RESEARCH ARTICLE

Learning an environment–actor coordination skill: visuomotortransformation and coherency of perceptual structure

Young U. Ryu Æ John J. Buchanan

Received: 13 April 2007 / Accepted: 6 May 2009 / Published online: 24 May 2009

� Springer-Verlag 2009

Abstract The coordination dynamics of learning were

examined in a visuomotor tracking task. Participants pro-

duced rhythmic elbow flexion–extension motions to learn a

visually defined 90� relative phase tracking pattern with an

external sinusoidal signal. There were two visuomotor

transformation groups, a correct feedback group and a

mirrored feedback group with feedback representing the

elbow’s motion transformed by 180�. In Experiment 1, the

to-be-tracked signal and the participant’s motion signal

were superimposed within a single window display. In

Experiment 2, the to-be-tracked signal and participant’s

signal were presented in separate windows. Before day 1

practice and 24 h after day 2 practice, participants

attempted visually defined 0�, 45�, 90�, 135�, and 180�relative phase tracking patterns either with or without

visual feedback of the arm’s motion. Before practice, only

the 0� and 180� tracking patterns were stable. Practice led

to a decrease in phase error toward the required 90� relative

phase pattern with a corresponding increase in stability in

both the experiments. No effect of visual transformation on

performance emerged during practice in the single window

task, but did emerge in the two window task. The one

window training facilitated transfer to the four unpracticed

relative phase patterns, whereas the two window training

display only facilitated transfer performance to a single

unpracticed relative phase pattern. These findings suggest

that the perceptual structure determined the degree of

learning and transfer and interacted with the visuomotor

transformation. The present findings are discussed with

reference to how the visual display constrains the coher-

ency of independent signals with regard to learning and

transfer and the role of perceptual discrimination processes

linked to transfer.

Keywords Coordination � Visuomotor tracking �Perception–action coupling � Feedback � Display �Coherency � Perceptual motor learning

Introduction

The ability to coordinate the movement of a limb or limbs

with an external event is essential for the performance of

many everyday activities. For instance, visuomotor tracking

is an essential skill for catching a ball, for driving a car, or for

dancing with a partner. For experienced individuals, the

actions just listed may seem trivial. Of course, experience

comes from practice with a task and practice is associated

with a change in behavior referred to as learning. The current

experiment attempted to identify changes in the coordination

dynamics of a practiced visuomotor tracking pattern in order

to identify the learning processes that underlie the develop-

ment of visuomotor tracking skills in general.

Extensive research has shown that in-phase (relative

phase / = 0�) coordination patterns in bimanual (Kelso

1984; Kelso et al. 1986), single limb (Kelso et al. 1991),

and visuomotor tracking (Wimmers et al. 1992; Peper

and Beek 1998) tasks are more stable than anti-phase

Y. U. Ryu � J. J. Buchanan

Human Performance Laboratories,

Department of Health and Kinesiology,

Texas A&M University, College Station, USA

Present Address:Y. U. Ryu (&)

Motor Control Development Laboratory,

Department of Biokinesiology and Physical Therapy,

University of Southern California,

Los Angeles, CA 90033, USA

e-mail: ryuyounguk@gmail.com

123

Exp Brain Res (2009) 196:279–293

DOI 10.1007/s00221-009-1847-9

(/ = 180�) coordination patterns. Thus, a series of

experiments employing quite different experimental tasks,

bimanual, single limb, and visuomotor tracking, reveals

shared dynamic principles that constrain coordination. The

differential stability between anti-phase and in-phase

coordination patterns has been modeled at the level of the

relative phase dynamics and at the level of the individual

component actions and their coupling (Haken et al. 1985;

Schoner et al. 1986). A key feature of the modeling was

that the in-phase and anti-phase patterns were interpreted

as stable fixed-point attractors of the collective variable

relative phase (/). Between these stable attractive states of

the model, a repeller at the relative phase value of 90�exists and will push the system towards the attractor at

either 0� or 180�. Both bimanual coordination research

(Zanone and Kelso 1992; Lee et al. 1995) and visuomotor

tracking research (Wilson et al. 2005) have shown that

when participants attempted a 90� relative phase pattern

there was a tendency to be drawn to either in-phase or anti-

phase. This implies that the ability to produce a stable 90�relative phase pattern in many skills must be learned

(Zanone and Kelso 1992; Lee et al. 1995; Buchanan 2004).

To examine the learning of a 90� relative phase pattern

in a bimanual task, Zanone and Kelso (1992) first per-

formed a pre-practice scan of the relative phase landscape

by requiring participants to produce 13 different relative

phases from 0� to 180� in 15� increments. The scanning

runs revealed that attempts at producing a 90� relative

phase pattern were attracted to either 0� or 180�. This

finding was consistent with the model of the relative phase

dynamics developed by Haken et al. (1985). After several

days of practice, post-practice scanning runs revealed that

the practiced relative phase pattern of 90� was produced

without attraction towards 0� or 180�. Thus, practice

resulted in the formation of a stable attractor at the relative

phase value of 90�. Further, the post-practice scanning

results demonstrated that practice of the 90� relative phase

pattern affected not only the required coordination pattern,

but also the entire coordination landscape (Schoner et al.

1992; Zanone and Kelso 1992, 1997). The ability to learn a

specific visuomotor tracking pattern, such as a 90� relative

phase between a limb and external signal, however, has not

been investigated from any theoretical viewpoint to our

knowledge. A primary goal of the current experiment was

to determine if learning a 90� visuomotor tracking pattern

shared similar dynamics with the learning of a 90� relative

phase bimanual pattern. Specifically, the experiment was

designed to determine (1) if a novel tracking pattern could

be learned and produced with and without visual feedback

of the arm’s motion, and (2) whether or not learning a

novel tracking pattern influenced performance (i.e., sta-

bility and accuracy) of the intrinsic visuomotor tracking

patterns of 0� and 180� (Wimmers et al. 1992).

Wilson et al. (2005) examined unimanual visuomotor

tracking when participants had to establish a 0�, 90� or

180� phase relationship between a moving cursor and arm

motion represented as a moving cursor in a visual display.

As expected, the 90� tracking pattern was not stable

whereas the 0� and 180� were stable, and Wilson et al.

concluded that this pattern of performance was consistent

with the Haken et al. (1985; Schoner et al. 1986) model of

bimanual coordination. This indicates that producing a

stable 90� relative phase visuomotor tracking pattern would

require practice (Liao and Jagacinski 2000). What might

influence the ability to learn a 90� visuomotor tracking

pattern? Research has demonstrated that transformed visual

feedback of the arm’s motion can influence the accuracy

and stability of visuomotor tracking. For example,

Roerdink et al. (2005) required participants to track an

external signal under three feedback conditions: no feed-

back, correct feedback (feedback representing the actual

wrist motion), and mirrored feedback (wrist motion trans-

formed by 180�). Results revealed that the accuracy and

stability of tracking was the worst in the no feedback

condition, suggesting the importance of feedback in

tracking performance. The transformed visual feedback did

not influence the in-phase pattern, but stabilized the less

stable anti-phase pattern when the visual transformation

produced a perceived in-phase pattern (Bogaerts et al.

