be variable. It is
generally known that the co-contraction levels increase

Journal of Advanced Computational Intelligenceand Intelligent Informatics

943

Kudo, K. et al.

in expectation of a physical perturbation. Interestingly,
previous experimental studies demonstrated that psychological stress exerts the same effects as that by physicalperturbation using situations that accompany extra cognitive load [19], increased precision demands [20, 21], or
social-evaluative threats [22, 23]. These sources of psychological stress could provide a condition where a new
perceptual-motor solution has to be found [24] due toincreased neuromotor noise [19], which possibly stems
from an altered state of the motor system (e.g., increased
corticospinal tract excitability [25–27] during early stagesof learning. Whether during movements at early stages oflearning or those under physical/psychological perturbations, stereotyped muscle co-contraction can be an adaptive strategy to stabilize movements by freezing the excessive biomechanical degrees of freedom [28] and mitigating the adverse effects of various sources of noise on
motor performance.

4. Variability as a Result of Compensatory Coordination
To organize spatiotemporal pattern of movements as
the solution for motor problems, humans have to deal withhuge degrees of freedom in each element of the motor
system (e.g., joints, muscles, neurons, synapses) [28, 29].
Because the individual degrees of freedom are subject tovariation, for example, fluctuation in the activation levelin α -motoneuron pool in the spinal cord, human movements inevitably become variable. Statistically, when individual degrees of freedom vary randomly, the whole
variance equals the sum of the individual variance. Therefore, even if the individual degrees of freedom have onlya small variance, the whole system with huge degrees of
freedom is obliged to show large variability.
The number of degrees of freedom in human motor system is not only very huge but also highly redundant. Thismeans that when the individual degrees of freedom arecoordinated to compensate one another, variability of thewhole system can be smaller than the sum of variability of
the individual degrees of freedom. Recent developmentof mathematical/statistical methods has made it possible
to quantify functional or task-related structure of variability [15, 30–32]. For example, Kudo et al. [15] showedthat the compensatory coordination among executing parameters improves the process of motor skill acquisition
by using a statistical method to evaluate this particular
type of coordination. In their experiment, the participants
practiced a ball-throwing task aimed at a stationary target
for 150 times with the non-dominant hand. The results
showed that the mean absolute error (i.e., mean absolute
distance from the ball landing point to the target) reducedwith practice, and the compensatory relationship among
the release parameters (i.e., ball-release point, and orientation and magnitude of velocity vector of the ball at therelease point) improved. Similar functional organizationof variability has been observed in reaching [20], force
production with fingers [33], and aiming [34] tasks, all of
944

which required accuracy. These studies suggest that noise
variability in novices changes into flexible and adaptive
variability in experts during motor skill acquisition.

5. Bayesian Integration: Variability Plays a
Critical Role in the Integration of Prior
Knowledge with Sensory Information
Experienced golf professionals who calculatingly playin The Open Championship know a lot about Links
courses (e.g., slope of the green, grain of grass, and direction and force of the wind). Without prior knowledge, theplayers could not select the optimal direction and strengthof stroke in various situations. This example implies
that expert athletes or players should have an appropriate
knowledge about the tasks or environments through previous experiences. However, trial-by-trial variations often
arise in the tasks and environments. When the variation isconsiderable, too much dependence on prior knowledge
may bring fatal erroneous judgements or actions. In thiscase, experts should perform based on current information that they sense on site by giving less priority to priorknowledge. Taken together, experts should appropriately
integrate prior knowledge with current sensory information. The Bayesian integration model [35–39] gives us an
optimal solution for integration in a probabilistic manner
Recent psychophysical studies have shown that
Bayesian integration is implemented in various human behaviors such as reaching [40, 41], reproducing forces [35],and timing [42–45]. In this review, we introduce data regarding the perceptual task requiring TOJ of two tactile
stimuli, one delivered to each hand.
The stimulus onset asynchrony (SOA) of the two stimuli is defined as that which are truly delivered to the hands(Ttrue ) and that which are represented in the early stage ofsensory processing (Tsensed ) These parameters can be formulated using the Bayesian theorem as follows:1p(Ttrue )p(Tsensed |Ttrue ), . . (1)awhere a is the normalization constant, p(Ttrue ) is the priorof Ttrue , p(Tsensed |T true ) is the likelihood of Tsensed when
Ttrue is given, and p(Ttrue |T sensed ) is the posterior of Ttrue
when the CNS perceives Tsensed .
Miyazaki et al. [45] experimentally imposed biased
Gaussian distributions on trial-by-trial variability of thetrue SOA between the two stimuli (Fig. 1A). Here, we assumed that the participants can learn the biased Gaussian
distribution as the prior and that the likelihood also had aGaussian distribution. Then, we can calculate the optimal
estimate for the SOA, Testimated (for details, see Kording etal. [40] as follows:p(Ttrue |Tsensed ) =Testimated = (1 − k)μprior + kTsensed ,

. . .

(2)

. . . . . . . . .

(3)

where
k=

2
σprior22
σprior+ σsensed

Journal of Advanced Computational Intelligenceand Intelligent Informatics.

Vol.15 No.8, 2011

Neurophysiological and Dynamical Control Principles

μ prior and σ prior denote the mean and standard deviation(SD) of the prior distributions, respectively. The σ sensed
denotes SD of the likelihood, that is, the size of the sensory uncertainty (i.e., inverse of sensory resolution) regarding the SOA. As shown in Eq. (3), the optimal estimate Testimated is obtained as a weighted sum of μ prior andTsensed .
The Bayesian integration model predicts the TOJ response as follows (for details, see Miyazaki et al. [45]):
σsensed 2
du = −
μprior ,. . . . . . . (4)σprior

Bayesian integration in temporal order judgement (TOJ) of two tactile stimuli, one delivered to eachhand [45]. A: Prior distribution of the stimulus-onset asynchrony (SOA) of the two stimuli. On the x-axis, the positive
values of the SOA signify that the right hand was stimulated
earlier than the left, and the negative values signify the left
hand was stimulated earlier. The y-axis is the frequency of
the SOA. The solid Gaussian has a mean (μ prior ) of 80 ms
and a standard deviation (SD; σ prior ) of 80 ms. Given thisprior, participants were exposed to stimulus pairs with theright hand being stimulated earlier in ≈84% of the trials.
Conversely, given the dashed Gaussian (μ prior = −80 ms,
σ prior = 80 ms), participants were exposed to stimulus pairs
with the left hand earlier in ≈84% of trials. B: Predictions of
TOJ responses by Bayesian integration model (Eq. (4)). The
psychometric functions shift away from peaks of the priordistributions. C: Experimental observation for tactile TOJ.
The psychometric functions of the participants’ responses
were in accordance with the Bayesian integration model.
The opposing effect of lag adaptation has been observed in
the multi-sensory [79–81] and multi-feature [82] integrative
tasks. Bayesian models predict that lag adaptation occurs at
the stage of likelihood (i.e., early sensory processing) [83]and that the prior-dependent Bayesian effects cannot be observed under strong lag adaptation [45]. In the tactile TOJ
between the hands, lag adaptation should not occur and a
Bayesian shift appears.

