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To transfer or not to transfer? Kinematics and laterality quotient predict
interlimb transfer of motor learning
Hannah Z. Lefumat,
1
Jean-Louis Vercher,
1
R. Chris Miall,
2
Jonathan Cole,
3
Frank Buloup,
1
Lionel Bringoux,
1
Christophe Bourdin,
1
and Fabrice R. Sarlegna
1
1
Aix-Marseille University, Centre National de la Recherche Scientifique, ISM UMR 7287, Marseille, France;
2
School of
Psychology, University of Birmingham, Birmingham, United Kingdom; and
3
Clinical Neurophysiology, Poole Hospital, and
School of Psychology, Bournemouth University, Poole, United Kingdom
Submitted 29 July 2015; accepted in final form 30 August 2015
Lefumat HZ, Vercher JL, Miall RC, Cole J, Buloup F,
Bringoux L, Bourdin C, Sarlegna FR. To transfer or not to transfer?
Kinematics and laterality quotient predict interlimb transfer of motor
learning. J Neurophysiol 114: 2764–2774, 2015. First published
September 2, 2015; doi:10.1152/jn.00749.2015.—Humans can re-
markably adapt their motor behavior to novel environmental condi-
tions, yet it remains unclear which factors enable us to transfer what
we have learned with one limb to the other. Here we tested the
hypothesis that interlimb transfer of sensorimotor adaptation is deter-
mined by environmental conditions but also by individual character-
istics. We specifically examined the adaptation of unconstrained
reaching movements to a novel Coriolis, velocity-dependent force
field. Right-handed subjects sat at the center of a rotating platform and
performed forward reaching movements with the upper limb toward
flashed visual targets in prerotation, per-rotation (i.e., adaptation), and
post-
rotation tests. Here only the dominant arm was used during adaptation
and interlimb transfer was assessed by comparing performance of the
nondominant arm before and after dominant-arm adaptation. Vision
and no-vision conditions did not significantly influence interlimb
transfer of trajectory adaptation, which on average was significant but
limited. We uncovered a substantial heterogeneity of interlimb trans-
fer across subjects and found that interlimb transfer can be qualita-
tively and quantitatively predicted for each healthy young individual.
A classifier showed that in our study, interlimb transfer could be
predicted based on the subject’s task performance, most notably motor
variability during learning, and his or her laterality quotient. Positive
correlations suggested that variability of motor performance and
lateralization of arm movement control facilitate interlimb transfer.
We further show that these individual characteristics can predict the
presence and the magnitude of interlimb transfer of left-handers.
Overall, this study suggests that individual characteristics shape the
way the nervous system can generalize motor learning.
sensorimotor adaptation; intermanual transfer; reaching arm move-
ments; cross-limb education
IF YOU EXCLUSIVELY LEARNED to write with your dominant hand
and have to write for the first time with your nondominant
hand, you may write legibly. Such performance necessarily
reflects an interlimb transfer of the learned skill. Understanding
generalization of motor learning is important since it reveals
the local or global nature of the underlying processes (Harris
1963; Poggio and Bizzi 2004; Seidler and Noll 2008; Carroll et
al. 2014; Sarwary et al. 2015). By studying goal-directed
upper-limb movements in sensorimotor adaptation paradigms,
previous work showed that interlimb transfer is generally
limited (Dizio and Lackner 1995; Criscimagna-Hemminger et
al. 2003; Wang and Sainburg 2004a,b; Taylor et al. 2011;
Joiner et al. 2013; Mostafa et al. 2014) although it can be
absent (Martin et al. 1996; Kitazawa et al. 1997). This suggests
that sensorimotor adaptation is largely specific to the effector,
but it remains unclear why interlimb transfer varies across
studies. In fact little is known about the factors that determine
whether, and to what extent, adaptation to a particular condi-
tion generalizes to other conditions. In the present study, we
considered that interlimb transfer may be determined by envi-
ronmental conditions but also by individual characteristics.
Malfait and Ostry (2004) suggested that sensorimotor adap-
tation could generalize across limbs when task conditions
allow the conscious perception of a perturbation/movement
error. Berniker and Kording (2008) then suggested that gener-
alization depended on the perceived source of motor errors,
adaptation generalizing mostly if the error is assigned to a
change in the environment. Sensory feedback, which is critical
to perceive errors and adapt (Della-Maggiore et al. 2004;
Scheidt et al. 2005; Hwang et al. 2006; Franklin et al. 2007;
Sarlegna et al. 2010), may thus influence interlimb transfer. In
fact, previous work showed that the availability of visual
feedback was important for the generalization (Shabbott and
Sainburg 2010; Taylor et al. 2013) and the interlimb transfer of
visuomotor adaptation (Cohen 1973), which may be linked to
a strategic processing of motor errors (Malfait and Ostry 2004;
Berniker and Kording 2008; Taylor et al. 2011). One aim of the
present study was thus to test whether different environmental
conditions, with or without vision, influence the conscious
perception of motor errors and interlimb transfer of adaptation.
While environmental conditions do seem to influence inter-
limb transfer, individual characteristics may also influence
interlimb transfer. Currently, between-subject differences are
often thought of as reflecting noise and discarded by averaging
data from a group of participants, but heterogeneity of perfor-
mance is inevitable as it reflects natural variations of genetic,
environmental, and biological factors (Kanai and Rees 2011).
Interindividual differences may be a key to understand the
processes underlying sensorimotor adaptation. For example,
Wu et al. (2014) recently showed that the more subjects were
variable in a prelearning phase, the better they learned a novel
arm-reaching task. Although variability can be seen as antag-
onistic to performance, this study provided evidence that in-
creased variability can relate to better performance. The func-
tional aspect of variability may be linked to the exploration of
Address for reprint requests and other correspondence: F. Sarlegna, Institute of
Movement Sciences, CNRS and Aix-Marseille Univ. (UMR 7287), 163 Av. de
Luminy-CP 910, 13009 Marseille, France (e-mail: fabrice.sarlegna@univ-amu.fr).
J Neurophysiol 114: 2764–2774, 2015.
First published September 2, 2015; doi:10.1152/jn.00749.2015.
2764 0022-3077/15 Copyright © 2015 the American Physiological Society www.jn.org
multiple solutions, at both brain activity and behavioral levels
(Deco et al. 2011; Garrett et al. 2011). A second aim of the
present study was thus to examine the possibility that interlimb
transfer depends on individual characteristics such as move-
ment kinematics, in particular variability of movement trajec-
tory, or the conscious perception of motor errors. The laterality
quotient (LQ) (Oldfield 1971) has also been shown to influence
interlimb transfer of motor learning (Chase and Seidler 2008),
and we investigated this issue first by testing the influence of
the degree of handedness on interlimb transfer in right-handers
and then by assessing interlimb transfer in left-handers.