2003; Roerdink et al. 2005; Wilson et al. 2005).

The transformation results in the two visual signals

moving in the same direction and a coherently grouped

visual motion structure is created that helps to stabilize the

anti-phase pattern. This suggests that coherency in the

visual structure can play an important role in detecting and

recognizing a particular relative phase pattern (Bogaerts

et al. 2003; Roerdink et al. 2005; Bingham et al. 1999; Zaal

et al. 2000), and therefore may play an important role in

learning a novel relative phase tracking pattern and transfer

of skill after learning. However, it must be noted that while

the presentation of 90� and 180� tracking patterns as a 0�pattern improves performance, the stability of the 90� and

180� patterns does not match the 0� pattern (Wilson et al.

2005). The coherency created by the visual display does

not completely override the role of limb proprioceptive

information. With regard to learning and transfer of visu-

omotor tracking skills, it is necessary to understand how

transformed feedback interacts with the coherency of the

visual display in order to understand the interaction

between visual and proprioceptive feedback. Since the 90�pattern is already an unstable pattern, transformation of the

arm’s motion by 180� will not result in a more stable

representation of the action as does the transformation of

the arm’s motion during anti-phase tracking. Thus, both

correct and mirrored displays will represent an unstable

relative phase pattern.

280 Exp Brain Res (2009) 196:279–293

123

Another factor that may influence learning and transfer

of visuomotor tracking skills is the distance between the

signals. Research has demonstrated that tracking perfor-

mance is most stable when the to-be-tracked signal in the

form of an open square and a cursor representing arm

motion are superimposed. When the two signals are not

superimposed, performance error increases and grows as

the gap between the to-be-tracked signal and arm’s signal

increases (Reed et al. 2003). This finding indicates that the

coherency created by transformed visual feedback in

tracking tasks could be influenced by signal separation

distance. In other words, the larger the separation in the

signals, the less of an impact on the performance

improvements of 180� and 90� patterns presented as 0�patterns. This possible interaction between transformed

visual feedback and signal separation distance and its

impact on learning and transfer of visuomotor tracking

skills was also examined in the current study.

Experiment 1 addressed the learning of a 90� relative

phase visuomotor tracking pattern under a correct and

mirrored (arm’s motion transformed by 180�) presentation

of the arm’s motion in the visual display. In this experi-

ment, the two signals were superimposed and presented in

the same window. It is predicted that both the correct and

mirrored representations will allow the 90� relative phase

pattern to be learned and reproduced with or without visual

feedback of the arm’s motion in the display. However,

when combining the bimanual work and visuomotor

transformation work, two possible outcomes may be

observed after training: First, since transformed feedback

can stabilize a 180� tracking pattern, practicing a 90� pat-

tern with transformed feedback may actually lead to an

increase in the stability of the 180� pattern when produced

with transformed feedback. This finding would be in con-

tradiction to the bimanual work of Zanone and Kelso

(1992). Second, practicing the 90� pattern with correct

feedback will destabilize the 180� tracking pattern when

produced with correct feedback. This finding would be

consistent with the bimanual work and the visuomotor

transformation work since the 180� tracking pattern with

correct feedback typically emerges as the least stable pat-

tern under correct and transformed conditions. Experiment

2 addressed the interaction between signal-distance in the

training display and the visual coherency created by the

transformed visual feedback. If the transformation effect on

tracking performance is independent of the distance

between signals, presenting the tracked signal and arm’s

signal in separate displays will not influence learning or

transfer. However, if the transformation effect is linked to

the superposition of the signals, visual coherency will be

reduced in the mirrored condition across separate display

windows, and result in decreased performance gains and

reduced transfer compared to the single window display.

Experiment 1

Materials and methods

Subjects

Twelve students (6 males and 6 females, mean age 22.2

years) volunteered to participate in the study. The IRB

board of Texas A&M University approved the experi-

mental protocol and consent form and all participants

voluntarily signed the consent form prior to the experiment.

All participants were self-reported right-handers and had

normal or corrected-to-normal vision.

Apparatus

Participants sat on a height-adjustable chair and faced a

computer monitor at a distance of 0.76 m. The partici-

pant’s right elbow was comfortably placed on a table in

a supine position, with the longitudinal axis of their

upper arm at about 45� to the surface of the table.

Participants wore a wrist orthosis to secure their wrist

joint in parallel with the longitudinal axis of the forearm.

In the visuomotor tracking literature, many experiments

are performed without vision of the moving limb (e.g.,

Bogaerts et al. 2003; Ceux et al. 2003; Roerdink et al.

2005; Wilson et al. 2005; Byblow et al. 1995) and with

vision of the moving limb (e.g., Buekers et al. 2000;

Carnahan et al. 1996; Foulkes and Miall 2000; Liao and

Jagacinski 2000). We chose to block the view of the

participant’s arm with a wall (0.84 m 9 0.48 m) moun-

ted on the table between the participants’ head and arm

(Fig. 1a). A computer monitor was used to display the

external tracking signal, the participant’s own tracking

motion, and an angle–angle plot of the tracking signal

versus the limb’s motion. The sinusoidal tracking signal

was produced with a Tektronix� AFG320 arbitrary

function generator. The oscillation frequency was set at

0.8 Hz with a total of 8 cycles in a trial. The oscillation

frequency of 0.8 Hz was chosen because previous

tracking studies have shown that multi-stability was

observed around 0.8 Hz (e.g., 0.67 Hz in Ceux et al.

2003; 1 Hz in Buekers et al. 2000). An OPTOTRAK�

3020 3D camera system recorded the position of infrared

light emitting diodes (IREDs). Three IREDs were

mounted as follows: (1) attached to a dowel held in the

hand, (2) lateral epicondyle of the elbow, and (3) on the

table corresponding to the dowel’s height when the

participant’s hand was extended to maximum extension

along the table (Fig. 1a). The participant’s elbow angular

motion was represented by the change in the angle hdefined by the dowel’s position with regard to the IRED

mounted on the table (Fig. 1a).

Exp Brain Res (2009) 196:279–293 281

123

Protocol

Each participant was randomly assigned to an experimental

group based on the presentation of the trace representing

the arm’s motion in the training display (Fig. 1b): (1) a

correct feedback group (COR, 3 males and 3 females) with

visual feedback of the elbow’s rotation representing the

movement as made, and (2) a mirrored feedback group

(MIR, 3 males and 3 females) with the elbow’s angle signal

was transformed by 180�. For the correct feedback group,

rotation of the forearm upward (flexion) produced an

upward motion of the elbow signal on the screen, and for

the mirrored feedback group, rotation of the forearm

upward (flexion) produced a downward motion of the

elbow signal on the screen. The angle h approached 0�when the elbow was fully extended. The angle increased

positively when flexing the elbow in the correct feedback

condition. In the mirrored feedback condition, the angle

decreased negatively when flexing the elbow since the

online visual feedback was multiplied by -1 during a trial.