Fig.1. 1.
Fig.

Vol.15 No.8, 2011

where du denotes the horizontal transition of the psychometric function of TOJ according to the prior distribution.
The Bayesian model predicts that the psychometric function shifts away from the peaks of the prior distributions(Figs. 1A, B). Such a psychometric shift was observedin the tactile TOJ (Fig. 1C). The psychometric shift implies that the participant’s TOJ was biased toward the order that they had previously and most frequently experienced. For example, after the participants frequently experienced stimulus pairs with the right hand stimulated
earlier than the left (solid lines Fig. 1A), their judgement
rate of the right-hand-first increased (solid lines Figs. 1B,
C). Thus, Bayesian integration induces the temporal order
estimation depending on prior experience.
The dependency on the prior experience is determinedin the size of the variability (Eq. (4)). When the variabilityof σ prior is smaller (i.e., reliability of prior information ishigher), the Bayesian estimate depends more on the priorexperience. Conversely, when the σ prior is larger, the estimate depends less on the prior but more on the current
sensory information. Theoretically, the Bayesian strategy
minimizes the mean square error (MSE) of the estimate
from the actual target state. The MSE can be expressed asfollows [40]:2MSE = kσsensed.

. . . . . . . . . . . (5)Note that the coefficient k (Eq. (3)) is always smaller than
1. This implies that Bayesian integration inevitably reduces the effect of sensory noise that is inherent to ournervous system.
Here, we should also note the side effects of Bayesian
integration. For example (solid lines Fig. 1C), after
the participants frequently experienced the stimulus pairs
with the right hand stimulated earlier, they tended to judge
even the stimulus pairs in which the left hand was stimulated slightly earlier (e.g., SOA = −20 ms) as right-handfirst. Such a judgement is an illusory or erroneous response. Thus, Bayesian integration can increase our success rate by depending on a stereotyped event in priorexperience, but it can also lead to erroneous behavior or
evaluations with infrequent or unfamiliar events. These
effects can explain the reason why players in sport games
are often faked or tricked by unfamiliar events. Experts at
faking opponents would cleverly inculcate the prior into
the opponents in the context of the games. Moreover,players skillful at avoiding the fakes may be able to in-

Journal of Advanced Computational Intelligenceand Intelligent Informatics

945

Kudo, K. et al.

hibit the responses biased to the stereotyped event in theprior experience.

6. Neural Mechanisms Related to Response Inhibition
In baseball, a batter has to decide to swing or not
to swing within a fraction of a second after the pitcher
throws the ball. For better batting performance, the batterhas to rapidly inhibit their ongoing swing when they judge
the ball thrown out of the strike zone. However, novices
in baseball batting usually have difficulty in stopping theirswing movement once they start to swing the bat.
In the area of experimental psychology and neurophysiology, this type of motor inhibition has been studied
by using Go/NoGo task [46–48] or stop signal paradigm[49–51], in which rapid inhibition of planned or ongoing movement is required. In addition, recent neuroimaging studies using functional magnetic resonance imaging(fMRI) have shown that the neural networks associatedwith inhibitory processing, including the dorsolateral andventrolateral prefrontal cortices (DLPFC and VLPFC, respectively), supplementary motor area (SMA), anterior
cingulate cortex (ACC), and temporal and parietal lobes[52, 53].
While comparing athletes and non-athletes, Kida et al.
[54] reported that skilled baseball players show shorter
Go/NoGo reaction times than tennis players and nonathletes, although there were no differences in the simple reaction time in players either for sports experience or skill levels. Their longitudinal study of highschool baseball players also showed that an intensive twoyears of baseball practice improved the Go/NoGo reaction time, while the simple reaction time remained constant. Nakamoto and Mori [55], used event-related potentials (ERPs) obtained by time-locked averaging electroencephalography (EEG) and found that the amplitudesof P300 in NoGo trials, which have been interpreted as
the strength of response inhibition, were larger in baseball players than in controls at frontal electrodes when
the stimulus–response mapping was similar to that usedin baseball batting. They suggested that baseball players have stronger brain inhibitory functions that can stopmovements as quickly as possible. Similar findings using ERPs during Go/NoGo tasks were also reported infencers [56]. These studies indicate that neural plasticchanges found in Go/NoGo tasks are related to the acquisition and execution of motor skills during extensive daily
physical and cognitive training that requires quick Go or
NoGo decision making.
Kadota et al. focused on the modified RPS task [57,58]. The RPS gestures are in a three-sided impasse (i.e.,rock wins over scissors, but loses to paper; paper wins
over rock, but loses to scissors; and scissors wins overpaper, but loses to rock). This game is frequently used indaily life, and the responses are well-learned behaviors. In
the modified RPS task, participants respond quickly to apresented gesture signifying rock, paper, or scissors. Be946

Fig. 2. Targeting of the left middle frontal gyrus for transcranial magnetic stimulation (TMS). A: Spatial overlap between activation areas during modified rock-paper-scissors(RPS) task. Red significant areas activated in the lose compared with the win group. Yellow significant activation in
the lose group. Green significant activation in the win group.
All regions shown are significant activation areas, P < 0.001
uncorrected. B: Illustration of the TMS experiment for a participant using the system that can precisely control stimulus
location on the brain [58].

