Download Learning of Sequences of Finger Movements and Timing: Frontal

J Neurophysiol
88: 2035–2046, 2002; 10.1152/jn.00116.2002.
Learning of Sequences of Finger Movements and Timing: Frontal
Lobe and Action-Oriented Representation
KATSUYUKI SAKAI,1 NARENDER RAMNANI,1,2 AND RICHARD E. PASSINGHAM1,3
Wellcome Department of Imaging Neuroscience, Institute of Neurology, London WC1N 3BG; 2Center for Functional
Magnetic Resonance Imaging of the Brain and 3Department of Experimental Psychology, University of Oxford,
Oxford OX1 3UD, United Kingdom
1
Received 19 February 2002; accepted in final form 3 June 2002
INTRODUCTION
Address for reprint requests: K. Sakai, Wellcome Department of Imaging
Neuroscience, Institute of Neurology, 12 Queen Square, London WC1N 3BG,
UK (E-mail: [email protected]).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked ‘‘advertisement’’
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
www.jn.org
Accurate and quick performance of motor action requires
both effector information (which effector to use to perform the
action) and temporal information (at which timing to perform
the action). Thus in motor sequence learning, one needs to
learn both a sequence of effectors and a sequence of timings.
To date, studies on neural mechanisms of motor sequence
learning have been mostly focused on the learning of an
effector sequence; subjects learned a sequence of finger movements while the timing of the movements were paced at a
constant rate (Doyon et al. 2002; Grafton et al. 1995; Hazeltine
et al. 1997; Honda et al. 1998; Jenkins et al. 1994; Jueptner et
al. 1997; Sakai et al. 1998a; Toni and Passingham 1999; Toni
et al. 1998). Only a few studies have examined the mechanisms
to learn a sequence of timing or a rhythm (Ramnani and
Passingham 2001; Sakai et al. 2000; Schubotz and von Cramon
2001). Because of the variable behavioral settings used to
study learning of effector sequences and of timing sequences,
it is hard to determine whether the two learning mechanisms
are subserved by separate neural systems or not.
In the first experiment reported here, we investigated the
learning mechanisms of a sequence of finger movements and of
a sequence of timing in the same experimental setting. An
important difference from previous studies (Doyon et al. 2002;
Grafton et al. 1995; Hazeltine et al. 1997; Honda et al. 1998;
Jenkins et al. 1994; Jueptner et al. 1997; Ramnani and Passingham 2001; Sakai et al. 1998a; Toni and Passingham 1999;
Toni et al. 1998) is that we used random timing when testing
learning of a finger sequence and used random finger movements when testing learning of a timing sequence. Thus in this
study, even when subjects learned a finger sequence, they did
not know at which timing to execute the finger movements.
When subjects learned a timing sequence, they did not know
with which finger to execute the movements. In other words,
the subjects were unable to prepare a specific motor action that
was defined by specific effector and specific timing. The coding of a motor sequence remained at an abstract level. In
contrast, previous studies used constant timing for learning of
a finger sequence (Doyon et al. 2002; Grafton et al. 1995;
Hazeltine et al. 1997; Honda et al. 1998; Jenkins et al. 1994;
Jueptner et al. 1997; Sakai et al. 1998a; Toni and Passingham
0022-3077/02 $5.00 Copyright © 2002 The American Physiological Society
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Sakai, Katsuyuki, Narender Ramnani, and Richard E. Passingham. Learning of sequences of finger movements and timing: frontal
lobe and action-oriented representation. J Neurophysiol 88: 2035–2046,
2002; 10.1152/jn.00116.2002. Motor sequence learning involves learning of a sequence of effectors with which to execute a series of
movements and learning of a sequence of timings at which to execute
the movements. In this study, we have segregated the neural correlates
of the two learning mechanisms. Moreover, we have found an interaction between the two learning mechanisms in the frontal areas,
which we claim as suggesting action-oriented coding in the frontal
lobe. We used positron emission tomography and compared three
learning conditions with a visuo-motor control condition. In two
learning conditions, the subjects learned either a sequence of finger
movements with random timing or a sequence of timing with random
use of fingers. In the third condition the subjects learned to execute a
sequence of specific finger movements at specific timing; we argue
that it was only in this condition that the motor sequence was coded
as an action-oriented representation. By looking for condition by
session interactions (learning vs. control conditions over sessions), we
have removed nonspecific time effects and identified areas that
showed a learning-related increment of activation during learning.