The required joint amplitude corresponding to the external

sinusoidal wave was 61�. Participants were required to

learn to rhythmically coordinate the flexion–extension

movement of the elbow with the external signal so the

elbow’s motion lagged the tracked signal at a 90� relative

phase relationship (Fig. 1b). The external signal and the

online visual feedback of the elbow’s rotation were both

presented as sine waves that overlapped in the workspace.1

The workspace was 14 cm (length) 9 4.5 cm (height) and

presented two peak-to-peak cycles of the external signal.

Participants were given four tracking trials to familiarize

themselves with the production of the joint motion dis-

played on the computer screen. Participants were asked to

track the external signal with both a 0� relative phase

(2 trials) and a 180� relative phase (2 trials) visuomotor

pattern. For these trials, the oscillation frequency was set at

0.5 Hz with a total of 8 cycles in a trial. This oscillation

frequency was slower than the one used in the experimental

sessions (0.8 Hz) to reduce the practice effect on tracking

before the experimental scanning run. Participants were

informed of the visuomotor transformation relationship

during these familiarization trials.

Scanning sessions

A pre-practice scanning session was conducted prior to the

practice of the 90� relative phase pattern on day 1, and a

post-practice scanning session was conducted on day 3

without any preceding practice trials. The scanning ses-

sions consisted of 30 trials for 5 different relative phase

patterns: 0�, 45�, 90�, 135�, and 180� with each relative

phase pattern defined by the relationship between the

elbow signal and the external signal on the monitor

(Fig. 1c). The scanning sessions consisted of a visual and

proprioceptive feedback condition (VPF) and a proprio-

ceptive feedback condition only (PF). In the VPF condi-

tion, the elbow angle trajectory was plotted along with the

signal to be tracked, and in the PF condition, the elbow

angle trajectory was not plotted with the tracking signal.

The VPF condition (3 trials) was conducted first and the PF

condition (3 trials) was conducted second in every scan-

ning session.

Before performing each relative phase pattern, the

required relative phase pattern was shown to a participant.

1

2 3θ

Wall

0.76 m

Monitor

A

0 45 ° 90° 135° 180°C

BRequired pattern Visuomotor transformation

Arm

flex.

ext.

COR MIR

time

°

Fig. 1 a Experimental setup showing the participant relative to the

wall blocking vision of the arm and the monitor displaying the

signals. b The plot on the left portrays the representation of the to-be-

learned 90� pattern as viewed by the participants. The black curverepresents the external signal and the gray curve represents the signal

that a participant is supposed to produce. The middle plot and plot on

the right display correct (COR) and mirrored (MIR) arm motion

(dashed curves). c The five target patterns used in the pre- and post-

practice scans are shown

1 Typically, the signal to be tracked and the arm’s motion in

visuomotor tracking tasks are presented in the form of cursors or lines

continuously moving on a monitor. We chose to present the signals as

continuous sine waves. Our pre-practice scanning procedure revealed

intrinsic dynamics consistent with other tasks that have used

continuous cursors to examine tracking (Roerdink et al. 2005; Wilson

et al. 2005) or discrete light flashes to examine learning in bimanual

tasks (Amazeen 2002; Zanone and Kelso 1992, 1997). This suggests

that our display is not introducing oddities inconsistent with other

visual presentation techniques and conveys similar information to the

participant just in a different format.

282 Exp Brain Res (2009) 196:279–293

123

The external signal was represented as a black sine wave

while the signal that the participants were supposed to

reproduce with elbow motion was represented with as a red

sine wave (Fig. 1c). The assigned participant’s signal lag-

ged the external signal in the 45�, 90�, and 135� relative

phase patterns. Participants were instructed to watch the

pattern represented by the two sine waves and to try to

reproduce the pattern when they performed the scanning

trials. After watching one of the relative phase patterns,

participants immediately performed three consecutive trials

for the assigned relative phase pattern. The five relative

phase patterns were performed in a random order.

Practice sessions

The practice session was initiated after the scanning trials.

Practice sessions 1 (day 1) and 2 (day 2) consisted of 6

trials per block that yielded 36 practice trials per session (a

total of 72 practice trials). Participants were required to

coordinate rhythmic elbow flexion–extension movements

to a continuous sinusoidal wave at a 90� relative phase

relationship. The elbow angle trajectory was provided

concurrently with the external sinusoidal signal in every

trial. After an odd numbered trial, both the re-play of the

performed trial and its angle–angle plot (tracking signal vs.

elbow angle) were provided as terminal feedback. Partici-

pants were informed that a circle in the angle–angle plot

represented the required 90� relative phase relationship

between the tracking signal and the participant’s elbow

motion. A variety of tasks have shown that the angle–angle

display as a form of terminal feedback acts as a beneficial

form of KR (knowledge of result) to represent performance

changes associated with the learning of relative phase

patterns (Lee et al. 1995; Buchanan et al. 2007).2

Data analysis

Prior to any data analysis, the 3D IRED trajectories were

filtered with a dual-pass Butterworth Filter with a cutoff

frequency of 10 Hz. The filtered data were used to compute

the elbow joint angle. The first cycle of motion was

dropped and considered as an adaptation phase so that 7

cycles were analyzed from each trial.

A continuous relative phase was computed to charac-

terize the spatiotemporal tracking relationship between the

external signal and the elbow angle trajectory. For both the

tracking signal (hexternal) and elbow angle (helbow), indi-

vidual phase angles were computed for every sampled

point i in a trial, hi ¼ tan�1½ðdxi=dtÞ=xi�, with xi the nor-

malized position and dxi/dt the normalized instantaneous

velocity. For each sampled point i in a trial, the continuous

relative phase was computed as /i = hexternal - helbow.

Mean resultant vectors were computed from the observed

unit vectors (x, y) of the /i values for each scanning trial

and practice trial (Burgess-Limerick et al. 1991). The mean

resultant vectors were used to compute a mean phase angle

(/obs) and a magnitude of circular variance (uniformity). A

value of /obs = 0� represented in-phase tracking and a

value of /obs = 180� represented anti-phase tracking. In

the pre- and post-scanning procedures, there were five

required relative phase values, /req = 0�, -45�, -90�,

-135�, and 180�. The target relative phase in the practice

condition was /req = -90�. For each scanning trial and

practice trial, an absolute phase error (/AE = |/req - /obs|)

was computed and used to evaluate visuomotor tracking

accuracy as a function of practice. The circular variance

falls within the range of 0–1 with 1 representing perfect

uniformity. In order to submit the circular variance data to

inferential tests based on standard normal theory (Mardia

1972), the circular variance score was transformed to the

range 0 to ? as follows, s0 = -2 logn(1 - S0)0.5. S0

denotes the measure of circular variance on the interval

[0, 1] with s0 the transformed circular variance (TCV)

measure that was submitted to statistical analysis. This

TCV measure was treated as an estimate of the variability

in tracking performance. An increase in the uniformity

measure with practice will indicate an increase in the

stability of tracking performance.