Several studies showed that interlimb transfer can only be
observed from the dominant arm (DA) to the nondominant arm
(NDA) (Criscimagna-Hemminger et al. 2003; Galea et al.
2007) although transfer has also been found to be bidirectional
(Wang and Sainburg 2004a; Sarwary et al. 2015). In the
present study, we thus used a paradigm allowing us to assess,
for each individual, transfer of sensorimotor adaptation from
the DA to the NDA. We specifically addressed the interlimb
transfer of adaptation to new limb dynamics by using a rotating
platform that could generate Coriolis forces on the entire arm
while subjects perform unconstrained arm reaching movements
(Dizio and Lackner 1995).
MATERIALS AND METHODS
Experiment on Healthy Right-Handed Young Adults
Subjects. Twenty subjects with no known sensorimotor impairment
participated. Ten young adults (5 males, 5 females, mean age: 24.6 yr)
were tested with direct vision of the limb and workspace throughout
the experiment (VP group). Ten other young adults (5 males, 5
females, mean age: 23.3 yr) had no visual feedback, only propriocep-
tive feedback of hand movement as the room was in darkness (P
group).
All 20 subjects were right handed (LQ ⱖ70%). The LQ, i.e., the
degree of handedness, was assessed by asking subjects to complete the
10-item version of the Edinburgh Inventory (Oldfield 1971). This
questionnaire assesses hand dominance in daily activities (e.g., writ-
ing, throwing), LQ ranging from ⫺100 (left handed) to 100 (right
handed). LQ was 80 ⫾10% on average for the P group and 87 ⫾11%
for the VP group.
All subjects had normal or corrected-to-normal vision and were
naive to the purpose of the experiment. Participants gave their in-
formed consent before the study, which was approved by the Institu-
tional Review Board of the Institute of Movement Sciences and
performed in accordance with the ethical standards of the Declaration
of Helsinki.
Experimental setup. Participants sat in a bucket seat (adapted from
a car seat) at the center of a motorized rotating platform and were
asked to reach toward flashed visual targets (Fig. 1A). An adjustable
headrest was used to restrain the head. On a horizontal board, at waist
level, a visual and tactile landmark indicated the starting hand posi-
tion. Visual targets were low-intensity red light-emitting diodes (3
mm in diameter). Three targets were positioned on a 37-cm radius
circular array at 0° (straight-ahead), 20° (to the right), and ⫺20° with
respect to start position.
Infrared active markers were taped to the right and left index
fingertips whose positions were sampled at 350 Hz using an optical
motion tracking system (Codamotion cx1 and MiniHub; Charnwood
Dynamics, Leicestershire, UK). As in Sarlegna et al. (2010), the
experimenter controlled the tracking system, the motorized platform,
and the presentation of the visual targets from an adjacent room by
using a customized software (Docometre) governing a real-time
acquisition system ADwin-Pro (Jäger, Germany).
Procedure. At the beginning of each trial, participants had to
actively position their hand at the starting location (Fig. 1A). They
were asked to reach as fast and accurately as possible with their hand
toward the visual target that was illuminated for 0.3 s. No explicit
instructions were given with respect to hand path. However, partici-
pants were required to “reach in one movement” and not to correct
after their finger contacted the horizontal board. At 1.6 s after trial
onset, an auditory 100-ms tone informed the subject to go back slowly
to the starting location. At 7.4 s after trial onset, a 600-ms tone
signaled to the participant that the trial had ended and that the next
trial would start immediately. Peak velocity (PV) of the targeted,
outward reaching movement was ⬃10 times greater than that of the
backward arm movement toward the start position. All participants
were familiarized with the task during a preliminary phase.
The experimental session consisted of three phases (Fig. 1B).
1) PRE-rotation test (baseline): participants executed 30 reaching
movements with the DA then with the NDA (10 trials per target for
each hand, in a pseudorandom order which was similar for all 20
participants) while the platform remained stationary, providing base-
line reaching performance in the normal force field. After the PRE-
test, the rotating platform was accelerated in 80 s up to a constant
velocity of 120°/s, i.e., 20 rpm.
2) PER-rotation (adaptation) phase: participants performed 100
movements with the DA to the central target while the platform was
rotating counterclockwise at 120°/s, generating clockwise Coriolis
forces on the moving limb. Coriolis forces (F
cor
; see Dizio and
Lackner 1995) are the product of the arm mass (m), platform’s angular
velocity (w) and tangential arm velocity (v) according to the equation:
F
cor
⫽⫺2m(w⫻v). After the adaptation phase, the rotating platform
was slowed to a stop within 80 s.
3) POST-rotation test: once the platform was stationary, partici-
pants executed 30 reaching movements with the nonexposed NDA
first and then with the DA (10 trials per target for each hand). The first
presented target was the central one (then left, right. . .).
Participants were instructed not to move any body part, including
their head and opposite arm, during or between trials, and an infra-red
AB
Fig. 1. A: experimental setup. B: illustration of the experimental
protocol.
2765INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
J Neurophysiol •doi:10.1152/jn.00749.2015 •www.jn.org
camera allowed continuous monitoring of subject’s behavior. A 60-s
delay was used between the end of the platform’s acceleration and
deceleration phases and the first reach trial to allow the vestibular
semicircular canals to return to their resting discharge frequency. The
PER-rotation test lasted ⬃20 min, the complete reaching task lasting
⬃45 min.
At the end of the experimental session, the subject, still in the
bucket seat, filled a questionnaire that combined open-choice, multi-
choice, and forced-choice questions related to awareness and credit
assignment of errors. We wished to determine whether subjects
consciously perceived errors in movement trajectory during the first
trials of the PER-rotation phase, asking first “Did you feel something
in particular in the first trials of the PER-rotation phase?” and “Were
you surprised by your performance in the first trial of the PER-rotation
phase?” If the subject talked about motor errors, he or she was
considered to be aware of his or her errors; otherwise he or she was
not. Then we showed a top view of each subject’s arm trajectory in the
first trial of the PER-rotation phase and asked subjects to fill a
questionnaire to determine whether errors were assigned to internal
and/or external factors. We thus asked, in a counterbalanced order,
“Did you associate the errors you made early in the PER-rotation
phase to external factors?” and “Did you associate the errors you
made in the PER-rotation phase to yourself (e.g., internal factors such
as fatigue, inattention. . .)?” Subjects here had to answer “Strongly
disagree,” “Somewhat disagree,” “Somewhat agree,” or “Strongly
agree.” At last subjects were asked to circle whether errors were more
associated to “Internal” or “External” factors.
Kinematic data analysis. Data were analyzed using Matlab (Math-
works, Natick, MA). A few trials (1.6%) had to be discarded because
they were not properly performed by the subjects or were corrupted by
noise. Position data from the markers on the right and left index
fingertips were low-pass filtered with a dual-pass, no-lag Butterworth
(cut-off frequency: 8 Hz; order: 2). Movement onset was defined as
the first time tangential hand velocity reached 3 cm/s and movement
offset as the first time hand velocity dropped below 3 cm/s.