cause losing is more difficult than winning, it is thoughtthat the win response is the stereotyped response in themodified RPS task and that the difficulty in making positive attempts to lose reflects a malfunction of stereotyped
response inhibition [59]. Recent fMRI studies have shownthat the prefrontal cortex is activated when healthy participants attempt to purposefully lose [57, 58, 60], suggesting that the prefrontal cortex is involved in an attempt tolose and may play a role in the inhibition of stereotyped
behavior.
However, while the results derived from fMRI studies
can show brain activities relating to the task, they cannot
show the specific functions of the activated brain regions.
Kadota et al. [58] therefore investigated the causal relationship between the brain area detected by fMRI andthe behavior of participants by single-pulse transcranial
magnetic stimulation (TMS) that is used as a momentaryinterference technique in a stimulated brain area. To examine the critical time interval during which the corticalarea was involved in the inhibition of the stereotyped response during the modified RPS task, we applied singlepulse TMS at various times (ranging from −500 ms before the onset of the visual stimulus to +300 ms after the
onset of the visual stimulus). From the fMRI data, participants in the lose condition showed higher activation of
the left DLPFC as compared to those in the win condition(Fig. 2). If the left DLPFC was essential for the inhibi-

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.15 No.8, 2011

Neurophysiological and Dynamical Control Principles
A
Number of total errors

3

Total (Opposite + Draw)

2.5
2
1.5
1
0.5
0
-0.5

-500

-400

-300

-200

-100

-1

B

100

200

300

no stimC
3

3

Opposite

2.5
2
1.5
1
0.5
0
-0.5
-1

-500 -400 -300 -200 -1000

100 200 300

no
stim

Number of draw errors

Number of opposite errors

0

Stimulus interval from visual stimulus (ms)

Draw

2.5
2
1.5
1
0.5
0
-0.5

-500 -400 -300 -200 -1000

100 200 300

-1

no
stim

Stimulus interval from visual stimulus (ms)Fig. 3. The increase in the number of errors from the win to the lose condition (mean± standard error [SE]) following TMS at each stimulus interval (black bar) and withno stimulus (open bar). A: Total number of response errors, B: opposite-type errors, C:
draw-type errors [58].

tion of stereotyped responses, the correct responses would
decrease following single-pulse TMS of this area during
the lose task. We delivered TMS over the left DLPFC
when participants were trying to win or lose and compared the effect of TMS. The number of response errors,
which was calculated from subtraction of the error numbers in the win condition from the numbers in the losecondition, increased when TMS was delivered at around−200 ms stimulus interval in comparison with no stimulation (Fig. 3). In particular, participants tended to show
an increased number of opposite-type errors (i.e., to winerroneously in the lose condition). Taken together, theseresults indicated that the left DLPFC is an essential neuralsubstrate for the inhibition of stereotyped responses.
These studies may indicate a neurophysiological mechanisms underlying stereotyped and nonstereotyped behavior (i.e., flexible and context-dependent behaviors),
which characterize the novices’ and experts’ movement
patterns. Because deactivation of a certain brain structure
was also reported necessary to produce context-dependent
response (i.e., timing and reaction time response to identical visual stimulus) [61], characteristics of brain activities of experts (and novices) should be investigated more
extensively in a variety of perceptual-motor skills to understand the neurophysiological principles of skill acquisition

7. Dynamical Principles of Motor Skill Acquisition
Novices become experts after long-term intensive anddeliberate practice [62]. The process of motor skill acquisition is neither smooth nor linear, but rather discontinuVol.15 No.8, 2011ous and nonlinear. There are several characteristic stages
in skill acquisition [3, 63], and diverse phenomena, suchas sudden improvement of performance, temporary deterioration in a once-acquired skill, or qualitative change
in motor control called automatization, can be observed
during the course of skill acquisition. The dynamical systems model that was originally introduced by Haken [64],has been suggested to have the potential to describe suchdiverse and complex phenomena on variability of motor
performance during the process of motor skill acquisition [5], because nonlinear dynamical systems can showcomplex behavior as it evolves in time.
Recently, Fujii et al. [65] showed that stable bimanual coordination pattern exists in professional drummers
compared to non-drummers during rapid and rhythmic
drumming movements. In their experiment, participants
were asked to hold a drumstick in each hand and performbimanual tapping in an anti-phase pattern as fast and as
accurately as possible for 10 s (Fig. 4A). A clear difference was observed between professional drummers andnon-drummers in the relative phase patterns (φ , the phasedifference between the right hand and the left hand movements when they are plotted on a phase plane consisting of
position and velocity) (Fig. 4B). That is, while the relative
phase of non-drummers showed continuous wrapping or
wandering in the interval between 0◦ to 360◦ , that of theprofessional drummers showed no such phase wrappingor wandering pattern. This occurred because the nondrummers erroneously tapped in succession with the same
hand (i.e., L–R–R–L) although they were asked to tap inan anti-phase pattern (i.e., L–R–L–R) (Fig. 4C). Correspondingly, the non-drummers showed a more variable φpattern than the drummers.

Journal of Advanced Computational Intelligenceand Intelligent Informatics

947

Kudo, K. et al.

Fig. 4. Experimental setting. B: Schematic diagrams of correct anti-phase drumming pattern(upper) and erroneous phase wandering pattern (lower). Left and right hand taps are shownby open and filled squares, respectively. More than one taps of the same hand appearing insuccession (e.g., L-R-R-L) considered an error and as phase wandering. C: Typical examples ofrelative phase (φ ) patterns observed in a professional drummer (red) and non-drummers (blue)
during bimanual anti-phase drumming.

What principles govern over the organization of rhythmic bimanual movements in professional drummers andnon-drummers? To theoretically describe the observed
bimanual coordination pattern in experts and novices,
we formulated a dynamical model by extending HakenKelso-Bunz [66] model as follows:φ̇ = −a sin φ − 2b sin2φ − c sin(φ − ψ )
. . . . . (6)+Δω + d cos φ + Qξt ,where the overdot indicates the derivative of the order parameter φ with respect to time and ξ t denotes a Gaussian
white noise that functions as stochastic force of strength
Q [67]. The terms −a sin φ and −2b sin2φ are referred toas intrinsic dynamics [5] (i.e., spontaneous coordination
tendencies in human bimanual coordination). The terms−c sin(φ − ψ ) and d cos φ are referred to as behavioral information that have a specific parametric influence on theorder parameter dynamics [5, 68–71].
The term Δω is a symmetry breaking parameter that
represents a difference in uncoupled frequency of the individual system components (e.g., asymmetry in maximum tapping frequency between hands) [65, 72]. An increase of Δω breaks the symmetry of the potential function and causes an appearance of running solution or aphase wandering pattern. Accordingly, the bimanual coordination patterns for both professional drummers andnon-drummers were successfully modeled by a nonspecific change in the symmetry breaking parameter, or intrinsic constraint of asymmetry Δω (Figs. 5A, B).
948