Statistical analysis

The ANOVAs used to analyze the pre-practice, practice,

and post-practice data each contained different factors and

have been reported in each section separately. Duncan’s

multiple range tests (p \ 0.05) were used to analyze all

post hoc main effects, and simple main effect tests

(p \ 0.05) were performed to reveal differences when

significant interactions were present. Partial eta-squared

values (g2p) were computed and have been reported with

every significant F value.

Results

Pre-practice scanning

The individual trial relative phase means (/obs) from the

pre-practice scanning session have been displayed in

Fig. 2. With visual and proprioceptive feedback, the most

2 The bimanual learning work of Zanone and Kelso (1992) utilized

cycle-to-cycle relative phase plots with both a graphical and

numerical representation of the to-be-learned pattern as a means of

KR. We know of no studies that have directly compared angle–angle

plots with cycle-to-cycle relative phase plots to determine the most

relevant form of display with regard to learning such coordination

patterns. The literature clearly shows that both forms of KR are

beneficial.

Exp Brain Res (2009) 196:279–293 283

123

clustering occurred for the 0� relative phase pattern fol-

lowed by the 180� relative phase pattern for both the cor-

rect and mirrored feedback groups (Fig. 2a). With

proprioceptive feedback only, a dense clustering of

observed mean relative phases only emerged in the correct

feedback group for the 0� tracking pattern (Fig. 2b).

The absolute phase error values (/AE) and TCV values

from the pre-practice scanning sessions were analyzed in a

2 Visuomotor Transformation (COR, MIR) 9 2 Feedback

(VPF, PF) 9 5 Pattern (0�, 45�, 90�, 135�, 180�) ANOVA

with the last two variables repeated measures. A significant

main effect of Pattern was found in the /AE data

(F(4,40) = 4.49, p \ 0.01, g2p ¼ 0:31). Post hoc tests

revealed that the /AE values were significantly smaller

when performing the 0�, 45�, and 180� patterns compared

to the 90� and 135� tracking patterns, with the largest /AE

associated with the 135� pattern (Fig. 3a). The PF condi-

tion (/AE = 70.7�) was characterized by significantly lar-

ger phase error than the VPF condition (/AE = 29.6�)

(F(1,10) = 14.4, p \ 0.01, g2p ¼ 0:59). The analysis of the

/AE data also revealed a significant Visuomotor Trans-

formation 9 Feedback interaction (F(1,10) = 5.21,

p \ 0.05, g2p ¼ 0:32). Post hoc tests revealed two main

findings. First, the tracking performance of the mirrored

feedback group (/AE = 86.8�) was significantly less

accurate than the correct feedback group (/AE = 22.5�)

when tracking in the PF condition. Second, tracking per-

formance in the mirrored feedback group was significantly

less accurate in the PF condition (/AE = 86.8�) compared

to the VPF condition (/AE = 54.6�).

A significant main effect of Pattern was found in the

TCV data (F(4,40) = 12.61, p \ 0.01, g2p ¼ 0:56) (Fig. 3b).

Post hoc tests showed that the 0� pattern was more stable

(larger TCV values) than the other four patterns. The 180�

A

0 45 90 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180CORMIR

B

RP pattern (deg)0 45 90 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180

Fig. 2 The individual trial observed mean relative phase values

(/obs) from the pre-practice scanning trials are plotted for the VPF (a)

and PF (b) conditions as a function of required relative phase: 0�,

-45�, -90�, -135� and ±180�. The negative values represent that

the task required elbow motion to lag the external signal in both the

correct and mirrored conditions

A

0 45 90 135 180

Abs

. Err

or (

deg)

0

20

40

60

80

100

B

TC

V

0

1

2

3

4

5

C

RP pattern (deg)

TC

V

0

1

2

3

4

5

CORMIR

0 45 90 135 180

0 45 90 135 180

Fig. 3 Pre-practice scanning data. a Mean absolute phase error (/AE)

and b mean TCV values are plotted as a function of required target

pattern. c Mean TCV values are plotted as a function of visuomotor

transformation and required target pattern. The error bars represent

the standard error of the between subject variability

284 Exp Brain Res (2009) 196:279–293

123

pattern was more stable than the 90� and 135� patterns with

no difference in stability between the 45�, 90�, and 135�patterns. The analysis of the TCV data also revealed a

significant Visuomotor Transformation 9 Pattern interac-

tion (F(4,40) = 3.73, p \ 0.05, g2p ¼ 0:27) (Fig. 3c). Tests

of the interaction revealed that within the mirrored feed-

back group the 0� and 180� tracking patterns were more

stable than the other three patterns, and within the correct

feedback group the 0� pattern was more stable than the

other four patterns. The correct feedback group tracked at

45� and 90� with more stability than the mirrored feedback

group, while the mirrored feedback group tracked at 180�with more stability than the correct feedback group.

Overall, the 0� and 180� patterns were produced with the

most accuracy in the pre-practice trials, and the 90�tracking pattern had the second largest absolute phase error

and was not stably produced in the either the VPF or PF

conditions.

Practice

The absolute phase error values (/AE) and TCV values

from the practice trials were analyzed in 2 Visuomotor

Transformation 9 2 Day (day 1, day 2) 9 6 Block (1, 2, 3,

4, 5, 6) ANOVA with the last two factors repeated. Practice

produced a significant reduction in absolute phase error

from day 1 (/AE = 14�) to day 2 (/AE = 10�)

(F(1,10) = 14.14, p \ 0.01, g2p ¼ 0:59). A main effect of

Block was found in the /AE data (F(5,50) = 3.32, p \ 0.05,

g2p ¼ 0:25), and post hoc tests (p \ 0.05) revealed that the

/AE values from blocks 1 to 4 (B1, /AE = 14.3�; B2,

/AE = 12.9�; B3, /AE = 12.8�; B4, /AE = 12.1�) were

significantly larger than those from blocks 5 (/AE = 10.8�)

and 6 (/AE = 10.2�). The analysis of the TCV data

revealed a significant increase in the stability of visuomotor

tracking performance from day 1 (TCV = 3.67) to day 2

(TCV = 3.82) (F(1,10) = 61.87, p \ 0.01, g2p ¼ 0:86). A

significant main effect of Block was also found in the TCV

data (F(5,50) = 2.92, p \ 0.05, g2p ¼ 0:23), and post hoc

tests revealed that the TCV values from blocks 1–3 (B1,

TCV = 3.48; B2, TCV = 3.59; B3, TCV = 3.6) were

significantly smaller than the TCV values from blocks 4–6

(B4, TCV = 3.69; B5, TCV = 3.68; B6, TCV = 3.68).