Learning of a finger sequence was associated with an increment of
activation in the right intraparietal sulcus region and medial parietal
cortex, whereas learning of a timing sequence was associated with an
increment of activation in the lateral cerebellum, suggesting separate
mechanisms for learning effector and temporal sequences. The left
intraparietal sulcus region showed an increment of activation in learning of both finger and timing sequences, suggesting an overlap between the two learning mechanisms. We also found that the middorsolateral prefrontal cortex, together with the medial and lateral
premotor areas, became increasingly active when subjects learned a
sequence that specified both fingers and timing, that is, when subjects
were able to prepare specific motor action. These areas were not active
when subjects learned a sequence that specified fingers or timing
alone, that is, when subjects were still dependent on external stimuli
as to the timing or fingers with which to execute the movements.
Frontal areas may integrate the effector and temporal information of
a motor sequence and implement an action-oriented representation so
as to perform a motor sequence accurately and quickly. We also found
that the mid-dorsolateral prefrontal cortex was distinguished from the
ventrolateral prefrontal cortex and anterior fronto-polar cortex, which
showed sustained activity throughout learning sessions and did not
show either an increment or decrement of activation.
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K. SAKAI, N. RAMNANI, AND R. E. PASSINGHAM
METHODS
Subjects
Twenty normal right-handed male subjects (ages 20 –33 yr) participated in the imaging experiment (12 in experiment1 and 8 in experiment2). Another eight subjects (ages 19 –25 yr) participated in behavioral pilot experiments. For all subjects, written informed consent
was obtained prior to the study. The experimental procedure was
approved by the ethics committee of the National Hospital for Neurology and Neurosurgery and the Institute of Neurology, London, and
the Administration of Radiation Safety Advisory Committee, United
Kingdom.
Behavioral task
The subjects performed sequential finger movements in response to
visual stimuli. A stimulus was an array of two horizontal bars and an
asterisk and was presented on a screen for 80 ms (Fig. 1A). Eight
arrays of stimuli, with an asterisk appearing at different positions,
were presented in a rhythmic sequence. The intervals of the stimulus
J Neurophysiol • VOL
FIG. 1. A: behavioral task. Subjects saw an array of 2 horizontal bars and
1 asterisk on the screen and responded by pressing a button using the right
index, middle, or ring finger to the asterisk shown on the left, middle, or right,
respectively. Eight arrays of stimuli were presented in a rhythmic sequence,
with the intervals of the stimulus onsets of 625, 1,250, or 2,500 ms. Sequence
of stimuli was repeated for 12 or 18 trials in 1 session. By setting the positions
of the asterisks in the 8 stimuli fixed or variable across trials, we created a
situation where subjects learned or did not learn a finger sequence. Also by
setting the timing of presentation of the 8 stimuli fixed or variable across trials,
we created a situation where subjects learned or did not learn a timing
sequence. B: task conditions. Four conditions (RANDOM, FINGER, TIMING,
and COMBINED) were created in a 2 ⫻ 2 factorial design, where learning
occurred or did not occur in effector domain (finger sequence) or temporal
domain (timing sequence). In experiment1, FINGER, TIMING, and RANDOM were tested for 4 sessions each. In experiment2, COMBINED and
RANDOM were tested for 6 sessions each.
onsets consisted of four 625-, two 1,250-, and one 2,500-ms intervals.
