6412 • The Journal of Neuroscience, April 10, 2013 • 33(15):6412– 6422

Behavioral/Cognitive

Human Sensorimotor Cortex Represents Conflicting
Visuomotor Mappings
Kenji Ogawa1 and Hiroshi Imamizu1,2
Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, Keihanna Science City, Kyoto 619-0288, Japan, andCenter for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University, Suita, Osaka
565-0871, Japan

1
2

Behavioral studies have shown that humans can adapt to conflicting sensorimotor mappings that cause interference after intensive
training. While previous research works indicate the involvement of distinct brain regions for different types of motor learning (e.g.,kinematics vs dynamics), the neural mechanisms underlying joint adaptation to conflicting mappings within the same type of perturbation (e.g., different angles of visuomotor rotation) remain unclear. To reveal the neural substrates that represent multiple sensorimotor
mappings, we examined whether different mappings could be classified with multivoxel activity patterns of functional magnetic resonance imaging data. Participants simultaneously adapted to opposite rotational perturbations (!90° and " 90°) during visuomotor
tracking. To dissociate differences in movement kinematics with rotation types, we used two distinct patterns of target motion and tested
generalization of the classifier between different combinations of rotation and motion types. Results showed that the rotation types wereclassified significantly above chance using activities in the primary sensorimotor cortex and the supplementary motor area, despite no
significant difference in averaged signal amplitudes within the region. In contrast, low-level sensorimotor components, including tracking error and movement speed, were best classified using activities of the early visual cortex. Our results reveal that the sensorimotor
cortex represents different visuomotor mappings, which permits joint learning and switching between conflicting sensorimotor skills.

Introduction
Humans can concurrently learn and switch among various motor
skills. There are two kinds of multiple-skill acquisition: one islearning the different types of perturbations, such as kinematics
versus dynamics transformation, which involves independent
processes in the sense that joint learning of two separate skills
does not cause interference (Krakauer et al., 1999). The othertype is adaptation to different parameters within the same type of
perturbation, such as varying angles of visuomotor rotation,
which sometimes induces conflicting interference (Wigmore etal., 2002). For example, people do not confuse riding a bike withdriving a car, but they have difficulty in switching between driving on the left and on the right side of the road in different
countries. Related to the latter type of adaptation, behavioral
experiments have shown successful learning for conflicting force
fields (Wada et al., 2003; Osu et al., 2004; Nozaki et al., 2006;
Howard et al., 2008) or opposite visuomotor rotations (Cunningham and Welch, 1994; Ghahramani and Wolpert, 1997; Ima-

Received Oct. 1, 2012; revised Feb. 20, 2013; accepted Feb. 27, 2013.
Author contributions: K.O. and H.I. designed research; K.O. performed research; K.O. analyzed data; K.O. and H.I.
wrote the paper.
This work was supported in part by a grant-in-aid for Japan Society for the Promotion of Science Fellows (No.
23-8279) to K.O. We thank Drs. Mitsuo Kawato, Hirokazu Tanaka, and Nicolas Schweighofer for helpful comments onan early version of this manuscript, and Satoshi Tada and Yuka Furukawa for technical support.
The authors declare no competing financial interests.
Correspondence should be addressed to Dr. Kenji Ogawa, ATR Cognitive Mechanisms Laboratories, 2-2-2, Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan. E-mail: k.ogawa@atr.jp.
DOI:10.1523/JNEUROSCI.4661-12.2013
Copyright © 2013 the authors 0270-6474/13/336412-11$15.00/0

mizu et al., 2007b; Choi et al., 2008) after intensive training.
Another study showed that exposure to various angles of visuomotor rotation induces structural learning (Braun et al., 2009).
Previous literature revealed the involvement of separate brain
regions in the learning of different types of perturbation (Imamizu et al., 2003; Krakauer et al., 2004; Girgenrath et al., 2008 forhuman neuroimaging; Rabe et al., 2009 for a lesion study), whichis consistent with behavioral evidence of independent learning aswell as the computational theory (Wolpert and Kawato, 1998). In
contrast, the neural mechanisms underlying joint adaptation to
the multiple parameters within the same types of perturbation
remain unclear. To reveal the neural substrates that represent
multiple sensorimotor mappings, we used functional magnetic
resonance imaging (fMRI) to measure brain activities related toconflicting visuomotor mappings. However, the conventional
univoxel fMRI analysis evaluates overall increases in activation of
specific regions and would not be sensitive to distributed representations contained in patterns of voxel activity. To overcome
this issue, this study used multivoxel pattern analysis (Haynes
and Rees, 2005; Kamitani and Tong, 2005; Norman et al., 2006)
for examining whether different mappings could be classified
with distributed patterns of neural activities.
Using visuomotor tracking, participants simultaneously
adapted to two types of visuomotor rotation (i.e., dual adaptationtask) in which the cursor position of a joystick was rotated by!90° or "90° around the starting position. After adaptation, weclassified the two rotation types during the tracking with themultivariate activity pattern of fMRI. One possible concern isthat tracking under different rotation types could be accompa-

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

J. Neurosci., April 10, 2013 • 33(15):6412– 6422 • 6413

min. The block order was counterbalanced as
A-B-B-A-A-B-B-A… with A and B pseudoranslow
fast
domly assigned the two rotation types acrosssessions. Unlike previous studies (Osu et al.,2004), we did not provide visual or auditory
cues for different rotation types to equate sen2s+90°sory inputs between conditions.
Stimuli were presented on a liquid crystal
display and projected onto a custom-madeviewing screen. Subjects lay supine in the scan400
ner and viewed the screen via a mirror, being
21 sunable to see their hand throughout this task.
They used their right index and middle fingers
090°
to control the joystick, with the right upperarm immobilized using foam pads to minimize
4s
body motions. A custom-made bite bar was-400
used for all participants to prevent head mo-400
0
400
time
(pixel)tions for all the scanning sessions. The amountC 400
of subjects’ head motion, which was estimatedTarget 400
400
Cursorfrom alignment of functional images (see below), ranged from 0.07–2.0 mm, with a median
200
200
200
of 0.36 mm within a session.
00
Functional localizer scans. On a separate dayfrom the main rotational experiment, an inde-200
-200
−200
pendent localizer scan was performed for allparticipants to define sensorimotor areas re-400
-400
−400
lated to visuomotor tracking. The task was the
0
3
6
9
12
15
18
21
0
3
6
9
12
15
18
21
same as in the main experiment except that it
Time (s)Time (s)had a longer baseline period to reliably detect
Figure 1. A, Time course of experiment. After display of start message for 2 s, target (circle) and cursor (cross) were presented increased fMRI responses related to visuomoon the screen. Subjects continuously tracked a moving target with cursor using joystick for 21 s, with rotational perturbations of tor tracking, and that subjects used a normal!90° or "90°. Task blocks were interleaved with rest periods of 4 s. B, Examples of target trajectory under different rotation types joystick without rotation. One session lasted
and velocity types in single block. C, Examples of horizontal positions of target with those of cursor within single block of first (left) #12 min and had 24 task blocks of 15 s, interleaved with 15 s rest periods using a fixationand last (right) practice sessions.
display.
Target trajectory. Stimuli were generated using Psychophysics Toolbox (http://www.psynied with differences in movement kinematics, which is a majorchtoolbox.org; Brainard, 1997) for MATLAB (MathWorks). The x- andconfound for decoding. We thus used two patterns of target motions
y-components of the target trajectory were the sums of sinusoids whose
by manipulating target velocities (Imamizu et al., 2000, 2003) andamplitudes and frequencies were pseudorandomly determined using thetested the classifier generalization between different combinations ofsame procedure as in our previous studies (Imamizu et al., 2000, 2003,
rotation and motion types. This cross-validation ensures that the2004, 2007a). Since these parameters were determined pseudorandomly
decoding of rotation types is not confounded by differences infor each run, the target paths differ across sessions and among subjects. In
low-level kinematics or reafferent sensory inputs associated withaddition, two different patterns of target trajectory were created to disthe rotational perturbations. Successful classification of rotationsociate low-level movement kinematics or reafferent sensory inputs withtypes would support the existence of neural substrates representtwo rotation types (see below, Multivoxel pattern analysis). We applied
three kinds of manipulations in target motion: (1) velocity: fast (averageing conflicting visuomotor mappings.
speed of 378 pixels/s) or slow (265 pixels/s), (2) position: bias of either
Materials and Methods!50 or "50 pixels introduced to on-screen position of target trajectory
for both x- and y-axes, and (3) phase between x- and y-components:
Subjects. Subjects were seven male volunteers with a mean age of 30.0
manipulated to be in-phase (results in positive correlation) or anti-phase
years (range, 22– 49 years). All subjects were right-handed, as assessed by(negative correlation) by introducing different phases into the x- and ya modified version of the Edinburgh Handedness Inventory (Oldfield,
components of target sinusoids. The position and phase were explicitly1971). Written informed consent was obtained from all subjects in acmanipulated in addition to the velocity because some participantscordance with the Declaration of Helsinki. The experimental protocol
showed differences in these measures between the rotation types in ourreceived approval from the local ethics committee.
preliminary behavioral experiment. These three types of manipulations
Task procedures. We used a continuous visuomotor tracking task
were introduced in fixed combinations: fast target was always accompa(Imamizu et al., 2000, 2003, 2004, 2007a): subjects moved a small crossnied with positional bias of !50 and "50 pixels for x- and y-axis, respechair cursor on the screen with a joystick and continuously tracked atively, and with in-phase motion, which generated two different types ofmoving target. After a starting message shown for 2 s, target and cursor
target trajectory. Figure 1 illustrates an example of the target trajectory ofwere presented on the screen. The target was a small green circle on a darka single block under each rotation and velocity type (Figure 1B) andbackground, which moved around within a square subtending horizonexamples of the horizontal positions of the target together with those of atal and vertical visual angles of #14°. The cursor was a small yellow cross.
cursor within a single block of the first (left) and last (right) practice
We introduced a rotational perturbation between cursor positions and asessions (Figure 1C). Hereafter, these two target types are referred to as
joystick angle of 90° around the screen center, in either clockwise or“fast” and “slow” conditions (Fig. 1B). Note that target motion was still
counterclockwise direction. During the task block of 21 s, subjects conpseudorandom but had these constraints. The two target patterns (fast,tinuously tracked the moving target. A fixation dot was displayed for 4 sslow) were manipulated independently of rotation types, and the numduring intertrial intervals (ITI) (Fig. 1A). After a practice session to bebers of the two target types were equalized between the two rotation
come familiarized with the scanner environment, the subject underwent
five sessions, with each consisting of 24 blocks. One session lasted #11
types.