Initial direction was computed as the angle between the vector from
the start position to the target position and the vector from the start
position to the hand position at 150 ms after movement onset. Initial
movement direction at 150 ms was considered the most critical
dependant variable because it mostly reflects the initial motor plan,
before online corrective feedback loops substantially influence move-
ment execution (Prablanc and Martin 1992; Saunders and Knill 2003;
Sarlegna et al. 2004; Franklin and Wolpert 2008). Besides, 150 ms
corresponded to the mean time (averaged across healthy right-handed
subjects) of PV, and therefore, the time at which the magnitude of the
Coriolis forces acting on arm movements is maximum during plat-
form rotation. As in Dizio and Lackner (1995), we computed the
lateral endpoint error, i.e., the perpendicular deviation (in cm) be-
tween the index fingertip at movement end and the straight line
connecting the starting point (i.e., movement onset) to the target, and
the maximum perpendicular deviation (in cm) between the index
fingertip and the straight line connecting the hand position at move-
ment onset and the target (Malfait and Ostry 2004). For all the
measures, we assigned positive values to rightward deviations and
negative values to leftward deviations.
To assess adaptation and interlimb transfer, we employed a proce-
dure similar to that used by Dizio and Lackner (1995). First, to assess
adaptation of the DA, we compared the mean of the 10 trials of the
PRE-test (baseline) with the first two trials and the last trial of the
PER-rotation phase (to observe the effects of the velocity-dependent
force field and the final adaptation, respectively) and the first two trials
of the POST-test (to observe an aftereffect and reaadaptation) for
movements toward the central target (0°). To assess whether adapta-
tion differed between groups, we fitted exponential curves to each
individual data of initial direction y⫽a⫻e
⫺bx
⫹c, in which a
represents the amplitude of the initial error, bthe time constant, and
cthe offset. To assess interlimb transfer of DA adaptation to the NDA,
we compared the 10 NDA movements toward the central target (0°) of
the PRE-test to the first NDA movement of the POST-test for
movements toward the central target.
The level of adaptation was assessed for both initial direction and
lateral endpoint error using the formula (first adaptation trial ⫺final
adaptation)/(first adaptation trial ⫺baseline) with final adaptation
referring to the mean of the last 10 trials of the adaptation phase and
baseline referring to the mean of the 10 trials towards the central
target in the preadaptation phase.
Statistical analysis. Repeated-measures ANOVAs were used to
assess the significance of DA adaptation and interlimb transfer. To
assess DA adaptation, we conducted 6 ⫻2 [PHASE (PRE-, PER-
Initial, PER-2nd trial, PER-Final, POST-Initial, POST-2nd trial) ⫻
VISION (P and VP groups)] ANOVAs, and to assess interlimb
transfer, we used 2 ⫻2 [PHASE (PRE-, POST-Initial) ⫻VISION (P
and VP groups)] ANOVAs.
All data had a normal distribution as verified with the Kolmogorov-
Smirnov method. The variance homogeneity across experimental
groups was also confirmed using Levene test. Newman-Keuls tests
were used for post hoc analysis. For all tests, the significance thresh-
old was set at 0.05.
The transfer value of each subject was defined using the formula
ti⫽Xi⫺
i
SEi
, where, for each subject i,Xis the initial direction of the
first POST-test NDA trial,
i
is the initial direction averaged across
NDA baseline movements, and SE is the standard error of the
baseline. We used the standard error instead of the standard deviation
as it is more appropriate for relatively small samples (Chin and Lee
2008). We used the transfer value to assess the impact of different
factors on transfer (i.e., the 3 variables used in the classification
model). As data analysis showed significant interlimb transfer with a
mean leftward shift (i.e., the values of the aftereffects were negative),
we multiplied for ease of understanding the t-scores by ⫺1 so that any
increase in the t-score would correspond to an increase in interlimb
transfer. The aftereffects on the DA were computed with the same
method.
Classification model. A linear discriminant analysis, a form of
classification model, was used to separate subjects in a qualitative
manner according to their aftereffects on the NDA. Two classes were
considered: “Transfer” and “No transfer.” Each subject was assigned
to a class based on the difference in NDA initial direction between
PRE- and POST-tests. Specifically, the 10 NDA movements toward
the central target in the PRE-test were used to compute a 99%
confidence interval (CI): CIi⫽
冋
x
i⫺t
␣
i
兹n
;x
i⫹t
␣
i
兹n
册
, where
for each observation i,xdesignates the mean of the baseline, t
␣
is the
value of the Student distribution for a 99% CI in function of the
degree of freedom
␣
,
is the standard deviation of the mean of
the baseline, and nis the number of trials. Based on Dizio and Lackner
(1995), we expected a negative shift in NDA initial direction; thus, if
the initial direction of the first NDA movement of the POST-test fell
below the lower bound of the CI, the subject was assigned to the
“Transfer” class. If the initial direction fell within the CI or above the
upper bound, the subject was assigned to the “No transfer” class.
Three variables were eventually selected (see below) to perform the
linear discrimination: variability (standard deviation) of the mean
initial direction across the last 10 trials in the PER-rotation phase,
mean PV across all 100 trials in the PER-rotation phase, and LQ
(Oldfield 1971).
A fivefold cross-validation method was used to assess the accuracy
of the classification model, i.e., 80% of the observations was used to
build the model (training dataset, n⫽16) and predict the remaining
20% of the observations (test dataset, n⫽4) in an iterative manner
until all the observations were tested once. Thirty iterations of the
fivefold cross validation were run to have a better estimate of the
model performance.
2766 INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
J Neurophysiol •doi:10.1152/jn.00749.2015 •www.jn.org
To evaluate the efficiency of the model, a receiver operating
characteristic (ROC) curve was employed. A ROC curve displays the
probabilities in term of sensitivity and specificity of the model at each
decision threshold (i.e., a value ranging from 0 to 1 above which the
observation is assigned to the class 1). In our study, sensitivity [true
positive (TP) rate] measured the proportion of actual “Transfer”
subjects who are correctly classified by the model. Specificity [true
negative (TN) rate] measured the proportion of actual “No transfer”
subjects who are correctly classified. FP and FN are the false positive
and negative rate.
sensitivity ⫽TP
TP ⫹FN ⫻100%
specificity ⫽TN
TN ⫹FP ⫻100%
Selection of the variables. The variables used for the linear dis-
crimination analysis were selected among a set of 57 variables with a
backward stepwise method, i.e., by eliminating the least necessary
variable one by one (and by evaluating the resulting errors through
cross validation). As the backward stepwise method cannot be per-
formed if there are more observations than variables, the selection was
applied first on multiple sets of 9 to 12 variables. For each set, we kept
the variables that contributed the most to the prediction of transfer.