The detailed simulation of Eq. (6) revealed an interesting relationship between the parameter Δω and the variability of relative phase (SD φ ). The change in the parameter Δω induced the S-shaped change in the intra-trial
variability (Fig. 5C) and the inverted U-shaped change
in the inter-trial variability (Fig. 5D). These nonlinearchanges in the variability of relative phase result from asaddle node bifurcation, a mechanism by which the attractor layout changes in dynamical systems [73].
Miura et al. [4] also found phase transition phenomena
in street dance movements. In street dance, basic kneebending movements are divided into two patterns: down
and up movements. Down movement flexes the knees on
the beat, while up movement extends the knees on thebeat. Both skilled street dancers and non-dancers, who
had not had the experience of street dance, could perform
relatively stable down movement over a wide range ofmovement frequencies. However, when the non-dancers
were instructed to perform up movement at high movement frequencies such as beyond 120 beats per minute,
unintentional phase transition from up to down movement
occurred. That is, although the non-dancers tried to perform the up movement (knee extension on the beat), they
performed the down movement (knee flexion on the beat)unintentionally during high movement frequency. On thecontrary, skilled street dancers could resist such unintentional phase transition, and could perform more stable
up movement than in non-dancers. These results suggest that intrinsically stable and relatively unstable action-

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.15 No.8, 2011

Neurophysiological and Dynamical Control Principles

8. Conclusions

Fig. 5. A: Schematic diagrams of evolving potential functions of Eq. (6) (a = 1, b = 0, c = 2, ψ = 180◦ , and Δω =0.00, 0.80, and 1.60). B: Typical example of observed andsimulated time evolution of relative phase (φ ) for a drummer(red) and non-drummers (blue). C: Intra-trial variability (SDφ ) as a function of Δω simulated√ by Eq. (6). (a = 1, b = 0,c = 2, ψ = 180◦ , d = 0.8Δω , Q = 20◦ , and Δω is changed
from 0.00 to 4.00 by 0.01.) The observed intra-trial SDφ s for
drummers (N = 8, red) and non-drummers (N = 8, blue) areplotted on the simulated line. The color of each point corresponds to the number of phase wanderings. D: Inter-trial
variability (SD φ ) as a function of Δω simulated in Equation 6. Intra-trial variability shows S-shaped function whileinter-trial variability shows inverted U-shaped function.

In the series of our studies examining the difference between experts and novices we have found that experts’
functional variability or coordination of the executing parameters can drive out erroneous variability typically observed in performance by novices. These results indicatethat experts do not use a unique or single set of parameters
for the tasks, suggesting the necessity of practicing undervarying conditions and requiring varying solutions. We
would like to emphasize the importance of practice for expertization, because growing evidence has suggested thata lot of characteristics in an expert’s brain was formed
through practice or acquired, rather than innate [76, 77].
We also found that stereotyped response (i.e., typical
EMG co-contraction or typical responses based on probability distribution of particular events) can be produced
from motor control strategies to reduce the final output
errors under variable conditions, and activation of particular brain structures are necessary to inhibit stereotyped
responses. In addition, we introduced the dynamical systems approach to describe the process of skill acquisition. This approach should be a promising candidate tounderstand the principles that govern spatiotemporal organization of movements in novices and experts, becausecharacteristic movement patterns specific to novices (i.e.,phase wandering in drumming and phase transition instreet dancing) and experts (i.e., stable movement patterns) can be seamlessly described as the time evolutionof nonlinear and self-organized dynamical systems.
Since motor skill acquisition in humans is a complex phenomena including the change in performance,
movement, and neurophysiological variables such as electromyographic and brain activities [78], interdisciplinary
approach consisting of experimental and theoretical methods are needed to not only understand human motor skill
but also develop an effective practice methods for motor
skill acquisition.
Acknowledgements
We thank Dr. Kozue Sakamoto for her assistance in the preparation of this manuscript. This work was supported by a Grantin-Aid for Scientific Research (No. 21300215 and 20240060) to
K.K.
References:

perception coordination pattern exists as constraints overthe formation of movement patterns, and the intrinsic dynamics is modified, that is, the intrinsically unstable coordination pattern (up movement) become stable in street
dancers by long-term practice over the years.
These studies show that the dynamical systems approach can contribute to understanding the mathematicalprinciples underlying skill acquisition and also suggestthat human motor control and learning can be describedas time-evolution or self-organization of dynamical systems (Kudo et al. [74] and Ohgane et al. [75]).

[1] R. A. Schmidt, H. Zelaznik, B. Hawkins et al., “Motor Output Variability: Theory for the Accuracy of Rapid Motor Acts,” Psychological Review, Vol.86, pp. 415-451, 1979.
[2] A. T. Welford, “Fundamentals of Skill,” Methuen, 1968.
[3] N. A. Bernstein, “On Dexterity and its Development,” in M.
L. Latash and M. T. Turvey, “Dexterity and its development,”
Lawrence Erlbaum, pp. 1-244, 1996.
[4] A. Miura, K. Kudo, T. Ohtsuki et al., “Coordination Modes in Sensorimotor Synchronization of Whole-Body Movement: A Study ofStreet Dancers and Non-Dancers,” Human Movement Science (inpress).
[5] J. A. S. Kelso, “Dynamic Patterns,” MIT Press, 1995.
[6] K. M. Newell and D. M. Corcos, “Variability and Motor Control,”
Human Kinetics, 1993.
[7] S. Sakurai and T. Ohtsuki, “Muscle Activity and Accuracy of Performance of the Smash Stroke in Badminton with Reference to Skill
and Practice,” J. of Sports Sciences, Vol.18, pp. 901-914, 2000.

Vol.15 No.8, 2011

Journal of Advanced Computational Intelligenceand Intelligent Informatics

949

Kudo, K. et al.