B

Horizontal position (pixel)

RotationA

Velocity

6414 • J. Neurosci., April 10, 2013 • 33(15):6412– 6422

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

Behavioral training. Before the fMRI scanA Left
Right
Lateralning, subjects underwent 9 –16 sessions of
Mot Som SPL
view
training outside the MRI scanner for 3 or 4 d.
The training session is the same as those usedCS
for fMRI scanning, including the same rotaPoCS
tion perturbations, except that ITI is shorter(2 s). One session lasted #10 min. One subject (author K.O.) did not participate in the
training sessions because he had already undergone sufficient practice during the preliminary experiment.
Behavioral analysis. Cartesian coordinates ofthe on-screen target and cursor positions wereMedialrecorded at 60 Hz. We then calculated the
Right
Left
SMA
SMA
view
following measures separately for x- and ycomponents: (1) tracking error defined as a
Euclidean distance between target position andcursor position, (2) distance of cursor trajectory, (3) correlation coefficient between targetVisVisand cursor trajectories, and (4) correlation
coefficient between x- and y-components.
These behavioral measures were calculated foreach block, normalized as a z-score among alltrials within subjects and then compared both
between the two conditions of rotation (!90°
B Leftz = 20z = 17z = 23vs "90°) and between those of velocity (fast vsCBLaslow). The power spectrum of cursor velocity
PF
CBLp
was calculated with fast Fourier transform tomeasure the intermittency, which evaluates thefrequency of on-line corrections based on visual feedback in manual tracking (Miall, 1996; Figure 2. The activated areas of localizer scans, averaged over subjects ( p % 0.001 uncorrected for multiple comparisons withan extent of 50 voxels) in the cerebral cortex (A) and in the cerebellum (B). Mot, Lateral premotor and primary motor area; Som,
Foulkes and Miall, 2000).
MRI acquisition. A 3 T Siemens Trio scanner primary somatosensory area; SMA, supplementary motor area; SPL, superior parietal lobule; Vis, early visual area; CBLa, anterior
with a 12-channel head coil was used to per- part of cerebellum; CBLp, posterior part of cerebellum; CS, central sulcus; PoCS, postcentral sulcus; PF, primary fissure.
form T2*-weighted echo planar imaging (EPI).
of 128 s, and serial correlations among scans were estimated with an
A total of 222 and 246 scans were acquired for the main and localizer
autoregressive model implemented in SPM8. This analysis yielded 24
sessions, respectively, with a gradient echo EPI sequence. The first four
independently estimated parameters (! values) per session for each inscans were discarded to allow for T1 equilibration. Scanning parameters
dividual voxel, which were subsequently used as inputs for decoding
were TR, 3000 ms; TE, 30 ms; FA, 80°; FOV, 192 $ 192 mm; matrix, 64 $analysis.
64; 50 axial slices; and slice thickness, 3 mm without gap. T1-weighted
Definition of regions of interest. The regions of interest (ROIs) wereanatomical imaging with an MP-RAGE sequence was performed with thedefined for each subject, both functionally with activations of localizer
following parameters: TR, 2250 ms; TE, 3.06 ms; FA, 9°; FOV, 256 $ 256
scans (Fig. 2) and anatomically with the automated anatomical labelingmm; matrix, 256 $ 256; 192 axial slices; and slice thickness, 1 mm with(AAL) toolbox (Tzourio-Mazoyer et al., 2002) or Brodmann areas inout gap.
cluded in the MRICro software (http://www.mricro.com). First, the loProcessing of fMRI data. Image preprocessing was performed using
calizer scan was used to identify the activated areas that showed
SPM8 software (Wellcome Department of Cognitive Neurology;
significantly higher response during a task epoch compared with the
http://www.fil.ion.ucl.ac.uk/spm). All functional images were first rebaseline period with an uncorrected threshold of p % 0.01 at voxel level.
aligned to adjust for motion-related artifacts. The realigned images were
The primary motor and lateral premotor areas (Mot) and the somatothen spatially normalized with the Montreal Neurological Institutesensory area (Som) were then anatomically masked with the precentral(MNI) template, based on affine and nonlinear registration of coregisand postcentral cortices of AAL, respectively. The supplementary motor
tered T1-weighted anatomical images (normalization procedure ofarea (SMA) was also defined using AAL mask. The early visual area was
SPM), and resampled into 3 mm cube voxels with sinc interpolation. All
anatomically masked within Brodmann areas 17 and 18. In the posterior
images were spatially smoothed using a Gaussian kernel of 8 $ 8 $ 8 mmparietal cortex, we selected the medial intraparietal cortex (mIPS), which
full-width at half-maximum. The smoothing was not performed except
is related to visually guided hand movements (Ogawa and Inui, 2012).
for the localizer scans, as this could blur fine-grained information conThe parietal ROI was masked both with mIPS having a 15 mm radiustained in multivoxel activity (Mur et al., 2009).
sphere around the peak average of previously reported coordinates ofUsing the general linear model, the 24 blocks per session were modeled["29, "51, 58] (Grefkes et al., 2004; Prado et al., 2005) and anatomicallyas separate 24 box-car regressors that were convolved with a canonicalwith the superior parietal lobule (SPL). The anterior and posterior parts
hemodynamic response function. Our previous study using the sameof the cerebellum were anatomically defined based on the primary fistracking task showed that switching of different visuomotor perturbasure. As a control ROI, we chose voxels outside of the brain centered attions takes #5 s after block onset (Imamizu et al., 2004). We thus re[57, 60, 57] with a radius of 15 mm (nonbrain). Several areas of one
moved the initial 6 s, corresponding to two fMRI scannings, to remove
subject did not show significant activations in the localizer scan, and thustransient blood oxygenation level-dependent activity at task onsets assoROIs were based on activations of group-level results of other subjects
ciated with task switching or increased tracking error, and thus box-car(n & 6) only for this participant. Table 1 describes mean (SD) MNI
length was 15 s covering the latter part of a 21 s block. The behavioral
coordinates and the number of voxels of ROIs.
analysis was also conducted by removing the first 6 s period correspondMass univariate analysis. For the conventional univariate analysis ofing with the fMRI analysis. The six movement parameters resulting fromindividual voxels, we directly compared two conditions for both rotathe realignment stage were modeled as confounding covariates. Lowtions (!90° vs "90°) and velocity (fast vs slow). Contrast images of eachfrequency noise was removed using a high-pass filter with a cutoff period