Pre-practice scanning versus post-practice scanning

Figure 4 displays the individual trial relative phase means

(/obs) from the post-practice scanning session. The clus-

tering in the post-practice session was much tighter than in

the pre-practice session for both visuomotor transformation

groups in both the VPF and PF conditions (compare Fig. 4

to Fig. 2). To test for performance differences in the pre-

and post-practice scanning sessions, the /AE and TCV

values were analyzed in a 2 Visuomotor Transformation 9

2 Feedback 9 2 Session (pre-practice, post-practice) 9 5

Pattern ANOVA with the last three variables repeated

measures.

The analysis of the /AE data revealed a significant

increase in tracking accuracy from the pre-practice session

(/AE = 50.2�) to the post-practice session (/AE = 26.7�)

(F(1,10) = 122.94, p \ 0.01, g2p ¼ 0:92). Tracking perfor-

mance was more accurate in the VPF condition

(/AE = 21.8�) than in the PF condition (/AE = 55.3�)

(F(1,10) = 20.16, p \ 0.01, g2p ¼ 0:69). A main effect of

Pattern was found in the /AE data (F(4,40) = 5.21,

p \ 0.01, g2p ¼ 0:34). Post hoc tests (p \ 0.05) revealed

that the 0� (/AE = 27.6�), 180� (/AE = 34.8�), and 45�(/AE = 32.9�) patterns were produced with less error than

the 90� (/AE = 44.9�) and 135� (/AE = 52�) tracking

patterns. A Visuomotor Transformation 9 Pattern interac-

tion effect was found in the /AE data (F(4,40) = 3.33,

p \ 0.05, g2p ¼ 0:25) (Fig. 5a). Tests of the interaction

revealed that the 0� and 45� tracking patterns were more

accurate than the other three patterns in the correct feed-

back group. However, no significant differences were

found among the five tracking patterns in the mirrored

feedback group. The correct feedback group produced

A

0 45 9 0 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180CORMIR

B

RP pattern (deg)

0 45 90 135 180R

elat

ive

Pha

se (

deg)

-180

-135

-90

-45

0

45

90

135

180

Fig. 4 The individual trial observed mean relative phase values

(/obs) from the post-practice scanning trials are plotted for the VPF

(a) and PF (b) conditions as a function of required relative phase: 0�,

-45�, -90�, -135� and ±180�

Exp Brain Res (2009) 196:279–293 285

123

significantly less error for the 0� and 45� tracking patterns

than the mirrored feedback group.

Overall, tracking performance was more stable with lar-

ger TCV values in the post-practice session (TCV = 3.57)

compared to the pre-practice session (TCV = 2.84)

(F(1,10) = 107.61, p \ 0.01, g2p ¼ 0:92). Across both ses-

sions, stability of tracking performance was less in the PF

condition (TCV = 2.93) compared to VPF condition

(TCV = 3.48) (F(1,10) = 12.84, p \ 0.01, g2p ¼ 0:56). A

main effect of Pattern was found in the TCV data

(F(4,40) = 19.79, p \ 0.01, g2p ¼ 0:66). Post hoc tests of the

Pattern effect revealed that the 0� relative phase pattern

(TCV = 4.05) was the most stable tracking pattern, fol-

lowed by the 180� tracking pattern (TCV = 3.37). There

were no differences in the stability of the 45� (TCV = 2.94),

90� (TCV = 2.86), and 135� (TCV = 2.82) patterns, which

were all less stable than the 0� and 180� patterns. Simple

main effects tests of a significant Visuomotor Transforma-

tion 9 Pattern interaction (F(4,40) = 5.51, p \ 0.01, g2p ¼

0:36) revealed two main findings (Fig. 5b): First, within the

correct feedback group the 0� tracking pattern was the most

stable, and within the mirrored feedback group the 0� and

180� tracking patterns were the most stable. Second, the

mirrored feedback group was more variable when producing

the 45� relative phase pattern and more stable when pro-

ducing the 180� relative phase pattern compared to the cor-

rect feedback group.

The primary hypothesis put forth was that a significant

change in the accuracy and stability of the 90� tracking

pattern would emerge with practice and be consistent with

the bimanual findings reported by Zanone and Kelso (1992,

1997). The above analyses revealed significant Session and

Pattern effects, but not Session 9 Pattern interactions

localizing a large change in performance associated with

the 90� relative phase tracking pattern. The above findings

suggest two possible hypotheses regarding performance

change that may explain the lack of the interaction: (1)

practice of the 90� pattern did not alter performance, as

suggested by the Pattern effects which basically mirror the

pre-practice Pattern effects; or (2) practice of the 90� pat-

tern produced equivalent changes in performance in all

five tracking patterns, which the Session effects suggest

(compare Fig. 4 and Fig. 2). To test these two hypotheses,

pair-wise t tests (p \ 0.05) of the pre- and post-practice

absolute phase error data (/AE) and TCV data were con-

ducted separately for each pattern. The t tests of the /AE

data revealed that absolute phase error associated with the

45�, 90�, 135�, and 180� tracking patterns was significantly

smaller in the post-practice session compared to the pre-

practice session (ts(142) [ 2.87, ps \ 0.01) (Fig. 6a). The

t tests of the TCV data revealed that the post-practice

tracking performance for each tracking pattern was less

variable than the pre-practice performance (ts(142) \-2.97, ps \ 0.01) (Fig. 6b). Thus, the data support the

second hypothesis regarding the impact of practicing the

90� relative phase tracking pattern.

Discussion

Overall, the learning of the 90� relative phase tracking

pattern was not influenced by the mirrored condition. In

fact, practice with either correct or mirrored visual infor-

mation benefited learning, retention, and unexpectedly

transfer to all unpracticed patterns, with or without con-

current feedback. The positive transfer was found in both

the relative phase error and TCV data. Clearly, such gen-

eral transfer across unpracticed patterns is not consistent

with the general predications of the Haken et al. (1985)

model. To the best of our knowledge, tracking studies

usually display the to-be-tracked signal as a cursor moving

on the screen. Within the current task, we displayed the to-

be-tracked signal as two cycles of an actual sine wave.

Thus, the general transfer effect may have arisen from

either the use of sine waves as the display signals or the one

window display. To examine the impact of the single

window display on the general transfer effect, we ran a

second experiment wherein the to-be-tracked signal and

A

0 45 90 135 180

Abs

. Err

or (

deg)

0

10

20

30

40

50

60

70CORMIR

B

RP pattern (deg)

TC

V

0

1

2

3

4

5

0 45 90 135 180

Fig. 5 a Mean absolute error (/AE) and b mean TCV values are

plotted as a function of visuomotor transformation and required target

pattern. The error bars represent the standard error of the between

subject variability

286 Exp Brain Res (2009) 196:279–293

123

participants signal were presented in separate windows. If

the general transfer effect is the result of the display being

sine waves, then the same general transfer phenomenon

should emerge when the signals are displayed in separate

windows. However, if the general transfer effect is the result

of the coherency of the visual motion structure (Bogaerts

et al. 2003; Roerdink et al. 2005) and the superimposition of

the signals (Reed et al. 2003), then general transfer should

not occur, and transfer results more consistent with the

Haken et al. (1985) model should emerge.