We reiterated the selection until we got the 12 best variables.
The variables were the LQ and kinematic variables. In the adapta-
tion phase, the kinematic variables representing the errors resulting
from the force field (i.e., initial direction at PV and at 150 ms,
maximum perpendicular deviation, lateral endpoint) were determined
across the first and last 10 trials, as well as across the 100 trials, while
other variables were determined across the 100 trials, except for the
area under the curve (first 10 and 10 last only).
1) From the adaptation phase
i) The variability and the mean of lateral endpoint error, PV,
maximum curvature, initial direction at PV, and at 150 ms.
ii) The mean of reaction time, movement time, difference in PV
between the PER-rotation and the NDA POST-rotation phase, area
under the curve with respect to a straight trajectory (first 10 and 10 last
trials only), perpendicular deviation throughout the trajectory, peak
acceleration, and time to PV.
iii) Amplitude and slope of the exponential fit of the adaptation
curve (i.e., unknown aand b).
2) From the baseline, the mean and standard deviation of maximum
perpendicular deviation, initial direction of the DA and NDA at lateral
endpoint, PV, and 150 ms (10 trials).
3) From the NDA POST-rotation phase, the reaction time, move-
ment time, time to PV, and PV of the first trial.
To determine whether the variables used for the linear discrimina-
tion analysis could determine the amount of interlimb transfer, we
performed a multiple regression analysis using the transfer value (see
Statistical analysis) as the dependent variable.
Experiment with Left-Handed Individuals
We tested left-handers to both test the robustness of the model on
new data and test whether our findings on right-handers were valid for
right-handers and left-handers.
Subjects. Nine young left-handed individuals (7 males, 2 females,
mean age: 23.4 ⫾3 yr old) performed the experiment in darkness, like
the P group of the main experiment. Mean LQ was ⫺70 ⫾37%. One
subject was a self-declared left-hander, who preferred to write or draw
with the left hand but had a LQ of ⫹10%. Participants gave their
informed consent before the study, which was approved by the
Institutional Review Board of the Institute of Movement Sciences and
performed in accordance with the ethical standards of the Declaration
of Helsinki.
Procedure. The experimental procedure was the same as for the
right-handed subjects of the P group except that subjects were tested
with a clockwise platform rotation. This was done so that the biome-
chanical consequences of the perturbation were matched for left- and
right-handers. The left DA was tested during the adaptation phase
while interlimb transfer was tested as the difference in right NDA
performance between trials immediately before and after the PER-
rotation phase. Apart from the arm used, data analysis was similar for
right- and left-handers. For the sake of understanding, results of
left-handers were flipped so that they corresponded to the other
right-handed subjects.
RESULTS
Adaptation of the Right DA to the Novel Force Field
A classical pattern of adaptation was observed in the right-
handed group. Figure 2, Aand B, shows that DA movements
were generally straight and accurate in both groups in the
PRE-test. However, the counterclockwise rotation generated
Coriolis forces that clearly perturbed, rightward, the hand path
on the initial trial of the PER-rotation phase in the VP and P
groups. Indeed, when considering only movements toward the
central target, a 2 ⫻6 {VISION (VP, P) ⫻PHASE [PRE-test
(mean of 10 trials), PER-Initial, PER-2, PER-Final, POST-1,
and POST-2]} ANOVA only showed an effect of the experi-
mental PHASE on initial direction [F(5,90) ⫽6.7; P⬍
0.0001], revealing that initial direction was substantially
shifted to the right compared with baseline (by 6.4° on average
across groups, Fig. 2, Cand D). Also, when subjects first
Fig. 2. Adaptation of the dominant arm (DA) movements toward the central
target. Aand B: top view of DA hand paths for a representative subject of the
vision-proprioception (VP) group (A) and proprioception (P) group (B). C:
mean initial direction (degree) of the central target averaged for the PRE-test
(baseline) for each trial across the adaptation phase and the POST-test for the
P (blue) and VP (red) groups. Shaded blue and red areas represent means ⫾
SE. D: mean initial direction (degree) for the P (blue) and VP (red) groups in
the PRE-test (baseline); the 1st, 2nd, and last trial of the PER-rotation phase;
and the 1st and 2nd trials of the POST-test. For each PRE-test, all 10 trials
were averaged to obtain a baseline reference. Error bars represent means ⫾SE.
*P⬍0.05, ***P⬍0.001, significant difference.
2767INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
J Neurophysiol •doi:10.1152/jn.00749.2015 •www.jn.org
encountered the biomechanical consequences of the platform
rotation on the arm movement, visual feedback was used to
adjust online the last portion of the arm trajectory compared
with subjects without vision. Indeed, a 2 ⫻6 ANOVA on the
lateral endpoint error revealed an effect of VISION [F(1,18) ⫽
21.2; P⬍0.001], PHASE [F(5,90) ⫽36.0; P⬍0.001], and an
interaction [F(5,90) ⫽3.1; P⬍0.02; Fig. 2D], which revealed
that endpoint error in the first two trials of the PER-rotation
phase differed from baseline in the P group (mean shift of the
first trial ⫽4.6 cm; P⬍0.001) but not in the VP group (P⫽
0.24). As trials were repeated, subjects gradually compensated
for the rightward deviation to restore straight, accurate move-
ment performance. The t-tests for independent samples did not
reveal any significant difference in adaptation between the P
and VP groups for any of the three parameters of the expo-
nential fit of the initial direction data (see MATERIALS AND
METHODS).
Once the rotation stopped, and after the left NDA was
tested in the normal force field, substantial aftereffects were
observed on the right DA. In the first trial of the POST-test,
toward the central target, DA trajectory differed from base-
line with an 11.8° shift in initial direction and a 4.6 cm shift
in lateral endpoint on average across groups. Similar find-
ings were observed when initial direction was computed at
150 ms or at PV. Overall, PV and movement time was
similar in PRE-, PER-, and POST-tests and across experi-
mental groups (mean in P group ⫽2.4 ⫾0.9 m/s and
423 ⫾16 ms; mean in VP group ⫽2.6 ⫾0.9 m/s and
429 ⫾16 ms).
We tested different target directions in PRE- and POST-tests
to assess whether the force-field adaptation of DA movements
toward the central target generalized to movements performed
in other directions. For the lateral, right, and left targets,
separate 2 ⫻3 ANOVAs {VISION (VP, P) ⫻PHASE [base-
line (mean of 10 trials), POST-1 and POST-2]} on initial
direction revealed only a significant effect of experimental
PHASE [left target: F(2,36) ⫽11.0; P⬍0.001; right target:
F(2,36) ⫽4.3; P⬍0.05]. Post hoc analyses revealed that for
the left target, initial direction in the first trial of the POST-test
tended to differ (P⫽0.06) from baseline (mean shift ⫽⫺3.0°)
and for the right target, the shift was significant (mean ⫽
⫺2.6°; P⬍0.05), indicating intralimb generalization (across
movement directions).