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

J. Neurosci., April 10, 2013 • 33(15):6412– 6422 • 6415

Table 1. Mean ROI coordinates and sizes (number of voxels) with SD in parenthesis
across subjects
MNI coordinates
ROI namexyz

ROI size

Mot
Som
SMA
SPL
Vis
CBLa
CBLp
Nonbrain"22 (4.9)"34 (7.0)"4 (6.8)"32 (6.5)"1 (17.6)
12 (7.9)
6 (20.5)
57 (0)"20 (6.4)"38 (7.7)"16 (8.3)"56 (9.8)"86 (11.3)"60 (9.4)"64 (7.6)
60 (0)

74 (5.2)
68 (4.4)
68 (6.7)
62 (3.7)"5 (7.6)"15 (4.9)"26 (12.5)
57 (0)

655 (139)
712 (92)605 (203)
900 (158)
437 (55)1234 (239)2131 (333)500 (0)subject, generated using a fixed-effects model, were taken into the group
analysis using a random-effects model of a two-way ANOVA with rotation and velocity types as within-subject factors. Activation was reportedwith a lenient threshold of p % 0.01 uncorrected for multiple comparisons at voxel level with an extent threshold of 10 voxels. The ROI analysis
was additionally performed by comparing averaged parameter estimates(! values) between the two conditions of rotation types as well as velocity
types.
Multivoxel pattern analysis. The multivariate classification analysis offMRI was performed with a binary classifier based on linear support
vector machine (SVM) implemented in LIBSVM (http://www.csie.ntu.
edu.tw/#cjlin/libsvm/), with default parameters (a fixed regularization
parameter C & 1). Parameter estimates (! values) of each trial of voxels
within ROIs were used as inputs to the classifier.
We conducted two types of classification analysis. First, we classified
the rotation types with cross-validation between different velocity types.
The decoder was first trained to classify !90 and "90 rotations with fast
target trials, and the same decoder was tested to classify !90 and "90rotations with slow target trials (Fig. 3A) to test whether the decoder for
rotation types could generalize between different kinematics (i.e., target
motion) patterns. We also conducted classification in the reverse direction and estimated the averaged decoding accuracy. The classification ofvelocity types between different rotation types was conducted in the samemanner. The decoder was first trained to classify fast and slow target with!90° trials, and the same decoder was tested to classify fast and slow with"90° trials. Second, we used different combinations of rotation types andvelocity types for training and testing datasets: the classifier was firsttrained to discriminate the trials with rotation of !90° and fast target
from those of "90° rotation and slow target. The same decoder was thentested to see if it could classify the trials of "90° rotation and fast target
separately from those of !90° rotation and slow target. We also conducted classification in the reverse direction. In this analysis, manipulation of rotation types and velocity types was orthogonal to each other,and thus classification accuracy of rotation was the opposite to that ofvelocity (i.e., the accuracy of rotation equals to 100% minus the accuracyof velocity; Fig. 3B). This was done to explicitly dissociate low-level
movement kinematics or reafferent sensory inputs associated with rotation types, since all types of behavioral differences including tracking
error and movement distance were larger in velocity types comparedwith rotation types (see Results, Behavioral performance). Such a “crossclassification,” a cross-validation between trials with different sets oftasks or stimuli, has been previously used to investigate the similarity orinvariance of neural representations by testing the generalization of aclassifier between different conditions or modalities (Etzel et al., 2008;
Knops et al., 2009; Stokes et al., 2009; Meyer et al., 2010; Oosterhof et al.,2010; Gallivan et al., 2011a; Kahnt et al., 2011; Ogawa and Inui, 2011,2012; Corradi-Dell’Acqua et al., 2011). A cross-validation was conducted
10 times (5 sessions $ 2 combinations) and then the average accuracy
was estimated.
Tests of statistical significance for classification accuracy were conducted in both parametric and nonparametric methods. First, a twosided t test was used to determine whether the observed decoding
accuracy was significantly higher than chance (50%) with intersubject
difference treated as a random factor (df & 6). To control for the problem

of multiple comparisons, we applied the Holm–Bonferroni procedure(Holm, 1979) based on the number of ROIs. Second, we conducted a
nonparametric test, since the observed classification accuracies may not
be normally distributed (Nichols and Holmes, 2002). We conducted arandomization test that was identical to the decoding analysis described
previously, except that we randomly shuffled correspondence between
the fMRI activations and the condition labels before training the classifier
within each subject and obtained average decoding accuracy across participants. This procedure was separately repeated 1000 times for each
ROI by reshuffling the labels each time, which created an empirical distribution of decoding accuracy under a null hypothesis of chance. A
significant above chance accuracy of p % 0.05 was assumed if the decoding accuracy with correct labeling exceeded the 95th percentile of the null
distribution.
Complementary to the a priori ROI analysis, we additionally conducted a volume-based “searchlight” analysis (Kriegeskorte et al., 2006).
Cross-classification with different pairs of rotation and velocity types(Fig. 3B) was performed using multivoxel activation patterns within a 9mm radius sphere (searchlight) containing at least 100 voxels (maximumof 123 voxels). The searchlight moved over the gray matter of the wholebrain, and the average classification accuracy for each searchlight withleave-one-session-out cross-validation was assigned to the sphere’s center voxel. The resulting map of the decoding accuracy was averaged oversubjects. Results were first thresholded at voxel level using a randomization test as in the ROI analysis. We conducted the same searchlight analysis by randomly shuffling the correspondence between the voxel
activities and the condition labels, and estimated the average accuracy
across participants. This procedure, which was repeated 100 times byreshuffling the labels each time, created an empirical distribution of decoding accuracy under a null hypothesis of chance, which was then collapsed over all searchlights (the number of searchlights was 35,512, andso the number of data in the null distribution was 3,551,200). For eachsearchlight (i.e., each center voxel), a significant above-chance accuracyof p % 0.0001 was assumed if the observed accuracy with correct labelingexceeded the 99.99th percentile of the null distribution. To correct for
multiple comparisons, a cluster-level threshold was further applied witha randomization test (Nichols and Holmes, 2002; Oosterhof et al., 2010)
using the same permutation distribution generated above. We obtained amaximum cluster size (i.e., the number of voxels) for each randomization sample at the same voxel-level threshold, which yields an empiricalnull distribution of a maximum cluster extent. Clusters were reported as
significant at a cluster level of p % 0.01 if the observed number of voxels
within the cluster with correct labeling exceeded the 99th percentile ofthis null distribution.
Additional experiment with normal joystick. To further confirm that theclassification result of rotation types was not confounded with differences in movement kinematics associated with rotational perturbations,
we conducted another experiment with five of the seven subjects who
participated in the main experiment. This time subjects used a normal(not rotated) joystick to track a target whose motions followed their own
joystick trajectory of the previous rotational experiments. The task design and number of trials as well as sessions per subject were the same as
those in the rotational experiment. We hypothesized that if differences inmovement patterns between rotational conditions had contributed tosuccessful classification of rotations, significant classification accuracy
could also be obtained in this control experiment.
Eye-movement recording. Our subjects freely made eye movements
during the tracking, which could be a potential confound. To exclude thepossibility that differences in eye movements contributed to the successful classification of rotation types, we measured the eye positions during
the rotational tracking outside the fMRI scanner after the main fMRI
experiment. The horizontal and vertical eye positions of four among
seven subjects who participated in the main experiment were measured at 60 Hz with the Tobii X120 infrared recording system (Tobii
Technology;http://www.tobii.com). The task sequences were the same as
those of the fMRI experiment except for a shorter trial duration (12 s) tominimize eye blinks; five sessions were conducted per participant. We
then ran the classification analysis based on the time series of the eye
positions as well as the power spectrum of the eye movement velocity

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

6416 • J. Neurosci., April 10, 2013 • 33(15):6412– 6422

Train

+90&fast
90&fastTestClassify+90 rotation+90&slow
90&slowTrain+90&fast+90&slow

Test

Activity of voxel #2

Results

A
Activity of voxel #2

with the same procedure as multivoxel patternanalysis (MVPA) using different combinationsof rotation types and velocity types (Fig. 3B).