Experiment 2

Materials and methods

Subjects

Twelve students (6 males, 6 females, mean age 23.4 years)

volunteered to participate in this study. The protocol and

consent forms were approved by the IRB board of Texas

A&M University and all participants signed a consent form

prior to the experiment. All participants were self-reported

right-handers and had normal or corrected-to-normal

vision.

Apparatus and data analysis

The same experimental apparatus, data collection proce-

dures, and data analyses used in Experiment 1 were

employed in this experiment.

Protocol

The task and procedures were identical to those used in

Experiment 1 except that the to-be-tracked signal and

participants signal were presented in separate windows

instead of superimposed in a single window. The display

windows on the monitor were 14 cm long 9 4.5 cm high.

The to-be-tracked signal was presented in the top window

and the participant’s motion signal was presented in the

bottom window. The bottom of window 1 and the top of

window 2 were 0.5 cm apart and this placed the peaks and

valleys of the two signals approximately 9.0 cm apart. A

9 cm distance was the largest employed by Reed et al.

(2003) when the to-be-tracked signal and the signal rep-

resenting arm motion were presented as cursors in the

visual display.

Statistical analysis

The ANOVAs used to analyze the pre-practice, practice,

and post-practice data sets each contained different factors

and have been reported in each section separately. Dun-

can’s multiple range tests (p \ 0.05) were used to analyze

all post hoc main effects, and simple main effect tests

(p \ 0.05) were performed to reveal differences when

significant interactions were present. Partial eta-squared

values (g2p) were computed and have been reported with

every significant F value.

Results

Pre-practice scanning

The clustering shown in Fig. 7 is consistent with that

shown in Fig. 2 and demonstrates a strong attraction

towards only the 0� and 180� relative phase patterns prior

to practice with the to-be-learned 90� relative phase. The

relative phase error data (/AE) and TCV data from the

pre-practice scanning sessions were analyzed in a 2

Visuomotor Transformation 9 2 Feedback 9 5 Pattern

ANOVA. A significant main effect of pattern was found in

the /AE data (F(4,40) = 7.64, p \ 0.0001, g2p ¼ 0:43) and

post hoc tests revealed that the 0� and 180� patterns were

produced with smaller error than the other patterns and that

the 90� target pattern was produced with significantly lar-

ger error than other patterns (Fig. 8a, pre-practice). Over-

all, /AE was larger in the PF condition (/AE = 63.4�)

A

0 45 90 135 180

Abs

. Err

or (

deg)

0

20

40

60

80

100 Pre-practicePost-practice

B

RP pattern (deg)

TC

V

0

1

2

3

4

5

* * * *

0 45 90 135 180* * * **

Fig. 6 Pre- and post-practice scanning data. a Mean absolute errors

(/AE) and b mean TCV values are plotted as a function of required

target pattern and scanning session. The error bars represent standard

error of between subject variability. Asterisks represent statistical

significance

Exp Brain Res (2009) 196:279–293 287

123

compared to the VPF (/AE = 37.7�) condition (F(1,10) =

16.16, p \ 0.01, g2p ¼ 0:62), and larger in the mirror visual

transformation (/AE = 61.8�) group compared to the cor-

rect (/AE = 39.2�) group (F(4,40) = 9.06, p \ 0.05,

g2p ¼ 0:47). The analysis of the TCV values found a

significant main effect of Pattern (F(1,10) = 13.42,

p \ 0.0001, g2p ¼ 0:57), and post hoc tests revealed that the

TCV values associated with the 0� phase pattern were

significantly larger (most stable) than the other patterns,

with the TCV values associated with the 90� and 135�phase patterns significantly smaller (least stable) than the

other phase patterns (Fig. 8b, pre-practice).

Practice

Overall, performance improved and stabilized with the two

window display used in this experiment. The /AE and TCV

values were analyzed in a 2 Visual Transformation 9 2

Day 9 6 Block ANOVA with repeated measures on block

and day. The analysis of /AE revealed a significant reduction

in tracking error from practice day 1 (/AE = 25.9�) to

practice day 2 (/AE = 15.4�) (F(1,10) = 23.8, p \ 0.001,

g2p ¼ 0:70), with more accurate tracking performance

emerging in the correct (/AE = 17.8�) versus mirrored

(/AE = 23.6�) visual transformation condition (F(1,10) =

5.04, p \ 0.05, g2p ¼ 0:36). Tracking performance also

became more stable with practice with larger TCV values

during day 2 (TCV = 3.10) compared to day 1 (TCV =

2.72) (F(1,10) = 23.03, p \ 0.001, g2p ¼ 0:7). A Day 9

Block interaction (F(5,50) = 3.81, p \ 0.01, g2p ¼ 0:28) was

also found in the TCV data. Post hoc tests found larger TCV

values (more stable tracking) in day 2 practice blocks 1–6

(TCV [ 3.03) compared to day 1 (TCV \ 2.97). Within

practice day 1, smaller TCV values were found in blocks 1–5

(TCV \ 2.72) compared to block 6 (TCV = 2.97). TCV

values were smaller in block 4 (TCV = 2.92) compared to

blocks 3 (TCV = 3.2) and 5 (TCV = 3.24) during practice

day 2.

Pre-practice scanning versus post-practice scanning

As in Experiment 1, the clustering was tighter around the

90� relative phase pattern following 2 days of practice

(Fig. 9). The issue is whether or not the clustering around

A

0 45 90 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180COR

MIR

B

RP pattern (deg)

0 45 90 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180

Fig. 7 The individual trial observed mean relative phase values

(/obs) from the pre-practice scanning trials are plotted for the VPF (a)

and PF (b) conditions as a function of required relative phase: 0�,

-45�, -90�, -135� and ±180�. The negative values represent that

the task required elbow motion to lag the external signal

A

0 45 90 135 180

Abs

. Err

or (

deg)

0

20

40

60

80

100Pre-practicePost-practice

B

RP pattern (deg)T

CV

0

1

2

3

4

5

* *

0 45 90 135 180

Fig. 8 Pre- and post-practice scanning data. a Mean absolute errors

(/AE) and b mean TCV values are plotted as a function of required

target pattern and scanning session. The error bars represent standard

error of between subject variability. Asterisks represent statistical

significance

288 Exp Brain Res (2009) 196:279–293

123

the non-practiced patterns changed significantly. The pre-

and post-practice /AE data and TCV data were analyzed in

a 2 Session 9 2 Visual Transformation 9 2 Feedback 9 5

Pattern repeated measures ANOVA.