[8] Y. Nakayama, K. Kudo, and T. Ohtsuki, “Variability and Fluctuationin Running Gait Cycle of Trained Runners and Non-Runners,” Gait
& Posture, Vol.31, pp. 331-335, 2010.
[9] S. Fujii, K. Kudo, T. Ohtsuki et al., “Tapping Performance andUnderlying Wrist Muscle Activity of Non-Drummers, Drummers,
and the World’s Fastest Drummer,” Neuroscience Letters, Vol.459,
pp. 69-73, 2009.
[10] S. Fujii, K. Kudo, M. Shinya et al., “Wrist Muscle Activity During
Rapid Unimanual Tapping with a Drumstick in Drummers and NonDrummers,” Motor Control, Vol.13, pp. 237-250, 2009.
[11] A. P. Georgopoulos, J. F. Kalaska, and J. T. Massey, “Spatial Trajectories and Reaction Times of Aimed Movements: Effects of Practice, Uncertainty and Change of Target Location,” J. of Neurophysiology, Vol.46, pp. 725-743, 1981.
[12] I. M. Franks and R. B. Wilberg, “The Generation of Movement Patterns During the Acquisition of a Pursuit Tracking Task,” Human
Movement Science, Vol.1, pp. 251-272, 1982.
[13] T. A. Salthouse, “Effects of Practice on a Typing-Like Keying
Task,” Acta Psychologica, Vol.62, pp. 189-198, 1986.
[14] B. Vereijken, R. E. A. Vanemmerik, H. T. A. Whiting et al.,“Free(z)ing Degrees of Freedom in Skill Acquisition,” J. of Motor
Behavior, Vol.24, pp. 133-142, 1992.
[15] K. Kudo, T. Ito, S. Tsutsui et al., “Compensatory Coordination of
Release Parameters in a Throwing Task,” J. of Motor Behavior,
Vol.32, pp. 337-345, 2000.
[16] S. Furuya and H. Kinoshita, “Organization of the Upper Limb
Movement for Piano Key-Depression Differs Between Expert Pianists and Novice Players,” Experimental Brain Research, Vol.185,
pp. 581-593, 2008.
[17] R. Osu, D. W. Franklin, H. Kato et al., “Short- and Long-Term
Changes in Joint Co-Contraction Associated with Motor Learning
as Revealed from Surface EMG,” J. of Neurophysiology, Vol.88,
pp. 991-1004, 2002.
[18] H. Heuer, “Control of the Dominant and Nondominant Hand: Exploitation and Taming of Nonmuscular Forces,” Experimental Brain
Research, Vol.178, pp. 363-373, 2007.
[19] G. P. Van Galen, M. Muller, R. G. J. Meulenbroek et al., “Forearm
EMG Response Activity During Motor Performance in Individuals Prone to Increased Stress Reactivity,” American J. of Industrial
Medicine, Vol.41, pp. 406-419, 2002.
[20] M. Yoshie, K. Kudo, and T. Ohtsuki, “Effects of PsychologicalStress on State Anxiety, Electromyographic Activity, and Arpeggio
Performance in Pianists,” Medical Problems of Performing Artists,
Vol.23, pp. 120-132, 2008.
[21] P. L. Gribble, L. I. Mullin, N. Cothros et al., “Role of Cocontraction in Arm Movement Accuracy,” J. of Neurophysiology, Vol.89,
pp. 2396-2405, 2003.
[22] M. Yoshie, K. Kudo, T. Murakoshi et al., “Music Performance Anxiety in Skilled Pianists: Effects of Social-Evaluative Performance
Situation on Subjective, Autonomic, and Electromyographic Reactions,” Experimental Brain Research, Vol.199, pp. 117-126, 2009.
[23] M. Yoshie, K. Kudo, and T. Ohtsuki, “Motor/Autonomic Stress Responses in a Competitive Piano Performance,” Annals of the New
York Academy of Sciences, Vol.1169, pp. 368-371, 2009.
[24] J. R. Pijpers, R. R. D. Oudejans, F. Holsheimer et al., “AnxietyPerformance Relationships in Climbing: A Process-Oriented Approach,” Psychology of Sport and Exercise, Vol.4, pp. 283-304,
2003.
[25] G. Hajcak, C. Molnar, M. S. George et al., “Emotion Facilitates Action: A Transcranial Magnetic Stimulation Study of Motor Cortex
Excitability During Picture Viewing,” Psychophysiology, Vol.44,
pp. 91-97, 2007.
[26] T. Baumgartner, M. Willi, and L. Jäncke, “Modulation of Corticospinal Activity by Strong Emotions Evoked by Pictures and Classical Music: A Transcranial Magnetic Stimulation Study,” Neuroreport, Vol.18, pp. 261-265, 2007.
[27] D. Schutter, D. Hofman, and J. Van Honk, “Fearful Faces Selectively Increase Corticospinal Motor Tract Excitability: A Transcranial Magnetic Stimulation Study,” Psychophysiology, Vol.45,
pp. 345-348, 2008.
[28] N. A. Bernstein, “The Co-ordination and Regulation of Movement,”
Pergamon Press, 1967.
[29] K. Kudo and T. Ohtsuki, “Adaptive Variability in Skilled Human
Movements,” Information and Media Technologies, Vol.3, pp. 409420, 2008.
[30] E. Todorov and M. I. Jordan, “Optimal Feedback Control as a Theory of Motor Coordination,” Nature Neuroscience, Vol.5, pp. 12261235, 2002.
[31] J. P. Scholz and G. Schoner, “The Uncontrolled Manifold Concept:
Identifying Control Variables for a Functional Task,” Experimental
Brain Research, Vol.126, pp. 289-306, 1999.
[32] M. L. Latash, J. P. Scholz, and G. Schoner, “Toward a New Theoryof Motor Synergies,” Motor Control, Vol.11, pp. 276-308, 2007.