-90

90&fast90&slow

Correlation coefficient

Normalized error

Normalized error

Activity of voxel #2

Behavioral performance
slow
Over the training sessions, subjects gradClassify
fast velocity
ually learned two rotational types, as indiActivity of voxel #1
Activity of voxel #1# correct: 6# correct: 7
cated by the decrease in tracking errors as# incorrect: 4# incorrect: 3
the number of sessions proceeded (Fig.
Decoding accuracy of velocity: 6/10 = 60%
Decoding accuracy of rotation: 7/10 = 70%
4A). We compared the average tracking# correct ofBrotation : velocity
errors of the first and the last three sesTest
A: 4
Label
Train
+90&slow
sions, respectively, for each subject, andA:
1+90&fast
A
Trial types:
found a significant decrease (paired t test,A:
2
90&slowA+90&fast
90&fastA: 3
90&fastt(5) & 6.49, p & 0.001; Fig. 4B). We nextA
+90&slowA
analyzed the maximal cross-correlation
90&slowA
Decoding accuracy ofAbetween the trajectories of the target androtation: 7/10 = 70%
velocity: 3/10 = 30%
the cursor averaged during the first andlast three sessions. This revealed a significant increase in correlation coefficients,
Activity of voxel #1
indicating that the cursor followed thetarget trajectory more accurately in the
Figure 3. Schematic illustrations of MVPA. A, Classification of rotation types with cross-validation between different velocity
later sessions (paired t test, t(5) & 4.55, p % types; the decoder was first trained to classify !90 and "90 rotations with fast target trials, and the same decoder was tested to
0.01; Fig. 4C).
classify !90 and "90 rotations with slow target trials (left). Classification of velocity types with cross-validation between
The time series of tracking errors different rotation types was also conducted (right). B, Procedure of cross-classification, where trials with different combinations ofwithin a 21 s block was compared between rotations (!90° vs "90°) and velocity types (fast vs slow) were used for the decoder training and testing.
rotation and velocity types (Fig. 5A). Because a transient increase in errors was
B
C
A3
found after block onset, behavioral and fMRI data were used only2
after 6 s of trial onset (Fig. 5A, gray areas) for later analysis. We
2
0.8
also compared the power spectrum of the cursor velocity between
11
the two conditions (Fig. 5B), which showed the differences in the
0.60cursor movement frequency or the intermittency of the feedback0
control. The figures show that differences in both tracking er0.4−1−1
rors and power spectrum were larger between velocity types
0.2
compared with rotations. For quantitative comparison, we calcu−2
147
10 13 16
Early
Late
Early
Late
lated various behavioral measures for cursor trajectory and comNumber of sessions
sessions
sessions
sessions
sessions
pared the normalized absolute differences between the two
Figure 4. A, The average (error bars indicate SDs) tracking error as the number of sessions
conditions of rotation and velocity types (see Materials andperformed in the practice sessions. B, The average tracking error of first and last three practice
Methods, Behavioral analysis for details; Fig. 5C). We then comsessions. C, Maximal cross-correlation between trajectories of target and those of cursor averpared the differences between the rotation and velocity types andaged during first and last three sessions. Each dot represents the results of an individual particfound significantly larger differences between the two velocity
ipant (n & 6).
types compared with the rotation types for measures including
tracking error and movement distance, while no measure
MVPA
showed significantly greater differences in the reverse direcFirst, we classified the rotation types with the cross-validation
tion. These behavioral results indicate that the kinematic difbetween different velocity types to test whether the decoder
ferences are larger in the two velocity types compared withtrained to classify two rotation types in one motion pattern could
those of the rotation types, which ensures that a higher decodbe generalized to the other (Fig. 3A). While the statistical results
ing accuracy of rotation types than velocity types later foundwere described based on the corrected threshold unless otherwise
in MVPA cannot be due to kinematic differences.
mentioned, we included both the corrected and uncorrected significance in Figure 7 for completeness. The results showed significant above-chance classification accuracy of rotation types in
Mass univariate analysis
Mot (62.2%, t(6) & 10.97, p % 0.001), Som (67.3%, t(6) & 4.14,
We directly compared two conditions of both rotation (!90° vs
p % 0.01), SMA (62.3%, t(6) & 6.10, p % 0.001), early visual area,"90°) and velocity (fast vs slow), using the traditional univariate
Vis (60.6%, t(6) & 4.80, p % 0.005), anterior part of cerebellum,
analysis of single voxels. A significant main effect of velocity types
CBLa (61.5%, t(6) & 3.34, p % 0.05), and posterior part of cerewas observed in the early visual areas bilaterally at a lenient
bellum, CBLp (61.3%, t(6) & 4.46, p % 0.005). The successful
threshold of p % 0.01 uncorrected for multiple comparisons (Fig.
classification of velocity types between different rotation types
6A). No significant main effect of rotation types or interactions
was found in Vis (78.9%, t(6) & 11.33, p % 0.001) (Fig. 7A).
was found at the same threshold. In addition, the ROI analysis
Since we found small differences in the behavioral measurescomparing averaged parameter estimates (! values) between thetwo conditions of rotation and velocity within each ROI revealed
between two rotation types and the velocity types (Fig. 5C), theno significant difference in either rotation or velocity comparison
successful classification of rotation types might reflect differences(Fig. 6B).
in the low-level kinematics or the resultant visual feedback ofOgawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