Interlimb Transfer of Dynamic Adaptation to the left NDA
Figure 3, Aand B, shows how movement trajectory of the
left NDA was influenced by adaptation of the right DA move-
ments: a 2 ⫻2 [VISION (VP, P) ⫻PHASE (PRE-, POST-
test)] ANOVA showed that NDA initial direction differed
between baseline and the first trial of the POST-test by ⫺2.8°
[F(1,18) ⫽5.6; P⬍0.05], as illustrated on Fig. 3C. There was
no significant VISION effect [F(1,18) ⫽0.1; P⫽0.76] and no
significant interaction [F(1,18) ⫽0.0; P⫽0.99], indicating
that interlimb transfer of force-field adaptation was not influ-
enced by vision. It should be noted that the mean difference
POST-PRE in initial direction was negative (leftward shift),
which is consistent with a representation of the limb dynamics
in extrinsic coordinates. Also, the mean interlimb transfer
represented 20% of the mean aftereffect observed on the DA
movements toward the central target (Fig. 3D). We found no
significant aftereffects on initial direction for the left [F(1,18) ⫽
0.61, P⫽0.44] and right targets [F(1,17) ⫽0.01, P⫽0.90],
indicating that adaptation of DA movements toward a single
target only generalized, to a significant but limited extent, to
NDA movements toward the same target (and to DA move-
ments toward different targets).
The analysis of lateral endpoint errors revealed slightly
different results since an interaction [F(1,18) ⫽7.9, P⬍0.05]
indicated a significant transfer only for the P group. Indeed, on
the first trial of the POST-test, visual feedback mechanisms
enabled subjects with vision (VP group) to correct the trajec-
tory errors induced by the aftereffect on the NDA and reach
more accurately the target at the end of the movement com-
pared with subjects without vision. Maximum perpendicular
deviation occurred on average 264 ⫾84 ms after movement
onset in the P group and after 239 ⫾90 ms in the VP group.
The 2 ⫻2 [VISION (VP, P) ⫻PHASE (PRE-, POST-test)]
ANOVA revealed an interaction [F(1,18) ⫽6.18, P⬍0.05],
which showed that the difference between PRE- and POST-test
trials was significant only for the P group, not for the VP group.
Vision thus appears to influence interlimb transfer only when
considering late or terminal kinematic features of the move-
ment, as in some previous studies (Cohen 1973), but this really
highlights the efficiency of visual feedback mechanisms to
minimize motor errors.
Awareness and Assignment of Motor Errors
The possible link between conscious assignment of errors
(i.e., to internal or external factors) and interlimb transfer was
investigated using debriefing questionnaires at the end of the
reaching experiment. Three subjects out of 10 in the P group
and 1 subject out of 10 in the VP group reported not being
aware of trajectory errors in the first trials of the PER-rotation
phase. A Mann-Whitney nonparametric test showed that the
transfer value did not differ according to the awareness of
errors (P⫽0.25). When asked “Did you associate the errors
you made in the first trials of the PER-rotation phase to
external factors?,” all 20 subjects agreed. When subjects were
ABCD
0
-4
-8
-12
10 10
10 10
4
3
2
1
0
-4
Non-Dominant Arm (left hand)
VP Group P Group
Initial Direction (°)
y (cm)
y (cm)
x (cm) x (cm) PRE POST-initial
Initial Direction (°)
NDA DA
20%
After-effects (Post-Pre)
Targ e t Tar g et
PRE
POST
150 ms
Fig. 3. Interlimb transfer of force-field adaptation in the right-handed group. A
and B: top view of nondominant arm (NDA) hand paths for a representative
subject of the VP group (A) and the P group (B) in the PRE-test (representative
trial in black) and in the POST-test (1st trial in green). C: initial direction in
baseline and the 1st trial of the POST-test with the NDA for both groups (no
groups effect). D: aftereffects (POST: baseline at initial direction) for the left
NDA and the right DA. Error bars represent SE. *P⬍0.05, significant
difference.
2768 INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
J Neurophysiol •doi:10.1152/jn.00749.2015 •www.jn.org
asked whether they would associate these errors to internal
factors, six subjects (4 of the P group, 2 of the VP group)
agreed but interlimb transfer was similar for subjects who
agreed or not [t(18) ⫽⫺1.2, P⫽0.25]. When forced to choose
whether errors were mostly due to internal or external factors,
only one subject (from the VP group) out of 20 assigned his
errors to internal factors.
Heterogeneity of DA-to-NDA Interlimb Transfer Across
Subjects
Most studies on interlimb transfer have determined whether
transfer was significant based on group averages. However, the
goal of the present study was to explore interindividual differ-
ences and Fig. 4Ashows a substantial heterogeneity in inter-
limb transfer across subjects. We determined whether transfer
occurred for each subject based on their baseline confidence
interval (see Statistical analysis). This method revealed that 12
out of 20 subjects were classified as “Transfer” subjects. Four
subjects presented little difference in initial direction between
PRE- and POST-tests, and four subjects seemed to produce an
opposite pattern of transfer compared with the ensemble aver-
age, but for statistical reasons, we grouped the eight subjects
who did not perform as the ensemble average and classified
them as “No transfer” subjects.
Classification Model of Interlimb Transfer Based on
Individual Characteristics
To determine whether the presence of interlimb transfer
could be linked to any characteristic of the subjects, we first
employed a qualitative approach and used a linear discriminant
analysis to find the combination of variables that best charac-
terizes the interclass differences (here “Transfer” vs. “No
transfer”). A backward-stepwise method was first used to find
the variables which could best discriminate the two different
classes of behaviour (Fig. 4B; see MATERIALS AND METHODS). For
the sake of simplicity, we selected the best three variables, i.e.,
variability of DA initial direction in the last 10 trials of the
PER-rotation phase, LQ (Oldfield 1971), and mean DA PV
across all 100 trials of the PER-rotation phase. Figure 4C
shows the ROC of the linear discriminant analysis based on
these three variables: the area under the curve was 97%, which
indicates strong discriminating ability at different decision
thresholds. Such thresholds influence the sensitivity and spec-
ificity of the classification model, but both were well balanced
as high sensitivity was not obtained at the cost of low speci-
ficity. In other words, the classification model using a linear
combination of the three variables could accurately predict
observations from the classes “Transfer” (class 1) and “No
transfer” (class 0). For instance, at the threshold 0.25 (red
circle), the sensitivity was equal to 0.83 and specificity was
0.88 (10/12 “Transfer” subjects and 7/8 “No transfer” subjects
were well detected). At the threshold 0.21 (green square),
sensitivity was 0.92 and specificity was 0.75 (11/12 “Transfer”
subjects and 6/8 “No transfer” subjects were well detected,
respectively). Combinations of two variables could also well
discriminate the classes “Transfer” and “No transfer”: for
instance, area under the curve when LQ was removed from the
analysis was 79%.