Overall, /AE values decreased from the pre-practice

(/AE = 50.6�) to post-practice (/AE = 32.9�) session

(F(1,10) = 22.5, p \ 0.001, g2p ¼ 0:69) were larger in the

mirrored (/AE = 51.1�) compared to the correct visual

transformation (/AE = 32.4�) condition (F(1,10) = 7.14,

p \ 0.0001, g2p ¼ 0:41), and larger without visual feedback

in the PF (/AE = 53.3�) compared to the VPF

(/AE = 30.2�) condition (F(1,10) = 19.68, p \ 0.01,

g2p ¼ 0:66). Relative phase error varied significantly

between the five tracking patterns (F(4,40) = 8.53,

p \ 0.0001, g2p ¼ 0:46) and a significant Pattern 9 Session

interaction (F(4,40) = 3.78, p \ 0.01, g2p ¼ 0:27) was found

(Fig. 8a). Post hoc tests revealed a significant decrease in

/AE values from the pre- to post-practice sessions for the

target pattern of 90� and the unpracticed pattern of 45�(Fig. 8a). The TCV values overall increased significantly

(less variability) from the pre-practice (TCV = 3.01) to

post-practice session (TCV = 3.42) (F(1,10) = 10.95,

p \ 0.01, g2p ¼ 0:52), and varied significantly between the

five phase patterns (F(4,40) = 42.8, p \ 0.0001, g2p ¼ 0:81).

Post hoc tests of the Pattern effect found that the TCV

values for the 0� (TCV = 3.9), 180� (TCV = 3.5), and 45�(TCV = 3.1) patterns were larger than the TCV values of

the 90� (TCV = 2.7) and 135� (TCV = 2.7) patterns.

Retention of the practiced 90� relative phase as a function

of window display

In this section, an analysis of the /AE values and TCV

values from the day 3 retention trials for the 90� relative

phase pattern is undertaken with a 2 Window (Exp 1, Exp

2) 9 2 Visual Transformation 9 2 Feedback repeated

measures ANOVA. In the retention data, relative phase

error in the one window display (/AE = 32�) was not

significantly different from the relative phase error in the

two window display (/AE = 36�) for the required 90�relative phase pattern (p = 0.66). What did influence rel-

ative phase error was the nature of the feedback, with less

error when both visual and proprioceptive feedback (PVF)

were available (/AE = 21�) compared to when visual

feedback (PF) was not available (/AE = 47�)

(F(1,20) = 8.65, p \ 0.01, g2p ¼ 0:3). Variability in perfor-

mance based on the TCV values was influenced by the

number of windows (F(1,20) = 5.6, p \ 0.05, g2p ¼ 0:22)

and by the nature of the feedback (F(1,20) = 15.1,

p \ 0.001, g2p ¼ 0:43), with a significant interaction

occurring between these two variables (F(1,20) = 12.92,

p \ 0.01, g2p ¼ 0:39). Post hoc tests of the interaction

found that tracking performance at the required 90� relative

phase pattern was the most stable in the one window

condition when both proprioceptive and visual feedback

were present (1 window PVF TCV = 3.81; 1 window PF

TCV = 2.72; 2 window PVF TCV = 2.89; and 2 window

PF TCV = 2.85).

Discussion

Performance of the to-be-learned 90� pattern improved

with practice in both the correct and mirrored visual con-

ditions, with the improvements larger in the correct con-

dition as predicted. Even with the difference in conditions,

the separate window presentation did not remove the

learning opportunity associated with the feedback displays.

However, the two window display reduced the general

transfer effect to only one unpracticed pattern, the 45�pattern. The lack of a reduction in phase error across three

of the four unpracticed patterns strongly suggests that the

nature of the sine wave display in and of itself did not

produce the general transfer finding in Experiment 1. The

smaller performance improvements in the mirrored

A

0 45 90 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180CORMIR

B

RP pattern (deg)

0 45 90 135 180

Rel

ativ

e P

hase

(de

g)

-180

-135

-90

-45

0

45

90

135

180

Fig. 9 The individual trial observed mean relative phase values

(/obs) from the post-practice scanning trials are plotted for the VPF

(a) and PF (b) conditions as a function of required relative phase: 0�,

-45�, -90�, -135� and ±180�. The negative values represent that

the task required elbow motion to lag the external signal

Exp Brain Res (2009) 196:279–293 289

123

condition together with the transfer results demonstrate that

the separation of the two signals decreased the visual

coherency of the display.

General discussion

The pre-practice results from the current experiments

produced three consistent findings with previous research

on the transformation of visual feedback. First, in the one

window display (Experiment 1), the mirrored feedback

group produced a more stable 180� tracking pattern than

the correct feedback group, and this supports the inter-

pretation that the transformed feedback produced a more

coherent visual motion structure defined by the 0� phase

relationship (Bogaerts et al. 2003; Roerdink et al. 2005).

However, in the two window display, there was no

advantage in the mirrored feedback group regarding the

180� tracking pattern. This indicates that the separation of

the signals reduced the strength of the visual coherence

created by the overlap in motion direction between the two

signals. Second, the transformed visual feedback did not

destabilize the production of the mirrored feedback group’s

0� tracking pattern in either experiment. This finding aligns

with other research demonstrating that participants ignore

visual feedback when the external signal and propriocep-

tive information coincide (Roerdink et al. 2005). Third, the

90� relative phase visuomotor tracking pattern was not

stable prior to practice, indicating that stabilizing a 90�visuomotor tracking pattern with a transformation that

provides a 0� relative phase representation between the

signal and arm’s motion, and learning a 90� visuomotor

tracking pattern are distinct process (Wilson et al. 2005).

Visuomotor transformation and visual coherency

in the enhancement of the 90� tracking pattern

The finding that the 90� tracking pattern was not stable

prior to practice is consistent with the bimanual studies

using a scanning procedure (Zanone and Kelso 1992, 1997)

and supports the conclusion that the 90� tracking pattern

did not correspond to an attractor of the visuomotor

tracking dynamics before practice. The level of perfor-

mance accuracy and the stability of the 90� tracking pattern

improved significantly across practice sessions and practice

blocks in both the one and two window display tasks. The

comparison of the retention data for the 90� relative phase

pattern from the two tasks also demonstrates the impor-

tance of the signal presentation. Although the relative

phase error was not significantly smaller in the one window

task compared to the two window task, the TCV values

were significantly smaller. Retention of the 90� pattern was

most stable in the single window display in the PVF (vision

and proprioception) condition, with no difference in the

stability of the 90� pattern between the one window PF

(proprioception only) condition and both the PVF and PF

conditions in the two window case. The advantage in

tracking stability emerged when the two signals were

superimposed visually and not separated. The advantage

does not emerge without visual feedback. This indicates

that signal presentation during training is important in

creating a global coherent visual structure that can influ-

ence the performance stability in dynamic perception–

actions tasks.

With the one window display, the correct feedback

group was less variable in performing the 90� relative

phase tracking pattern compared to the mirrored feedback

group before practice. However, no significant visuomotor

transformation effects were found in the practice sessions

as a function of day or block. The two window display,

however, resulted in poorer tracking accuracy in the mir-

rored group compared to the correct group during practice.