J. Neurosci., April 10, 2013 • 33(15):6412– 6422 • 6417

chance accuracies found by the t test werealso significant by the randomization test.
The same classification was also con200
ducted by equating the number of voxels
200
across ROIs to eliminate differences infeature size. The voxels in each ROI were150
150
selected in the order of highest t value ofthe localizer scan, which was based on theunivariate analysis, until the number of
100
100
0
3
6
9
12
15
18
21
0
3
6
9
12
15
18
21
voxels reached 400 for each subject
Time (s)Time (s)(Pereira et al., 2009). This replicated theB 99previous results: significant decoding acfast+90°
curacy of rotation for Mot (56.2%, t(6) &slow
90°
4.79, p % 0.005), Som (60.3%, t(6) & 2.74,
66
p % 0.05), and SMA (57.3%, t(6) & 2.45,
p % 0.05), and that of velocity for Vis
3
3(70.0%, t(6) & 5.59, p % 0.005) with uncorrected p values.
Next, the same classification was per00
formed with averaged ! values of all vox0
2
4
6
0
2
4
6
els within an ROI as inputs. No
Frequency (Hz)Frequency (Hz)C
significantly higher than chance accuracy
****
**
***
was found in any ROI, which indicates***that decoding was based on differences in
1.5
1.5
1.5
1.5
distributed spatial patterns of multiple
voxels, rather than overall amplitude dif1
1
1
1
ferences within the ROI (Fig. 7C). This is
0.5
0.5
0.5
0.5
also supported by the voxel bias map,
x
x
Error
x
which displays the averaged weights (posyyyCursor0
0
0
0
itive or negative) of the classifier across 10
Rotation Velocity
Rotation Velocity
Rotation Velocity
Rotation Velocity
folds (i.e., iterations) in the crossError
Distance
Correlation of
Correlation of
p < 0.05*
target & cursor
x&y
validation for individual voxels within** p < 0.01
ROIs; this map showed intermingled patFigure 5. Behavioral results during fMRI scanning. A, Time series of mean tracking error within block. Two rotation and velocity terns of voxels with two rotational types
types were compared in left and right figures, respectively. Trial onset of 6 s (gray areas) was removed for later analysis. Shaded (!90 vs "90 biased voxels) within ROIs
region with colors denotes SD across subjects. B, Power spectrum of cursor trajectory averaged over trials and two dimensions in all subjects (Fig. 8A for a single typicalduring fMRI scanning. Two rotation and velocity types were compared in left and right figure, respectively. C, The comparisons of subject). In addition, the bias patterns bebehavioral measures between rotation and velocity types.
tween different pairs of participants
showed low correlations around zero (Fig.
cursor motions. The higher than chance decoding accuracy for
8B), which indicates idiosyncratic patterns of weights specific torotations together with the velocity types in Vis suggests thateach subject.
slight differences in kinematics could also contribute to the clasFinally, the searchlight map revealed the voxels for higher accusification of rotation and velocity types. To exclude that possibilracy of rotation types in the sensorimotor cortex as well as in theity, we then performed “cross-classification” with different
SMA (Fig. 9, red). These areas closely correspond to the activatedcombinations of rotation and velocity types to explicitly dissociregions in the localizer scans that were conducted separately (Fig.
ate the movement kinematics with rotation types (Fig. 3B). If the2A). We also found the voxels for higher accuracy of velocity types
kinematics contributed to successful classification of rotation
mostly located in the early visual areas (Fig. 9, blue). These are largely
types, the accuracies for velocity types should exceed those of
consistent with the results of the previous ROI analysis.
rotation types in this analysis, because the differences in all the
behavioral measures were larger among the velocity types comClassification with behavioral measurespared with rotation types (Fig. 5). Significant decoding accuracy
To further validate that successful classification of rotation typesof rotation was found for Mot (59.2%, t(6) & 9.21, p % 0.001),
is not due to differences in low-level movement kinematics, weSom (62.1%, t(6) & 3.12, p % 0.05), SMA (57.6%, t(6) & 2.94, p %conducted multivariate classification analysis using the various0.05), and CBLa (57.5%, t(6) & 2.78, p % 0.05), while that ofbehavioral measures. In this analysis, all of the observed behavvelocity was found for Vis (71.9%, t(6) & 6.94, p % 0.001) (Fig.
ioral measures (see Materials and Methods, Behavioral perfor7B). Among these ROIs, Mot and Vis showed significant abovemance) were used separately or jointly as features (Fig. 10A), andchance accuracy after correction for multiple comparisons, andclassification of rotation types versus velocity types was conthe results of each individual subject (denoted by small dots inducted in the same way as the fMRI–MVPA (Fig. 3B). The time
Fig. 7B) show high consistency. We additionally conducted aseries of tracking error as well as the power spectrum of cursor
nonparametric randomization test and found that it generally
trajectory was also used as a feature vector for classification (Fig.
produced a more lenient threshold for p values compared with
10B). These analyses revealed significantly greater classification
the parametric t test (similar results were reported by Gallivan etaccuracy only between velocity types compared with rotations.
al., 2011b). This confirmed that all of the significant aboveWe further conducted the same classification analysis using theNormalized differencePower (a.u.)Error (pixel)

A 250

250

+90°
90°

fast
slow

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

6418 • J. Neurosci., April 10, 2013 • 33(15):6412– 6422

Discussion

B

Right

Rotation
Velocity

0.1

0

0.1

Mot

Som

SMA

SPL

Vis

CBLa

CBLp

non-brain

Figure 6. Mass-univariate fMRI results. A, The areas with a significant main effect of velocity types at p % 0.01 uncorrected for
multiple comparisons with an extent of 10 voxels. B, ROI analysis comparing averaged parameter estimates (! values) between
two conditions of rotation and velocity within each ROI. Error bars indicate SEMs. Abbreviations are as defined in Figure 2 legend.

A

C

B

*

Mot

*

* **

Som

Rotation*** **Velocity*** ** *

SMA

*

SPL

*

**

* **
**

CBLp

* ***

Significancelevels* ** *

non-brain

90

70

*

*

corrected

CBLa

**

50

90

p < 0.01
p < 0.05

Vis

p < 0.05

Additional experiment withnormal joystick
We additionally conducted the same
MVPA using different combinations ofrotation types and velocity types (Fig. 3B)
when subjects used a normal (not rotated)joystick to track a target in a control experiment. The averaged (SD) maximum
cross-correlations between the target andcursor trajectory of the x- and y-directions
were 0.78 (0.05) and 0.81 (0.04), respectively. This indicates that all of the subjects
accurately followed the target trajectory.
A significant decrease in classification accuracies for rotation types compared withthose of the main rotational experiment
was observed in Mot (paired t test; t(4) &5.38, p % 0.01), Som (t(4) & 4.11, p %0.05), and CBLa (t(4) & 3.30, p % 0.05),
and a marginal significance was found in
SMA (t(4) & 2.41, p & 0.07), with nonsignificant differences with the chance levelin the control experiment (all p ' 0.5)
(Fig. 11). In a post experimental debriefing, none of the subjects could discriminate whether a target motion was fromtheir cursor trajectory of the !90° or"90° condition in the previous rotational
experiment, while they could notice fast
versus slow target trajectories. This indicates that movement kinematics weremore similar between the two rotation
types than between the velocity types,
which is consistent with the results of behavioral analysis (Figs. 5, 10).

A LeftDifference inaveraged beta

time series as well as the power spectrumof the eye movements, which again
showed a higher accuracy for the velocity
types compared with the rotation types(Fig. 10C). These results indicate thathigher classification accuracy for rotation
over velocity in MVPA (Fig. 7B) cannot be
attributed to the differences in the movement or the sensory reafferents associatedwith rotational perturbations as well as ineye movements.

70
50
70
90
Rotation
Velocity

70
50
70
Rotation
VelocityThis study aimed to reveal neural subClassification accuracy (%)
Classificationlifi i accuracy (%) Classification accuracy (%)
strates that represent conflicting visuomotor mappings. After intensive training, Figure 7. MVPA results. A, Averaged classification accuracy of rotation types or velocity types with cross-validation among
the participants jointly adapted to the two different velocity or rotation conditions (Fig. 3A). B, Averaged classification accuracy of “cross-classification” with different comrotational perturbations, which is indi- binations of rotation and velocity types (Fig. 3B). C, The result of the same analysis as B except using the averaged activation withincated by the behavioral measures (Fig. 4). ROIs. Error bars and small dots indicate SEMs and individual result of subjects (n & 7), respectively. Small and large asterisks
The fMRI decoding showed that rota- indicate statistical significance of p values uncorrected and corrected for multiple comparisons using Holm–Bonferroni procedure.
tion types were successfully classified Abbreviations are as defined in the Figure 2 legend.
with multivariate activity patterns in thethe sensorimotor cortex represents different mappings, which
sensorimotor cortex when trials with different types of rotasubserves simultaneous adaptation of multiple visuomotor
tion and velocity were combined and cross-validated in thecorrespondences.
ROI analysis (Fig. 7B). Searchlight analysis also revealed a signifThis successful classification cannot be due to differences in
icant above-chance accuracy of rotation types in the sensorilow-level movement kinematics or sensory reafferents associatedmotor cortex and the SMA (Fig. 9). Our findings reveal that

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

A

J. Neurosci., April 10, 2013 • 33(15):6412– 6422 • 6419

SMA

Left

Mot

A

B

Error

Time series
x

Som

x

**

y

**

Correlation coefficientB+90° biased voxels
90° biased voxels

0.2
0.2
0.1
0.1

Distance

x

**

y

**

Frequency

y

0.2
0.2

C

**

Mot

Som

SMA

Error

CBLa

Figure 8. Voxel bias of classifiers. A, The voxel bias maps of “cross-classification” shown in
Figure 3B from a single typical subject, within the ROIs having significant accuracy of rotation
types. The yellow and pink voxels show positive weight for !90° and "90° rotation, respectively. B, Distributions of correlation coefficients ( R) of bias between different pairs of subjects.
Each dot denotes R of a single pair, where the total number of dots is 21 (combination of 2 of 7
subjects). The thick horizontal line and gray patch represent mean and SD, respectively. Abbreviations are as defined in the Figure 2 legend.

**
**

70
50
70
90
Rotation
Velocity
Classification accuracy (%)

Time seriesxCorrelation of x & y

0.1
0.1

x
y

Correlation of target & cursor
x
**

0
0

**
**

y

CS

CBLa

** p < 0.01

**

y

**
**

CursorFrequency
All
70
50
70
90
Rotation
Velocity
Classification accuracy (%)

**

x
y

**
**

70
50
70
90
Rotation
Velocity
Classification accuracy (%)

Figure 10. Decoding accuracy of “cross-classification” based on the behavioral measures (A)and the time series and power spectrum of the tracking errors (B) as well as the eye movements(C). Error bars indicate SEMs.