950[33] M. L. Latash, Z. M. Li, and V. M. Zatsiorsky, “A Principle of Error Compensation Studied Within a Task of Force Production by a
Redundant Set of Fingers,” Experimental Brain Research, Vol.122,
pp. 131-138, 1998.
[34] G. A. Arutyunyan, V. S. Gurfinkel, and M. L. Mirskii, “Organization of Movements on Execution by Man of an Exact Postural Task,”
Biophysics, Vol.14, pp. 1162-1167, 1969.
[35] K. P. Kording, S. P. Ku, and D. M. Wolpert, “Bayesian Integrationin Force Estimation,” J. of Neurophysiology, Vol.92, pp. 3161-3165,
2004.
[36] K. P. Kording and D. M. Wolpert, “Bayesian Decision Theory
in Sensorimotor Control,” Trends in Cognitive Sciences, Vol.10,
pp. 319-326, 2006.
[37] D. M. Wolpert, “Probabilistic Models in Human Sensorimotor Control,” Human Movement Science, Vol.26, pp. 511-524, 2007.
[38] D. Kersten, P. Mamassian, and A. Yuille, “Object Perception
as Bayesian Inference,” Annual Review of Psychology, Vol.55,
pp. 271-304, 2004.
[39] D. C. Knill and A. Pouget, “The Bayesian Brain: the Role of Uncertainty in Neural Coding and Computation,” Trends in Neuroscience,
Vol.27, pp. 712-719, 2004.
[40] K. P. Kording and D. M. Wolpert, “Bayesian Integration in Sensorimotor Learning,” Nature, Vol.427, pp. 244-247, 2004.
[41] H. Tassinari, T. E. Hudson, and M. S. Landy, “Combining Priors andNoisy Visual Cues in a Rapid Pointing Task,” J. of Neuroscience,
Vol.26, 10154-63, 2006.
[42] M. Jazayeri and M. N. Shadlen, “Temporal Context Calibrates Interval Timing,” Nature Neuroscience, Vol.13, pp. 1020-1026, 2010.
[43] J. Lopez-Moliner, D. T. Field, and J. P. Wann, “Interceptive Timing:
Prior Knowledge Matters,” J. of Vision, Vol.7, No.11, pp. 1-8, 2007.
[44] M. Miyazaki, D. Nozaki, and Y. Nakajima, “Testing Bayesian Models of Human Coincidence Timing,” J. of Neurophysiology, Vol.94,
pp. 395-399, 2005.
[45] M. Miyazaki, S. Yamamoto, S. Uchida et al., “Bayesian Calibration of Simultaneity in Tactile Temporal Order Judgment,” Nature
Neuroscience, Vol.9, pp. 875-877, 2006.
[46] H. Nakata, K. Inui, Y. Nishihira et al., “Effects of a Go/NoGo
Task on Event-Related Potentials Following Somatosensory Stimulation,” Clinical Neurophysiology, Vol.115, pp. 361-368, 2004.
[47] H. Nakata, K. Inui, T. Wasaka et al., “Somato-Motor Inhibitory Processing in Humans: a Study with MEG and ERP,” European J. of
Neuroscience, Vol.22, pp. 1784-1792, 2005.
[48] R. Simson, H. G. Vaughan, and W. Ritter, “Scalp Topography ofPotentials in Auditory and Visual-Discrimination Tasks,” Electroencephalography and Clinical Neurophysiology, Vol.42, pp. 528-535,
1977.
[49] K. Kudo and T. Ohtsuki, “Functional Modification of AgonistAntagonist Electromyographic Activity for Rapid Movement Inhibition,” Experimental Brain Research, Vol.122, pp. 23-30, 1998.
[50] A. R. Aron, P. C. Fletcher, T. Bullmore et al., “Stop-Signal Inhibition Disrupted by Damage to Right Inferior Frontal Gyrus in Humans,” Nature Neuroscience, Vol.6, pp. 115-116, 2003.
[51] G. D. Logan and W. B. Cowan, “On the Ability to Inhibit Thought
and Action: A Theory of an Act of Control,” Psychological Review,
Vol.91, pp. 295-327, 1984.
[52] J. Chikazoe, S. Konishi, T. Asari et al., “Activation of Right Inferior Frontal Gyrus During Response Inhibition Across Response
Modalities,” J. of Cognitive Neuroscience, Vol.19, pp. 69-80, 2007.
[53] H. Nakata, K. Sakamoto, A. Ferretti et al., “Somato-Motor Inhibitory Processing in Humans: an Event-Related Functional MRI
Study,” Neuroimage, Vol.39, pp. 1858-1866, 2008.
[54] N. Kida, S. Oda, and M. Matsumura, “Intensive Baseball Practice
Improves the Go/NoGo Reaction Time, but not the Simple Reaction
Time,” Cognitive Brain Research, Vol.22, pp. 257-264, 2005.
[55] H. Nakamoto and S. Mori, “Effects of Stimulus-Response Compatibility in Mediating Expert Performance in Baseball Players,” Brain
research, Vol.1189, pp. 179-188, 2008.
[56] F. Di Russo, F. Taddei, T. Apnile et al., “Neural Correlates ofFast Stimulus Discrimination and Response Selection in Top-Level
Fencers,” Neuroscience Letters, Vol.408, pp. 113-118, 2006.
[57] H. Kadota, Y. Nakajima, M. Miyazaki et al., “Anterior Prefrontal
Cortex Activities During the Inhibition of Stereotyped Responses
in a Neuropsychological Rock-Paper-Scissors Task,” Neuroscience
Letters, Vol.453, pp. 1-5, 2009.
[58] H. Kadota, H. Sekiguchi, S. Takeuchi et al., “The Role of the
Dorsolateral Prefrontal Cortex in the Inhibition of Stereotyped
Responses” Experimental Brain Research, Vol.203, pp. 593-600,
2010.
[59] H. Kashima and M. Kato, “Tests for Frontal Function-Patternof Frontal Dysfunction and its Assessment,” Shinkei Kenkyu no
Shinpo, Vol.37, pp. 93-110, 1993.