Supplemental Video S1 (Supplemental Material for this
article is available online at the Journal website) shows how
“Transfer” and “No transfer” subjects were discriminated with
three individual characteristics. The variable with the highest
coefficient in the discriminant function of the classification
model was the variability of DA initial direction over the 10
last adaptation trials, followed by the LQ and the mean PV
during adaptation. Figure 4Dshows a positive linear correla-
tion between the transfer value and the variability (r⫽0.49;
P⫽0.03). The transfer value was correlated with other
measures of motor variability such as variability of DA direc-
tion at PV across the adaptation phase (r⫽0.59; P⬍0.01),
adj R² = 0.58, p<0.01
R = 0.49, p<0.05
5
0
-5
-10
-15
Transfer P group
Transfer VP group
No Transfer P group
No Transfer VP group
-4 -2
-6 -6 -4 -2
5 - 30 -
-4
-8
1-
Fig. 4. Individual characteristics determining interlimb transfer. A: difference, for each subject, in initial direction between the 1st trial of the POST-test of the
NDA and the baseline in the P group (left-pointed triangles) and the VP group (right-pointed triangles). Subjects classified with significant interlimb transfer
according to their baseline 99% confidence interval (CI) are in black while other subjects are in grey. B: misclassification error (MCE) in percentage as a function
of the number (Nb) of variables. The MCE reaches the minimum when 5 variables are used and then overfitting occurs with ⬎10 variables. C: receiving operator
characteristics (ROC) curve of the linear discriminant analysis, with additional results at 2 decision thresholds (red circle: 0.25, green square: 0.21). AUC, area
under the curve. D: correlation between variability of initial direction in the last 10 trials of the adaptation phase (x) and the transfer value (y). E: peak velocity
in the classes “Transfer” and “No Transfer.” Each data point represents a subject. The distribution is represented by a boxplot where the red line is the mean,
the red shaded area the 95% interval, and the blue area the SE. F: observed transfer values as a function of the values predicted by the multiple regression
(dependent variable: transfer value; independent variables: variability, laterality quotient, and peak velocity).
2769INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
J Neurophysiol •doi:10.1152/jn.00749.2015 •www.jn.org
variability of DA initial direction across the adaptation phase
(r⫽0.56; P⬍0.05).
The positive correlation between LQ and the transfer value
was marginally significant (P⫽0.06). There was no significant
correlation between PV and the transfer value (r⫽0.18; P⫽
0.44) but PV marginally differed [t(18) ⫽2; P⫽0.06]
between the “Transfer” class (mean ⫽2.6 ⫾0.3 m/s) and the
“No Transfer” class (mean ⫽2.3 ⫾0.3m/s; see Fig. 4E). No
effect of VISION was observed on the three variables (vari-
ability: P⫽0.45; PV: P⫽0.33; LQ: P⫽0.24). The three
variables were not significantly correlated to each other.
We also investigated the link between interlimb transfer and
the level of adaptation. However, the level of adaptation (see
MATERIALS AND METHODS) when considering initial direction and
lateral endpoint error could not be linked to interlimb transfer
as the level of adaptation did not significantly differ across
classes of transfer [t(18) ⫽⫺0.36, P⫽0.71; t(18) ⫽0.88,
P⫽0.39; respectively] and there was no significant correlation
with the amount of transfer (r⫽0.05, P⫽0.8; r⫽⫺0.10,
P⫽0.7; respectively).
The Amount of Interlimb Transfer Can Be Determined Based
on Individual Characteristics
To examine whether the magnitude of interlimb transfer
could be predicted based on the three variables of the model,
we used a multiple regression analysis. Figure 4Fshows that
the variables could predict the observed transfer value
[F(3,16) ⫽7.5; r
2
⫽0.58; adjusted r
2
⫽0.50; P⬍0.01]. All
the variables contributed significantly to the model (P⬍0.01
for variability and PV; P⬍0.05 for LQ). The equation of the
multiple regression was as follows: 46 ⫹2.8 ⫻variability ⫹
0.2 ⫻LQ ⫹0.08 ⫻PV.
We tested whether the aftereffects on the right DA could be
predicted by the variables listed in the MATERIALS AND METHODS,
with the exception of variability of DA initial direction in the
baseline as it was used to assess the DA aftereffects. We
applied a multiple regression with stepwise selection (forward
and backward) similar to the methods used to predict interlimb
transfer but none of the (combination of) variables could
predict the DA aftereffects.
Interlimb Transfer of Dynamic Adaptation in Left-Handers
Adaptation of the left DA. To test the influence of handed-
ness on interlimb transfer and to test our qualitative and
quantitative models of transfer, we repeated the experiment
with nine left-handed subjects. Figure 5, Aand B, shows a
classical pattern of adaptation in initial direction. A one-way
ANOVA {PHASE [PRE-test (mean of 10 trials), PER-Initial,
PER-2, PER-Final, POST-1, and POST-2]} revealed an effect
of the experimental phase [F(5,35) ⫽5; P⬍0.01]. The first
trial of the PER-rotation, adaptation phase differed from all
other trials (all P⬍0.05; the difference with baseline being
marginally significant, P⫽0.05). The mean shift in initial
direction of the first trial compared with baseline was 5.8 ⫾6°
and the mean aftereffect, on initial direction, was ⫺5.4 ⫾5.2°.
Interlimb transfer of dynamic adaptation to the right NDA.
On average, initial direction was similar in PRE-test (mean ⫽
⫺1.7 ⫾3.5°) and POST-test (mean ⫽0.1 ⫾6.2°) such that
there was no significant interlimb transfer for the group [t(8) ⫽
⫺0.76; P⫽0.4]. However, for four subjects out of nine, initial
direction in the first trial of the POST-test differed from the
99% confidence interval of the NDA PRE-test (baseline),
indicating interlimb transfer. For these four “Transfer” subjects
interlimb transfer (mean ⫽⫺6.3 ⫾3.9°) was consistent with
a representation of limb dynamics in extrinsic coordinates. In
the “No transfer” class, the mean shift between PRE- and
POST-test was 1.9 ⫾1.8° (see Fig. 5C).
Test of the classifier on left-handed individuals. Testing
left-handers gave us the opportunity to test the robustness of
the model on new data and to investigate whether the LQ had
an influence on transfer among left-handers (in other words,
does the extent of handedness and handedness itself matter to
interlimb transfer?).