Figure 9. The classification accuracy map with the searchlight analysis. The red and blue
voxels show significant above-chance decoding accuracy of rotation and velocity types, respectively ( p % 0.01 corrected for multiple comparisons at the cluster level).

with the rotational perturbations for the following two reasons.
First, the differences in the various behavioral measures, including tracking error, movement distances, and correlation of target
and cursor motions, as well as power spectrum, are larger between the velocity type compared with the rotation type (Fig. 5).
The classification using these behavioral measures as well as eye
movements, conducted in the same way as the fMRI–MVPA,
further showed significantly higher accuracies of velocity types
over rotation types (Fig. 10). Second, the additional experiment,
in which subjects tracked their joystick trajectory with a normal
joystick, showed a significant decrease in accuracy for rotation
types in the same ROIs with a chance-level performance (Fig. 11).
These results indicate that successful decoding of rotation types is
not confounded with movement kinematics. In contrast, low-levelsensorimotor components, which were associated with different velocity types including tracking error and movement speed, were bestclassified using activities of the early visual cortex (Figs. 7, 9). We also
found a significantly higher than chance decoding accuracy of velocity types as well as rotation types in the sensorimotor cortex and the
cerebellum when classification of velocity types was cross-validated
between different rotation types (Fig. 7A). This indicates that lowlevel sensorimotor components were also represented in these areas,
together with the rotational types.
Previous studies with MVPA or fMRI adaptation (GrillSpector and Malach, 2001) revealed the directional selectivity ofhand movements in the primary motor cortex (Eisenberg et al.,2010, 2011; Fabbri et al., 2010; Ogawa and Inui, 2012). Eisenberg
et al. (2011) introduced a single rotational perturbation (45°)
between hand and cursor movements to dissociate movement
directions in the visual and motor coordinates. In contrast, ourcurrent study introduced the two conflicting rotational perturbations between hand and cursor movements (!90 or "90°),
requiring participants to move their hands in an opposite direction depending on the rotation types. We then decoded the rotational directions of the visuomotor perturbation that are
independent of the movement directions. Our study indicates,
for the first time, that multiple visuomotor mappings can be
decoded with distributed neural activity patterns of fMRI. Previous human neuroimaging studies revealed that separate brain
regions are involved in the learning of different types of perturbation (Imamizu et al., 2003; Krakauer et al., 2004; Girgenrath etal., 2008), which indicates that the size of these separate modules
are larger than fMRI individual-voxel resolution. In contrast, ourresults showed that parametric differences in the same type of
perturbation cannot be discriminated with the mass univoxel
analysis (Fig. 6) but could be revealed using MVPA. The voxel
bias map showed intermingled patterns of voxels with two rotational types idiosyncratic to each participant (Fig. 8). The previous studies showed that orientation columns in the early visualcortex could be discriminated using MVPA that is beyond fMRI
voxel resolution (Haynes and Rees, 2005; Kamitani and Tong,
2005). Our results suggest that parametric differences within thesame perturbation are represented more closely and intermixed
at a resolution below that of fMRI voxels, compared with different perturbations in separate areas that are larger than voxel size.
This discrepancy may correspond to the existence of behavioral
interference: joint learning of the different types of perturbations,such as kinematics versus dynamics, does not cause interference(Krakauer et al., 1999), while adaptation to different parameters
within the same type of perturbation, like that in the current

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

6420 • J. Neurosci., April 10, 2013 • 33(15):6412– 6422

Classification accuracy (%)

study, induces conflicting interference
80
80
80
80
Mot
Som
SMA
CBLa
(Wigmore et al., 2002). This interference
could be caused by the conflicting use of
70
70
70
70
the same neural representations, which
are more closely located for multiple para60
60
60
60
metric mapping within the same type
50
50
50
50
rather than different types of perturbations. Noted that the possible information
40
40
40
40
source of MVPA remains unclear withsome recent controversies (Freeman et al.,RotatedNormalRotatedNormalRotatedNormalRotatedNormal2011; Beckett et al., 2012). The decoding
accuracy of MVPA is considered to be de- Figure 11. Classification accuracy of additional experiment using normal (not rotated) joystick, within ROIs of significant
pendent on the spatial patterns of distinct accuracy of rotation types. Abbreviations are as defined in the Figure 2 legend. Each dot represents the result of an individual
neuronal populations and/or accompany- participant (n & 5).
ing vasculature units together with thespatial resolution of fMRI (Bartels et al.,internal models (Diedrichsen et al., 2005). These findings indicate
2008; Gardner, 2010). Our results are also in line with recent
the role of the sensorimotor cortex in representing motor skills, andpapers showing the effectiveness of MVPA in motor systems by
they are generally consistent with the view that the primary motor
successfully decoding multiple actions (Dinstein et al., 2008; Galcortex is not a low-level static controller of muscles but rather conlivan et al., 2011a,b), representations of individual fingers (Wiestains higher level representation of various movement parameterstler et al., 2011; Diedrichsen et al., 2013), or movement directions(for review, see Scott, 2003). Our results support this view, indicating(Eisenberg et al., 2010, Ogawa and Inui, 2011, 2012), which arethat distinct visuomotor mappings are stored in the sensorimotor
not possible with the conventional univoxel analysis.
cortex.
There are two possibilities for the neural mechanisms underHuman neuroimaging has shown activities related to the aclying the joint adaptation of the multiple parameters within thequisition of an internal model in the cerebellum (Imamizu et al.,same types of perturbation. One is that a single component that
2000, 2003; Seidler and Noll, 2008). Imamizu et al. (2003)
represents the visuomotor correspondence continuously adapts
showed that the learning of different types of sensorimotor perand readapts to different mappings depending on the current
turbations (rotation and velocity) is subserved with the acquisienvironment, which regards switching as recalibration between
tion of multiple internal models in the cerebellum. Our study
the visual and motor frames of reference. The other is that mulrevealed significant above-chance accuracy in the anterior ceretiple mappings have distinct substrates, each of which represents
bellum in the ROI analysis, which supports the existence of mula specific visuomotor correspondence (Lee and Schweighofer,
tiple internal models in the cerebellum. However, since the2009). In our current study, it remains unclear whether the motor
accuracy in the cerebellum did not survive the threshold corsystem serially recalibrates a single general model or acquires
rected for the multiple comparisons in the both the ROI and themultiple models specific to each mapping, since both possibilities
searchlight analysis, we do not consider the cerebellum result to
induce specific distributed activation patterns of multiple voxels
be robust and discuss it no further.
for decoding. Further studies are needed to clarify whether thePrevious studies indicated a role of the posterior parietal cordistinct mappings are represented with a single or multiple models.
tex (PPC) in visuomotor rotational learning (Inoue et al., 2000;
Previous research identified the sensorimotor cortex and cerGraydon et al., 2005). In contrast, we did not observe significant
ebellum as neural substrates of visuomotor learning (for review,classification accuracy in the parietal cortex. While the previous
see Shmuelof and Krakauer, 2011). Neurophysiological studies
research reported changes of activity in the midst of learning, ourwith nonhuman primates revealed changes in neuronal response
studies focused on activations when subjects sufficiently adaptedreflecting learning of rotational perturbations in the primary moto rotations after intensive practice. Our results indicate that the
tor cortex (M1) (Wise et al., 1998; Paz et al., 2003; Paz and Vaadia,
parietal cortex might be related to the early middle phases of a2004) as well as in the SMA (Padoa-Schioppa et al., 2004; Paz etlearning process, rather than long-term storing of motor memoal., 2005). A recent study using unit recording of nonhumanries. A recent computational model-based study also predictedprimates suggested that two opposing rotations cannot be reprethat the site for learning is not in the PPC but in the connectivity
sented with a single neuron in M1 (Zach et al., 2012). Their
between the PPC and the motor areas (Tanaka et al., 2009).
results support the existence of separate representations for difThis study investigated opposite rotational perturbations thatferent mapping, consistent with our current findings. Together
causeinterference in joint learning. However, it is known thatwith these previous unit recordings of nonhuman primates, ourpositive transfer of learning occurs when the rotational degrees ofresults indicate the existence of different neuronal populations in
perturbations are close to each other (e.g., 30° or 45°) (Seidler andthe motor-related region.
Noll, 2008). Future studies are needed to examine whether disHuman neuroimaging studies also indicated increased activatinct visuomotor mappings exist in such cases, or if multivoxel
tions (Karni et al., 1995) as well as structural changes in M1
pattern analysis can identify the mappings for close perturbarelated to motor learning (Draganski et al., 2004; Landi et al.,tions, if any. Extension of our studies to joint learning between2011). The recent studies using transcranial magnetic stimulation
various degrees of visuomotor rotations would give us importantor transcranial direct current stimulation indicate the acquisition
clues to understanding the modular units used for sensorimotor(Hunter et al., 2009) as well as storage (Hadipour-Niktarash etlearning as well as the neural mechanisms used for positive andal., 2007; Galea et al., 2011) of sensorimotor skills in the human M1.
negative (interference) transfer of learning.
The error-related increase in fMRI signal was also reported in the
In summary, our findings indicate that the sensorimotor corhuman sensorimotor areas when subjects learned visuomotor pertex represents conflicting visuomotor mappings, which subserves
turbations, which could be used as training signals for acquisition of