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.15 No.8, 2011

Neurophysiological and Dynamical Control Principles[60] M. Matsubara, S. Yamaguchi, J. Xu et al., “Neural Correlates for
the Suppression of Habitual Behavior: a Functional MRI Study,” J.
of Cognitive Neuroscience, Vol.16, pp. 944-954, 2004.
[61] K. Kudo, M. Miyazaki, T. Kimura et al., “Selective Activation andDeactivation of the Human Brain Structures Between Speeded andPrecisely Timed Tapping Responses to Identical Visual Stimulus:
an fMRI Study,” Neuroimage, Vol.22, pp. 1291-1301, 2004.
[62] K. A. Ericsson, R. T. Krampe, and C. Teschromer, “The Role ofDeliberate Practice in the Acquisition of Expert Performance,” Psychological Review, Vol.100, pp. 363-406, 1993.
[63] P. M. Fitts and M. I. Posner, “Human Performance,” Brooks Cole,
1967.
[64] H. Haken, “Synergetics, an Introduction: Non-Equilibrium Phase
Transitions and Self-Organization in Physics, Chemistry and Biology,” Springer, 1977.
[65] S. Fujii, K. Kudo, T. Ohtsuki et al., “Intrinsic Constraint of Asymmetry Acting as a Control Parameter on Rapid, Rhythmic Bimanual Coordination: A Study of Professional Drummers and NonDrummers,” J. of Neurophysiology, Vol.104, pp. 2178-2186, 2010.
[66] H. Haken, J. A. S. Kelso, and H. Bunz, “A Theoretical Model ofPhase Transitions in Human Hand Movements,” Biological Cybernetics, Vol.51, pp. 347-356, 1985.
[67] G. Schöner, H. Haken, and J. A. S. Kelso, “A Stochastic Theory of
Phase Transitions in Human Hand Movement,” Biological Cybernetics, Vol.53, pp. 247-257, 1986.
[68] J. Scholz and J. A. S. Kelso, “Intentional Switching Between Patterns of Bimanual Coordination Depends on the Intrinsic Dynamics
of the Patterns,” J. of Motor Behavior, Vol.22, pp. 98-124, 1990.
[69] J. A. S. Kelso, J. P. Scholz, and G. Schöner, “Dynamics Governs
Switching Among Patterns of Coordination in Biological Movement,” Physics Letter A, Vol.134, pp. 8-12, 1988.
[70] H. J. de Poel, C. L. Peper, and P. J. Beek, “Laterally Focused Attention Modulates Asymmetric Coupling in Rhythmic Interlimb Coordination,” Psychological Research, 2006.
[71] E. L. Amazeen, P. G. Amazeen, P. J. Treffner et al., “Attention and
Handedness in Bimanual Coordination Dynamics,” J. of Experimental Psychology: Human Perception and Performance, Vol.23,
pp. 1552-1560, 1997.
[72] J. J. Jeka and J. A. S. Kelso, “Manipulating Symmetry in the Coordination Dynamics of Human Movement,” J. of Experimental Psychology: Human Perception and Performance, Vol.21, pp. 360-374,
1995.
[73] S. H. Strogatz, “Nonlinear Dynamics and Chaos: With Applications
to Physics, Biology, Chemistry, and Engineering,” Perseus Books,
1994.
[74] K. Kudo, H. Park, B. A. Kay et al., “Environmental Coupling Modulates the Attractors of Rhythmic Coordination,” J. of Experimental
Psychology: Human Perception and Performance, Vol.32, pp. 599609, 2006.
[75] K. Ohgane, S. Ei, K. Kudo et al., “Emergence of Adaptability
to Time Delay in Bipedal Locomotion,” Biological Cybernetics,
Vol.90, pp. 125-132, 2004.
[76] T. F. Munte, E. Altenmuller, and L. Jancke, “The Musician’s Brain
as a Model of Neuroplasticity,” Nature Reviews Neuroscience,
Vol.3, pp. 473-478, 2002.
[77] S. L. Bengtsson, Z. Nagy, S. Skare et al., “Extensive Piano Practicing Has Regionally Specific Effects on White Matter Development,”
Nature Neuroscience, Vol.8, pp. 1148-1150, 2005.
[78] H. Nakata, M. Yoshie, A. Miura et al., “Characteristics of the Athletes’ Brain: Evidence from Neurophysiology and Neuroimaging,”
Brain Research Reviews, Vol.62, pp. 197-211, 2010.
[79] W. Fujisaki, S. Shimojo, M. Kashino et al., “Recalibration of Audiovisual Simultaneity,” Nature Neuroscience, Vol.7, pp. 773-778,
2004.
[80] J. V. Hanson, J. Heron, and D. Whitaker, “Recalibration of Perceived Time Across Sensory Modalities,” Experimental Brain Research, Vol.185, pp. 347-352, 2008.
[81] J. Vroomen, M. Keetels, B. de Gelder et al., “Recalibration of Temporal Order Perception by Exposure to Audio-Visual Asynchrony,”
Cognitive Brain Research, Vol.22, pp. 32-35, 2004.
[82] D. H. Arnold and K. Yarrow, “Temporal Recalibration of Vision,”
Proc. of the Royal Society B: Biological sciences, Vol.278, pp. 535538, 2011.
[83] Y. Sato, T. Toyoizumi, and K. Aihara, “Bayesian Inference Explains
Perception of Unity and Ventriloquism Aftereffect: Identificationof Common Sources of Audiovisual Stimuli,” Neural Computation,
Vol.19, pp. 3335-3355, 2007.

Vol.15 No.8, 2011

Name:
Kazutoshi Kudo

Affiliation:
Department of Life Sciences, Graduate Schoolof Arts and Sciences, The University of Tokyo

Address:
3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan

Brief Biographical History:
2002-2003 Visiting Researcher, Center for the Ecological Study ofPerception & Action, University of Connecticut, USA
2007- Assistant Professor, The University of Tokyo
2011- Associate Professor, The University of Tokyo

Main Works:• “Intrinsic Constraint of Asymmetry Acting as a Control Parameter on
Rapid, Rhythmic Bimanual Coordination: A Study of Professional
Drummers and Non-Drummers,” J. of Neurophysiology, Vol.104,
pp. 2178-2186, 2010.
• “Environmental Coupling Modulates the Attractors of Rhythmic
Coordination,” J. of Experimental Psychology: Human Perception and
Performance, Vol.32, pp. 599-609, 2006.
• “Selective Activation and Deactivation of the Human Brain Structures
Between Speeded and Precisely Timed Tapping Responses to Identical
Visual Stimulus: an fMRI Study,” NeuroImage, Vol.22, pp. 1291-301,
2004.

Membership in Academic Societies:• The Japan Neuroscience Society (JNS)• Japan Society of Physical Education, Health and Sport Sciences• Japanese Society of Sport Psychology (JSSP)

Name:
Makoto Miyazaki

Affiliation:
Research Institute of Kochi University of Technology

Address:
185 Miyanokuchi, Tosayamada, Kochi 782-8502, Japan

Brief Biographical History:
2002- National Rehabilitation Center for Persons with Disabilities
2005- Waseda University
2009- Kochi University of Technology

Main Works:• “Bayesian Calibration of Simultaneity in Tactile Temporal Order
Judgment,” Nature Neuroscience, Vol.9, No.7, pp. 875-877, Jul. 2006.
• “Testing Bayesian Models of Human Coincidence Timing,” J. of
Neurophysiology, Vol.94, No.1, pp. 395-399, Jul. 2005.
• “The “Cutaneous Rabbit” Hopping Out of the Body,” J. of Neuroscience,
Vol.30, No.5, pp. 1856-1860, Feb. 2010.