With respect to the individual characteristics that were
identified as determining interlimb transfer, variability of ini-
tial direction during the last 10 trials of the adaptation phase
did not significantly differ between left and right-handers
[mean variability ⫽3.4 ⫾1.2°; t(27) ⫽⫺0.9; P⫽0.4].
Although all subjects were instructed that they should reach as
fast and as accurately as possible left-handers were faster than
right-handers during the adaptation phase [mean PV ⫽3.2 ⫾
0.3 vs. 2.5 ⫾0.3 m/s; t(27) ⫽⫺7; P⬍0.001]. This advantage
in left-handers in term of movement speed is consistent with
previous work (Kilshaw and Annett 1983).
The large difference in PV between right and left-handers
greatly reduced the classifier’s accuracy because the discrimi-
nant function is very sensitive to out-of-range data. Hence,
only LQ and variability were used for the classifier, which was
built with the data of the 20 right-handed subjects (training
dataset) and tested on the 9 left-handed subjects. At the
threshold 0.58, the model misclassified only one “Transfer”
subject.
PRE
PER-Initial
PER-Final
POST
150 ms
PRE
POST
150 ms
Fig. 5. Adaptation and interlimb transfer in left-handers. A:
top view of DA hand paths for a representative subject. B:
initial direction (in degree) for the DA averaged across
subjects in the PRE-test (baseline); the 1st, the 2nd, and the
last trial of the PER-rotation phase; and the 1st and second
trials of the POST-test. ***P⬍0.001, significant differ-
ence. Error bars represent SE. C: top view of NDA hand
paths for 2 representative subjects
2770 INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
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Next, we pooled the data of the left- and right-handers and
ran the cross-validation on all the subjects to consider the
distribution of the two variables. We ran a fivefold 30-iteration
cross validation on 29 subjects: 16 were in the class “Transfer,”
13 in the class “No Transfer.” The area under the curve
indicated 77% of good discrimination, which was higher that
when the 2 variables were tested on the 20 right-handers (Fig.
6A). At the threshold 0.58, the sensitivity and the specificity
were equal to 0.62 and 0.85, respectively (i.e., 10/16 “Trans-
fer” subjects and 11/13 “No Transfer” subjects were well
detected; the misclassified subjects were 6 right and 2
left-handers).
When considering more quantitative aspects, Fig. 6Bshows
that variability was correlated to the transfer value (n⫽28,
r⫽0.43; P⬍0.05). Figure 6Cshows that the LQ was
marginally significant to discriminate the classes “Transfer”
and “No Transfer” (P⫽0.08). Altogether, variability and LQ
were useful to discriminate “Transfer” and “No transfer” sub-
jects (Fig. 6D).
Finally, we conducted a multiple regression analysis to
examine whether the magnitude of interlimb transfer could also
be predicted by variability and LQ with both right and left-
handers in the data set. Figure 6Eshows that the observed
transfer value could be predicted [F(2,26) ⫽10.3; r⫽0.65; r
2
⫽0.42; adjusted r
2
⫽0.37; P⬍0.01], with a combination of
the two variables. The equation of the multiple regression was
as follows: 15.2 ⫹2.9 ⫻variability ⫹0.1 ⫻LQ.
DISCUSSION
Conflicting results have been reported on the interlimb
transfer of sensorimotor adaptation (Imamizu and Shimojo
1995; Morton et al. 2001; Anguera et al. 2007; Galea et al.
2007) and we sought to identify the factors that determine
interlimb transfer. We first used an experimental approach that
showed, on average in healthy right-handed subjects, signifi-
cant interlimb transfer of force-field adaptation. Interlimb
transfer was similar irrespective of the sensory feedback con-
ditions (vision/proprioception and proprioception only), the
conscious perception of motor errors, and the level of adapta-
tion. Previous studies already reported a similar level of adap-
tation irrespective of visual feedback conditions (Franklin et al.
2007; Hinder et al. 2008; Arce et al. 2009), and our study
appears to extend these findings to the interlimb transfer of
sensorimotor adaptation. More importantly, the present study
suggests that an individualized approach can reveal the critical
factors determining interlimb transfer. Three main predictors
of interlimb transfer were identified in the right-handed group:
variability, PV, and LQ. When left-handers were tested, inter-
limb transfer could also be qualitatively and quantitatively
predicted with variability and LQ. Taken together these results
suggest that beyond the environmental conditions, interlimb
transfer is determined by quantitative features of the adaptation
phase and by the subjects’ degree of lateralization.
Variability of Motor Execution and PV are Key Predictors
of Interlimb Transfer
When considering our results averaged across subjects, they
are in line with previous studies that reported significant, but
limited, interlimb transfer of sensorimotor adaptation. Our
results are also consistent with studies that reported interlimb
transfer of adaptation to novel limb dynamics reflecting a
central representation of limb dynamics in extrinsic coordi-
nates (Dizio and Lackner 1995; Criscimagna-Hemminger et al.
2003; Malfait and Ostry 2004). However, when we looked at
R² = 0.37, p<0.01
R = 0.43, p<0.05
5-fold,30-Iterations
False Positive (1-Specificity)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
True Positive (Sensitivity)
ABC
DE
Predicted values
Observed values
Fig. 6. Classification and regression models for both right- and left-handers. A: in blue, the ROC of the left DA classification model built with 2 variables
(laterality quotient and variability) for all (both right- and left-handed) subjects (n⫽29). A result is shown at the decision threshold 0.58 (blue circle). In pink,
the ROC for the right-handers (n⫽20) only. B: correlation between variability and the transfer value. C: representation of the laterality quotient distribution
between the classes “Transfer” and “No transfer.” Each data point represents a subject. The red line is the mean, the red shaded area the 95% interval, and the
blue area the SE. D: transfer value as a function of variability (°) and laterality quotient (%). The regression model is represented by the hyperplan. E: observed
transfer values as a function of the predicted transfer values based on the multiple regression (dependent variable: transfer value; independent variables: variability
and laterality quotient).
2771INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
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interlimb transfer for each individual, we uncovered a substan-
tial heterogeneity. A classification model revealed that a key
predictor of interlimb transfer is the variability of initial move-
ment direction during the last part of the adaptation phase.
Recent studies have highlighted how motor variability at the
execution level could enhance motor learning, first in song-
birds (Ölveczky et al. 2005; Tumer and Brainard 2007) and
more recently in humans with arm-reaching tasks (Wu et al.
2014). A direct link between our study and that of Wu et al.
(2014) cannot be firmly established yet since they investigated
the link between baseline performance and motor adaptation
while we investigated the link between motor adaptation and
transfer. However, our results seem to relate to their findings,
suggesting that overall, variability may reflect action explora-
tion, which in turn could facilitate sensorimotor adaptation.