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping

simultaneous adaptation of multiple sensorimotor mappings.
We hypothesize that similar neural mechanisms underlie joint
adaptation of opposing force fields. In future applications, decoding of a specific parameter in sensorimotor transformation
could be used as a neurofeedback signal to achieve efficient structural learning with reduced interference (Braun et al., 2009), using a method recently applied to perceptual learning (Shibata etal., 2011).

References

Bartels A, Logothetis NK, Moutoussis K (2008) fMRI and its interpretations: an illustration on directional selectivity in area V5/MT. Trends
Neurosci 31:444 – 453. CrossRef Medline
Beckett A, Peirce JW, Sanchez-Panchuelo RM, Francis S, Schluppeck D(2012) Contribution of large scale biases in decoding of direction-ofmotion from high-resolution fMRI data in human early visual cortex.
Neuroimage 63:1623–1632. CrossRef Medline
Brainard DH (1997) The Psychophysics Toolbox. Spat Vis 10:433– 436.
CrossRef Medline
Braun DA, Aertsen A, Wolpert DM, Mehring C (2009) Motor task variationinduces structural learning. Curr Biol 19:352–357. CrossRef Medline
Choi Y, Qi F, Gordon J, Schweighofer N (2008) Performance-based adaptive schedules enhance motor learning. J Mot Behav 40:273–280.
CrossRef Medline
Corradi-Dell’Acqua C, Hofstetter C, Vuilleumier P (2011) Felt and seen
pain evoke the same local patterns of cortical activity in insular and cingulate cortex. J Neurosci 31:17996 –18006. CrossRef Medline
Cunningham HA, Welch RB (1994) Multiple concurrent visual-motor
mappings: implications for models of adaptation. J Exp Psychol Hum
Percept Perform 20:987–999. CrossRef Medline
Diedrichsen J, Hashambhoy Y, Rane T, Shadmehr R (2005) Neural correlates of reach errors. J Neurosci 25:9919 –9931. CrossRef Medline
Diedrichsen J, Wiestler T, Krakauer JW (2013) Two distinct ipsilateral cortical representations for individuated finger movements. Cereb Cortex, inpress.
Dinstein I, Gardner JL, Jazayeri M, Heeger DJ (2008) Executed and observed movements have different distributed representations in humanaIPS. J Neurosci 28:11231–11239. CrossRef Medline
Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A (2004)
Neuroplasticity: changes in grey matter induced by training. Nature 427:
311–312. CrossRef Medline
Eisenberg M, Shmuelof L, Vaadia E, Zohary E (2010) Functional organization of human motor cortex: directional selectivity for movement. J Neurosci 30:8897– 8905. CrossRef Medline
Eisenberg M, Shmuelof L, Vaadia E, Zohary E (2011) The representation ofvisual and motor aspects of reaching movements in the human motor
cortex. J Neurosci 31:12377–12384. CrossRef Medline
Etzel JA, Gazzola V, Keysers C (2008) Testing simulation theory with crossmodal multivariate classification of fMRI data. PLoS ONE 3:e3690.
CrossRef Medline
Fabbri S, Caramazza A, Lingnau A (2010) Tuning curves for movement
direction in the human visuomotor system. J Neurosci 30:13488 –13498.
CrossRef Medline
Foulkes AJ, Miall RC (2000) Adaptation to visual feedback delays in a human manual tracking task. Exp Brain Res 131:101–110. CrossRef Medline
Freeman J, Brouwer GJ, Heeger DJ, Merriam EP (2011) Orientation decoding depends on maps, not columns. J Neurosci 31:4792– 4804. CrossRef
MedlineGalea JM, Vazquez A, Pasricha N, de Xivry JJ, Celnik P (2011) Dissociating
the roles of the cerebellum and motor cortex during adaptive learning: themotor cortex retains what the cerebellum learns. Cereb Cortex 21:1761–
1770. CrossRef Medline
Gallivan JP, McLean DA, Smith FW, Culham JC (2011a) Decoding effectordependent and effector-independent movement intentions from humanparieto-frontal brain activity. J Neurosci 31:17149 –17168. CrossRef
MedlineGallivan JP, McLean DA, Valyear KF, Pettypiece CE, Culham JC (2011b)
Decoding action intentions from preparatory brain activity in humanparieto-frontal networks. J Neurosci 31:9599 –9610. CrossRef Medline
Gardner JL (2010) Is cortical vasculature functionally organized? Neuroimage 49:1953–1956. CrossRef Medline

J. Neurosci., April 10, 2013 • 33(15):6412– 6422 • 6421
Ghahramani Z, Wolpert DM (1997) Modular decomposition in visuomotor learning. Nature 386:392–395. CrossRef Medline
Girgenrath M, Bock O, Seitz RJ (2008) An fMRI study of brain activation ina visual adaptation task: activation limited to sensory guidance. Exp Brain
Res 184:561–569. CrossRef Medline
Graydon FX, Friston KJ, Thomas CG, Brooks VB, Menon RS (2005)
Learning-related fMRI activation associated with a rotational visuomotor transformation. Brain Res Cogn Brain Res 22:373–383. CrossRef
MedlineGrefkes C, Ritzl A, Zilles K, Fink GR (2004) Human medial intraparietal
cortex subserves visuomotor coordinate transformation. Neuroimage 23:1494 –1506. CrossRef Medline
Grill-Spector K, Malach R (2001) fMR-adaptation: a tool for studying
the functional properties of human cortical neurons. Acta Psychol
107:293–321. CrossRef Medline
Hadipour-Niktarash A, Lee CK, Desmond JE, Shadmehr R (2007) Impairment of retention but not acquisition of a visuomotor skill
through time-dependent disruption of primary motor cortex. J Neurosci 27:13413–13419. CrossRef Medline
Haynes JD, Rees G (2005) Predicting the orientation of invisible stimuli
from activity in human primary visual cortex. Nat Neurosci 8:686 – 691.
CrossRef Medline
Holm S (1979) A simple sequentially rejective multiple test procedure.
Scand J Stat 6:65–70.
Howard IS, Ingram JN, Wolpert DM (2008) Composition and decomposition in bimanual dynamic learning. J Neurosci 28:10531–10540. CrossRef
MedlineHunter T, Sacco P, Nitsche MA, Turner DL (2009) Modulation of internal
model formation during force field-induced motor learning by anodal
transcranial direct current stimulation of primary motor cortex. J Physiol
587:2949 –2961. CrossRef Medline
Imamizu H, Miyauchi S, Tamada T, Sasaki Y, Takino R, Pütz B, Yoshioka T,
Kawato M (2000) Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403:192–195. CrossRef Medline
Imamizu H, Kuroda T, Miyauchi S, Yoshioka T, Kawato M (2003) Modularorganization of internal models of tools in the human cerebellum. Proc
Natl Acad Sci U S A 100:5461–5466. CrossRef Medline
Imamizu H, Kuroda T, Yoshioka T, Kawato M (2004) Functional magnetic
resonance imaging examination of two modular architectures for switching multiple internal models. J Neurosci 24:1173–1181. CrossRef Medline
Imamizu H, Higuchi S, Toda A, Kawato M (2007a) Reorganization of brain
activity for multiple internal models after short but intensive training.
Cortex 43:338 –349. CrossRef Medline
Imamizu H, Sugimoto N, Osu R, Tsutsui K, Sugiyama K, Wada Y, Kawato M(2007b) Explicit contextual information selectively contributes to predictive switching of internal models. Exp Brain Res 181:395– 408.
CrossRef Medline
Inoue K, Kawashima R, Satoh K, Kinomura S, Sugiura M, Goto R, Ito M,
Fukuda H (2000) A PET study of visuomotor learning under opticalrotation. Neuroimage 11:505–516. CrossRef Medline
Kahnt T, Heinzle J, Park SQ, Haynes JD (2011) Decoding the formation of
reward predictions across learning. J Neurosci 31:14624 –14630. CrossRef
MedlineKamitani Y, Tong F (2005) Decoding the visual and subjective contents of
the human brain. Nat Neurosci 8:679 – 685. CrossRef Medline
Karni A, Meyer G, Jezzard P, Adams MM, Turner R, Ungerleider LG (1995)
Functional MRI evidence for adult motor cortex plasticity during motor
skill learning. Nature 377:155–158. CrossRef Medline
Knops A, Thirion B, Hubbard EM, Michel V, Dehaene S (2009) Recruitment of an area involved in eye movements during mental arithmetic.
Science 324:1583–1585. CrossRef Medline
Krakauer JW, Ghilardi MF, Ghez C (1999) Independent learning of internal
models for kinematic and dynamic control of reaching. Nat Neurosci
2:1026 –1031. CrossRef Medline
Krakauer JW, Ghilardi MF, Mentis M, Barnes A, Veytsman M, Eidelberg D,
Ghez C (2004) Differential cortical and subcortical activations in learning rotations and gains for reaching: a PET study. J Neurophysiol 91:924 –
933. Medline
Kriegeskorte N, Goebel R, Bandettini P (2006) Information-based functional brain mapping. Proc Natl Acad Sci U S A 103:3863–3868. CrossRef
MedlineLandi SM, Baguear F, Della-Maggiore V (2011) One week of motor adapta-