Membership in Academic Societies:• Society for Neuroscience (SfN)• The Japan Neuroscience Society (JNS)• Japanese Society for Sport Psychology (JSSP)

Journal of Advanced Computational Intelligenceand Intelligent Informatics

951

Kudo, K. et al.

Name:

Name:

Hirofumi Sekiguchi

Shinya Fujii

Affiliation:

Affiliation:

Sports Management Program, Faculty of Business and Information Sciences, Jobu University

Research Fellow of the Japan Society for the
Promotion of Science (PD), Music and Neuroimaging Laboratory, Department of Neurology at Beth Israel Deaconess Medical Center andHarvard Medical School

Address:

Address:

634-1 Toyatsukamachi, Isesaki, Gunma 372-8588, Japan

330 Brookline Avenue - Parmer 127, Boston, MA 02215, USA

Brief Biographical History:

Brief Biographical History:

2001- Research Fellow, Department of Rehabilitation for Movement
Functions, Research Institute of National Rehabilitation Center for Persons
with Disabilities
2004- Research Fellow, Department of Rehabilitation for Sensory
Functions, Research Institute of National Rehabilitation Center for Persons
with Disabilities
2008- Lecturer, Consolidated Research Institute for Advanced Science andMedical Care, Waseda University
2009- Associate Professor, Sports Management Program, Faculty ofBusiness and Information Sciences, Jobu University

2007- Graduate School of Human and Environmental Studies, Kyoto
University
2007- Research Fellow of the Japan Society for the Promotion of Science(DC1)
2010- Graduate School of Education, The University of Tokyo
2010- Research Fellow of the Japan Society for the Promotion of Science(PD)
2011- Beth Israel Deaconess Medical Center and Harvard Medical School

Main Works:• “TMS-Induced Artifacts on EEG can be Reduced by Rearrangement of
the Electrode’s Lead Wire Before Recording,” Clinical Neurophysiology,
Vol.122, pp. 984-990, 2011.
• “Modulation of Corticospinal Excitability During Lengthening andShortening Contractions in the First Dorsal Interosseus Muscle of
Humans,” Experimental Brain Research, Vol.178, pp. 374-384, 2007

Membership in Academic Societies:• Society for Neuroscience (SfN)• Japanese Society of Physical Fitness and Sports Medicine (JSPFSM)• Japanese Society of Biomechanics (JSB)• Japanese Society Clinical Neurophysiology (JSCN)

Main Works:• “Synchronization Error of Drum Kit Playing with a Metronome at
Different Tempi by Professional Drummers,” Music Perception, Vol.28,
No.5, pp. 191-503, 2011.
• “Intrinsic Constraint of Asymmetry Acting as a Control Parameter on
Rapid, Rhythmic Bimanual Coordination: A Study of Professional
Drummers and Non-drummers,” J. of Neurophysiology, Vol.104, No.4,
pp. 2178-2186, 2010.
• “Tapping Performance and Underlying Wrist Muscle Activity of
Non-Drummers, Drummers, and the World’s Fastest Drummer,”
Neuroscience Letters, Vol.459, No.2, pp. 69-73, 2009.

Membership in Academic Societies:• The Society for Neuroscience (SfN)• The Organization for Human Brain Mapping (HBM)• The Japan Society for Physical Education, Health and Sport Sciences

Name:
Name:

Hiroshi Kadota

Akito Miura

Affiliation:
Affiliation:

Graduate School of Education, The University ofTokyo

Graduate School of Arts and Sciences, The University of Tokyo

Address:
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Address:

Brief Biographical History:

3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan

2005- Research Institute, National Rehabilitation Center for Persons with
Disabilities
2008- Shibaura Institute of Technology
2009- The University of Tokyo

Brief Biographical History:

Main Works:• “The Role of the Dorsolateral Prefrontal Cortex in the Inhibition of
Stereotyped Responses,” Experimental Brain Research, Vol.203,
pp. 593-600, 2010.

Membership in Academic Societies:• Society for Neuroscience (SfN)• The Japan Neuroscience Society (JNS)• Japanese Society of Sport Psychology (JSSP)

952

2009- M.S., Department of Life Sciences, Graduate School of Arts andSciences, The University of Tokyo
2011- Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for
JSPS Fellows

Main Works:• “Coordination Modes in Sensorimotor Synchronization of Whole-Body
Movement: A Study of Street Dancers and Non-Dancers,” Human
Movement Science (in press).

Membership in Academic Societies:• Japan Society of Training Science for Exercise and Sport• Japan Society of Physical Education, Health and Sport Sciences• Society for Neuroscience (SfN)

Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.15 No.8, 2011

Neurophysiological and Dynamical Control Principles

Name:
Michiko Yoshie

Affiliation:
Japan Society for the Promotion of Science(JSPS)
Research Fellow, Clinical Imaging Sciences
Centre, Brighton and Sussex Medical School,
University of Sussex

Address:
Falmer, Brighton, BN1 9RR, United Kingdom

Brief Biographical History:
2008- Research Fellow, Japan Society for the Promotion of Science
2010- Visiting Research Fellow, Brighton and Sussex Medical School,
University of Sussex
2011 Ph.D, Department of Life Sciences, Graduate School of Arts andSciences, The University of Tokyo

Main Works:• “Music Performance Anxiety in Skilled Pianists: Effects ofSocial-Evaluative Performance Situation on Subjective, Autonomic, andElectromyographic Reactions,” Experimental Brain Research, Vol.199,
pp. 117-126, Aug. 2009.

Membership in Academic Societies:• Performing Arts Medicine Association (PAMA)• The Japanese Psychological Association (JPA)• Japan Music Education Society (JMES)

Name:
Hiroki Nakata

Affiliation:
Faculty of Sport Sciences, Waseda University

Address:
2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan

Brief Biographical History:
2007- Postdoctoral Fellow, Japan Society for the Promotion of Science
2009- Assistant Professor, Faculty of Sport Sciences, Waseda University

Main Works:• “Characteristics of the Athletes’ Brain: Evidence from Neurophysiology
and Neuroimaging,” Brain Res Review, Vol.62, pp. 197-211, 2010.

Membership in Academic Societies:• The Japanese Society of Clinical Neurophysiology (JSCN)• The Japan Biomagnetism and Bioelectromagnetics Society (JBBS)

Vol.15 No.8, 2011

Powered by TCPDF (www.tcpdf.org)

Journal of Advanced Computational Intelligenceand Intelligent Informatics

953