Why did the visuomotor transformation only affect learn-

ing in the two window display? The mirrored feedback in

this task did not convert the 90� relative phase pattern to a

more stable pattern such as 0�. Instead, the transformation

converted the 90� relative phase pattern to a 270� pattern,

which is a repeller just as the 90� pattern. In the one

window display, the superposition of the signals allowed

participants to more easily discriminate the mismatch

between the arm’s visual feedback representation and the

relative phase representation required by the task. The two

window display reduced visual coherency and weakened

the discrimination process making it harder for participants

to overcome the mismatch between the arm’s visual feed-

back representation and the required relative phase.

Modulation of the attractor landscape with practice

The dynamic pattern theory predicts that learning will take

the form of a phase transition, whereby a repeller will be

transformed into an attractor with practice (Schoner et al.

1992). This transition will occur when the to-be-learned

environmentally specified pattern does not coincide with a

preexisting stable attractor in the system’s coordination

landscape (Schoner et al. 1992; Zanone and Kelso 1992).

Characteristic features of a phase transition in learning will

be found in an increase in accuracy of the newly defined

pattern and an increase in the stability of the newly defined

pattern (Schoner et al. 1992; Zanone and Kelso 1992).

Overall, findings from both experiments regarding learning

of the 90� relative phase pattern are consistent with the

dynamical systems theoretical interpretation (Schoner et al.

1992) and with previous experimental results (Zanone and

Kelso 1992, 1997; Fontaine et al. 1997; Buchanan 2004;

Buchanan et al. 2007).

290 Exp Brain Res (2009) 196:279–293

123

Although good agreement between previous work and

the Haken et al. (1985) bimanual model was found with

regard to leaning the 90� pattern, the general transfer to

four unpracticed patterns in Experiment 1 raises some

issues of how far the analogy can be drawn between

visuomotor tracking and this model (Wimmers et al.

1992; Roerdink et al. 2005). For example, Russell and

Sternad (2001) argue that the bidirectional coupling in

the HKB model is inappropriate for unimanual visuo-

motor tracking skills, since only the participant adjusts

and the target oscillation is independent of these

adjustments. The display in the visual tracking paradigm

may allow the participant to overcome errors and phase

differences and override the entrainment strategies that

characterize interlimb coordination in many tasks (Rus-

sell and Sternad 2001). With the two window display,

the general transfer effect to the four unpracticed pat-

terns was eliminated, and only transfer to the unpracticed

pattern of 45� emerged. Byblow et al. (1995) found that

0� and 180� tracking patterns were more stable when

the curser to be tracked moved continuously across the

screen versus a discrete cursor only showing at the left-

most and the rightmost positions of the screen. The

differences in the discrete versus continuous cursor dis-

play may be analogous to our one and two window

displays. The sine wave presentation of the signals dur-

ing training provided a trail of visual information over

two complete cycles of motion. In the study by Byblow

et al. (1995), the continuous cursor may be viewed as

providing more information with which to regulate

tracking. In the current tasks, the sine wave display may

be viewed as possibly providing more information than a

continuously moving cursor. The extensive information

provided when training with the sine wave display in the

single window task may have increased the coherency of

the visual motion structure in the display more so than a

continuously moving dot does in comparison to a dis-

crete pulsed display. Moreover, 2 days of exposure to

such a rich display may have facilitated an improvement

in perceptual discrimination (or resolution) processes in

general (Gibson and Gibson 1955), and not just with the

to-be-learned pattern. Thus, the creation of the coherent

visual structure resulted in the development of a possible

perceptual discrimination capacity allowing for general

transfer with the integrated one window display in the

current study. The two window display, however,

reduced performance during practice, decreased stability

during retention, and significantly altered transfer per-

formance. The overlap in the single window provided a

more coherently grouped visual structure than the two

window display, just as the continuously moving cursor

provided a more coherently grouped visual structure than

the discrete cursor display.

Neural areas associated with visuomotor tracking

Neuroimaging studies have revealed a variety of neural

substrates involved in visuomotor skills (Alexander and

Crutcher 1990; Grafton et al. 2001). For example, the pre-

supplementary motor area has been shown to be associated

with encoding of the visuomotor relationship (Sakai et al.

1999), and the supplementary motor area has been shown

to be active in the initiation and guidance of visuomotor

tracking movements (Picard and Strick 2001). Imaging

studies have also demonstrated that basal ganglia lesions in

monkeys impair visuomotor tracking (Bowen 1969), and

have revealed a role for the basal ganglia in sensorimotor

adaptation processes early in training with a transformed

visuomotor condition (Seidler et al. 2006). In a tracking

task with compatible or incompatible visuomotor feedback,

Grafton et al. (2001) observed an increase in activity in the

pre-central gyrus, with maximal activity in the arm motor

area during compatible tracking and maximal activity in

the putative frontal eye fields during incompatible tracking.

Transfer to the compatible condition following training

with the incompatible condition resulted in a decrease in

activity in the putative frontal eye fields and an increase in

activity in the arm motor areas in the Grafton et al. (2001)

study. The current experiment also required learning of an

incompatible visuomotor display, and based on both per-

ceptual judgment tasks (Bingham et al. 1999; Zaal et al.

2000) and visuomotor tracking tasks (Bogaerts et al. 2003;

Roerdink et al. 2005) one that may have been more

incompatible than the task examined by Grafton et al.

(2001). A speculative link to the current experiments may

be that greater putative frontal eye field activity was

present when learning the 90� tracking pattern. This greater

activity may be associated with the transfer that was

observed to the other patterns, especially the 45� and 135�patterns. In other words, an increase in activity in the

putative frontal eye fields may be associated with percep-

tual discrimination processes in visuomotor tracking which

might account for the overall transfer performance in the

current task.

Conclusion

The present experiment revealed similarities and differ-

ences in the processes associated with learning a 90� rel-

ative phase pattern in bimanual and visuomotor tracking

tasks. The most striking difference was the degree of

transfer to other patterns following practice with a single

visuomotor tracking pattern that seemed to be dependent

on the presentation of the signals. This difference in

transfer performance between the one and two window

tasks raises the issue of the generality of the learning

Exp Brain Res (2009) 196:279–293 291

123

extension of the Haken et al. (1985) model, especially in

light of the strong similarities drawn between bimanual

coordination and visuomotor tracking with regard to the 0�and 180� relative phase patterns. The findings from the

current set of experiments join a growing body of research

demonstrating that visual displays can enhance motor

performance in a variety of tasks, e.g., bimanual coordi-

nation tasks (Mechsner et al. 2001; Tomatsu and Ohtsuki

2005; Hurley and Lee 2006; Kovacs et al. 2009) and rapid

aiming tasks (Fernandez and Bootsma 2008; Kovacs et al.

2008). What the current results suggest is that performance

advantages linked to visual displays arise from the global

structure of the display and the degree of visual coherency

created by this global structure.

Acknowledgments The authors would like to thank Dr. G.P.

Bingham and two other anonymous reviewers for their comments on

previous versions of this manuscript.

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