There are multiple types of variability and for instance motor
variability at the execution level differs from variability in task
goal (Ranganathan and Newell 2013). Increasing the variabil-
ity in task goal is also well known to improve motor learning
(Braun et al. 2009; Kitago and Krakauer 2013) as it stimulates
problem-solving capacities. However, adaptation with multiple
target directions to a visuomotor rotation (Wang and Sainburg
2004b) and a novel force field (Mattar and Ostry 2007) was not
found to facilitate interlimb transfer. These findings raise the
question as to why variability of movement execution is so
beneficial to adaptation and interlimb transfer.
Although motor variability has often been considered as
noise, it is now thought to be beneficial to learning since it
would reflect action exploration (Kanai and Rees 2011; Her-
zfeld and Shadmehr 2014; Wu et al. 2014). Variability pre-
sumably allows the nervous system to develop a general
knowledge of the relationship between efferent signals and
their actual consequences, which in turn would enable the
selection of the most appropriate movement strategy. New
experiments are necessary to clarify how exactly variability
facilitates sensorimotor adaptation and its generalization.
PV during the adaptation phase also seemed to determine
intermanual transfer in our study. Kitazawa et al. (1997)
previously showed that the velocity of reaching had a substan-
tial influence on sensorimotor adaptation and our study seems
to extend this finding to the interlimb transfer of force-field
adaptation. Indeed, we observed a greater PV in so-called
“Transfer” subjects compared with “No transfer” subjects. This
could be related to the notion that fast movements, with a high
PV, are mostly controlled based on feedforward control mech-
anisms because there is less time to process peripheral sensory
feedback during movement execution. Minimizing errors of
fast movements thus relies more on the update of motor
planning processes, which would facilitate interlimb transfer.
One hypothesis to experimentally test is that manipulating
movement speed should influence interlimb transfer such that
an increased movement speed would result in an increased
interlimb transfer.
LQ Influences the Interlimb Transfer of Sensorimotor
Adaptation
LQ, a quantitative assessment of handedness in everyday
activities (Oldfield 1971), was the second most important
predictor of interlimb transfer in our study. Many studies with
healthy subjects and stroke patients (Wang and Sainburg
2004a; Mutha et al. 2012; Sainburg 2014) support the idea that,
in right-handers, the left hemisphere is specialized for the
control of limb dynamics. While the control of dominant hand
movements involves greater activation of the contralateral
hemisphere compared with the ipsilateral one (Dassonville et
al. 1997; Volkmann et al. 1998; Pool et al. 2014), movements
of the nondominant hand are controlled by a more balanced
pattern of hemispheric activation (Kawashima et al. 1993; Kim
et al. 1993; Ziemann and Hallett 2001). There is also neuro-
psychological evidence that damage to the dominant left hemi-
sphere can impair the motor function of both right and left
hands (Haaland and Harrington 1996; Sainburg 2014). This
body of work suggests that, for our right-handed participants,
the internal representation of the right hand dynamics was
updated during the adaptation phase primarily in the left
hemisphere. The contribution of the left hemisphere to the
control of the NDA may be the basis of interlimb transfer in
right-handers, an idea consistent with a study (Criscimagna-
Hemminger et al. 2003) that showed interlimb transfer of
force-field adaptation in a split-brain patient. In our study with
right-handers, the fact that the amount of interlimb transfer
increased with the LQ may be explained by a greater activation
of the contralateral (left) hemisphere in strongly lateralized
subjects when using the dominant (right) hand (Dassonville et
al. 1997; Pool et al. 2014) and/or by more involvement of the
ipsilateral (left) hemisphere in strongly lateralized subjects
when using the nondominant (left) hand (Verstynen et al.
2004).
One might argue, contrarily, that less neural asymmetry and
more bilateral brain activity in general might favor interlimb
transfer. When Chase and Seidler (2008) investigated interlimb
transfer of adaptation to a visuomotor rotation, they observed
in left-handers that interlimb transfer increased as the degree of
handedness decreased. Also, when they studied whether learn-
ing a sequence of finger movements with one hand transferred
to the other hand, they found that, in right-handers, interlimb
transfer increased as the degree of handedness decreased. We
presume that there are differences between adapting to a novel
dynamic environment and adapting to a visuomotor rotation
(Krakauer et al. 1999; Rabe et al. 2009) or learning a sequence
of finger movements. However, these and our findings collec-
tively highlight the importance of handedness for interlimb
transfer.
Although all left-handers could adapt to the novel force field
with their left DA, we did not observe significant interlimb
transfer on the right NDA, perhaps because of a small sample
size. However, a large heterogeneity was found across subjects
and a subject-by-subject analysis indicated that transfer was in
fact observed in a few subjects. These “Transfer” subjects
could be discriminated from so-called “No transfer” subjects
based on the LQ as well as variability of initial direction during
late adaptation. The fact that PV was not found to be a
significant predictor of transfer likely results from the finding
that left-handers were faster than right-handers, which is con-
sistent with previous work (Kilshaw and Annett 1983). Be-
cause LQ was found to influence interlimb transfer of all, right-
and left-handed individuals, our findings suggest that it is the
extent of handedness that influences intermanual transfer of
sensorimotor adaptation.
In summary, LQ and other subjects’ characteristics such as
movement kinematics are important for interlimb transfer,
2772 INDIVIDUAL CHARACTERISTICS DETERMINE INTERLIMB TRANSFER OF ADAPTATION
J Neurophysiol •doi:10.1152/jn.00749.2015 •www.jn.org
which does not result solely from environmental conditions.
These individual characteristics should be carefully considered
as they could explain the large heterogeneity of results in the
literature.
ACKNOWLEDGMENTS
We thank G. M. Gauthier and A. Donneaud for help when developing the
rotating platform; C. Martha, P. Roussel, and J. Griffet for help with the
questionnaires; N. Schweighofer for advice with the experimental design; Y.
Wazaefi and B. Fertil for help with data analysis; and F. Danion and P. Mutha
for helpful comments.
GRANTS
This work benefited from the financial support of the Institute of Movement
Sciences (ACI), Aix-Marseille University (International Relations Grant), the
Royal Society (International Exchange Grant), and Centre National de la
Recherche Scientifique (PICS, DEFISENS, and AUTON Programs).
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: H.Z.L., J.-L.V., F.B., and F.R.S. conception and
design of research; H.Z.L. and F.R.S. performed experiments; H.Z.L. analyzed
data; H.Z.L., J.-L.V., R.C.M., J.C., L.B., C.B., and F.R.S. interpreted results of
experiments; H.Z.L. prepared figures; H.Z.L. and F.R.S. drafted manuscript;
H.Z.L., J.-L.V., R.C.M., J.C., L.B., C.B., and F.R.S. edited and revised
manuscript; H.Z.L., J.-L.V., R.C.M., J.C., F.B., L.B., C.B., and F.R.S. ap-
proved final version of manuscript.
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