6422 • J. Neurosci., April 10, 2013 • 33(15):6412– 6422
tion induces structural changes in primary motor cortex that predict
long-term memory one year later. J Neurosci 31:11808 –11813. CrossRef
MedlineLee JY, Schweighofer N (2009) Dual adaptation supports a parallel architecture of motor memory. J Neurosci 29:10396 –10404. CrossRef Medline
Meyer K, Kaplan JT, Essex R, Webber C, Damasio H, Damasio A (2010)
Predicting visual stimuli on the basis of activity in auditory cortices. Nat
Neurosci 13:667– 668. CrossRef Medline
Miall RC (1996) Task-dependent changes in visual feedback control: a frequency analysis of human manual tracking. J Mot Behav 28:125–135.
CrossRef Medline
Mur M, Bandettini PA, Kriegeskorte N (2009) Revealing representational
content with pattern-information fMRI- an introductory guide. Soc
Cogn Affect Neurosci 4:101–109. Medline
Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25.
CrossRef Medline
Norman KA, Polyn SM, Detre GJ, Haxby JV (2006) Beyond mind-reading:multi-voxel pattern analysis of fMRI data. Trends Cogn Sci 10:424 – 430.
CrossRef Medline
Nozaki D, Kurtzer I, Scott SH (2006) Limited transfer of learning between
unimanual and bimanual skills within the same limb. Nat Neurosci
9:1364 –1366. CrossRef Medline
Ogawa K, Inui T (2011) Neural representation of observed actions in theparietal and premotor cortex. Neuroimage 56:728 –735. CrossRef
Medline
Ogawa K, Inui T (2012) Reference frame of human medial intraparietal
cortex in visually guided movements. J Cogn Neurosci 24:171–182.
CrossRef Medline
Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113. CrossRef Medline
Oosterhof NN, Wiggett AJ, Diedrichsen J, Tipper SP, Downing PE (2010)
Surface-based information mapping reveals crossmodal vision-action
representations in human parietal and occipitotemporal cortex. J Neurophysiol 104:1077–1089. CrossRef Medline
Osu R, Hirai S, Yoshioka T, Kawato M (2004) Random presentation enables
subjects to adapt to two opposing forces on the hand. Nat Neurosci 7:111–112. CrossRef Medline
Padoa-Schioppa C, Li CS, Bizzi E (2004) Neuronal activity in the supplementary motor area of monkeys adapting to a new dynamic environment.
J Neurophysiol 91:449 – 473. Medline
Paz R, Vaadia E (2004) Specificity of sensorimotor learning and the neural
code: neuronal representations in the primary motor cortex. J Physiol
Paris 98:331–348. CrossRef Medline
Paz R, Boraud T, Natan C, Bergman H, Vaadia E (2003) Preparatory activity
in motor cortex reflects learning of local visuomotor skills. Nat Neurosci
6:882– 890. CrossRef Medline
Paz R, Natan C, Boraud T, Bergman H, Vaadia E (2005) Emerging patternsof neuronal responses in supplementary and primary motor areas during
sensorimotor adaptation. J Neurosci 25:10941–10951. CrossRef Medline

Ogawa and Imamizu • Neural Substrate of Conflicting Visuomotor Mapping
Pereira F, Mitchell T, Botvinick M (2009) Machine learning classifiers andfMRI: a tutorial overview. Neuroimage 45:S199 –209. CrossRef Medline
Prado J, Clavagnier S, Otzenberger H, Scheiber C, Kennedy H, Perenin MT(2005) Two cortical systems for reaching in central and peripheral vision.
Neuron 48:849 – 858. CrossRef Medline
Rabe K, Livne O, Gizewski ER, Aurich V, Beck A, Timmann D, Donchin O(2009) Adaptation to visuomotor rotation and force field perturbation iscorrelated to different brain areas in patients with cerebellar degeneration. J Neurophysiol 101:1961–1971. CrossRef Medline
Scott SH (2003) The role of primary motor cortex in goal-directed movements: insights from neurophysiological studies on non-human primates. Curr Opin Neurobiol 13:671– 677. CrossRef Medline
Seidler RD, Noll DC (2008) Neuroanatomical correlates of motor acquisition and motor transfer. J Neurophysiol 99:1836 –1845. CrossRef Medline
Shibata K, Watanabe T, Sasaki Y, Kawato M (2011) Perceptual learning
incepted by decoded fMRI neurofeedback without stimulus presentation.
Science 334:1413–1415. CrossRef Medline
Shmuelof L, Krakauer JW (2011) Are we ready for a natural history of motor
learning? Neuron 72:469 – 476. CrossRef Medline
Stokes M, Thompson R, Cusack R, Duncan J (2009) Top-down activation ofshape-specific population codes in visual cortex during mental imagery.
J Neurosci 29:1565–1572. CrossRef Medline
Tanaka H, Sejnowski TJ, Krakauer JW (2009) Adaptation to visuomotor
rotation through interaction between posterior parietal and motor cortical areas. J Neurophysiol 102:2921–2932. CrossRef Medline
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling ofactivations in SPM using a macroscopic anatomical parcellation of the
MNI MRI single-subject brain. Neuroimage 15:273–289. CrossRef
MedlineWada Y, Kawabata Y, Kotosaka S, Yamamoto K, Kitazawa S, Kawato M(2003) Acquisition and contextual switching of multiple internal models
for different viscous force fields. Neurosci Res 46:319 –331. CrossRef
MedlineWiestler T, McGonigle DJ, Diedrichsen J (2011) Integration of sensory andmotor representations of single fingers in the human cerebellum. J Neurophysiol 105:3042–3053. CrossRef Medline
Wigmore V, Tong C, Flanagan JR (2002) Visuomotor rotations of varying
size and direction compete for a single internal model in motor working
memory. J Exp Psychol Hum Percept Perform 28:447– 457. CrossRef
MedlineWise SP, Moody SL, Blomstrom KJ, Mitz AR (1998) Changes in motor cortical activity during visuomotor adaptation. Exp Brain Res 121:285–299.
CrossRef Medline
Wolpert DM, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11:1317–1329. CrossRef Medline
Zach N, Inbar D, Grinvald Y, Vaadia E (2012) Single neurons in m1 andpremotor cortex directly reflect behavioral interference. PLoS ONE
7:e32986. CrossRef Medline