Subjects responded to each stimulus by pressing buttons using the
index, middle, or ring finger of the right hand when an asterisk
appeared on the left, center, or right, respectively (Fig. 1A). We asked
the subjects to respond to the visual stimuli as accurately and quickly
as possible. An eight-stimulus sequence comprised a trial, and the
sequences were given for 18 (experiment1) or 12 trials (experiment2)
in one session. A visual cue, “start,” indicated the start of each trial of
a sequence, and the trials were separated from each other by 2.5 s.
We created four conditions (RANDOM, FINGER, TIMING, and
COMBINED), where learning occurred or did not occur in terms of
the order of finger movements or in terms of the timing of finger
movements (Fig. 1B). In the RANDOM condition, the order of the
position of an asterisk and the timing of the presentation of the eight
stimuli in a sequence were varied across trials. Thus the finger to be
used and the timing to press buttons remained unpredictable, and no
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1999; Toni et al. 1998) and a fixed finger movements for
learning of a timing sequence (Ramnani and Passingham
2001). Therefore subjects became able to prepare specific
finger sequence at specific timing in both types of learning. The
results could have been confounded by preparation of a motor
sequence; it is hard to determine whether the observed brain
activation is associated with acquisition of sequence information or an increased level of motor preparation. In fact, a
similar set of brain areas including prefrontal and premotor
areas has been shown to be active in learning of finger sequences (Doyon et al. 2002; Grafton et al. 1995; Hazeltine et
al. 1997; Honda et al. 1998; Jenkins et al. 1994; Jueptner et al.
1997; Sakai et al. 1998a; Toni and Passingham 1999; Toni et
al. 1998) and in learning of timing sequences (Ramnani and
Passingham 2001; Sakai et al. 2000; Schubotz and von Cramon
2001). Our first experiment is free from the confound of motor
preparation.
In the second experiment reported here, we tested a third
learning condition where subjects learned a sequence of specific finger movements at specific timing. In this condition, the
subjects were able to prepare a sequence of specific finger
movements at specific timing. Here the motor sequence could
be coded at an action-specific level as opposed to an abstract
level in the other two learning conditions mentioned above.
The three learning conditions were compared with a control
condition where subjects made random finger movements at
random timing according to visual cues. Thus we tested four
conditions where subjects learned or did not learn a sequence
of effectors or a sequence of timing. This design not only
allowed us to investigate differences in the learning mechanisms between sequences of effectors and sequences of timing,
but also allowed us to assess the interaction term, that is,
differences between the action and abstract levels of sequence
coding.
We used positron emission tomography to measure learningrelated changes in brain activation. By looking for the change
of brain activation relative to the control condition, the nonspecific time effects could be removed. We discriminated areas
that showed a learning-related increment of activation, a
learning-related decrement of activation, and sustained activation during learning.
ACTION REPRESENTATION IN FRONTAL LOBE
J Neurophysiol • VOL
Experiment 1
Twelve subjects were scanned while they performed one of three
alternating conditions (RANDOM, FINGER, and TIMING), for four
sessions each. The order of the three conditions was counter-balanced
across subjects. Each session started with a visual cue indicating the
name of the condition and lasted for 180 s (18 trials of a sequence).
Before the scans, subjects practiced each of the three conditions for 3
min using sequences different from the ones used during the scans.
After the scans (12 sessions), the subjects performed each of the three
conditions for one more session without scans, using the same sequence as used during the scans. The subjects were also asked to
reproduce the learned sequence without visual cues. Subjects reproduced the order of finger movements of the learned FINGER sequence
at an arbitrary timing for 10 trials. Accuracy of the reproduced order
was assessed by comparing it with the order of the original sequence
and calculating the percentage of the correctly performed movements.
Subjects also reproduced the timing of button presses of the learned
TIMING sequence with the right index finger for 10 trials. The
accuracy of the reproduced timing was assessed by comparing the
intervals of the eight moves of the reproduced TIMING sequence with
the inter-stimulus intervals of the original TIMING sequence. We
performed a one-sample t-test over the difference between the reproduced intervals and the original intervals. The null hypothesis is that
there is no difference between them.
Experiment 2
Eight subjects were scanned while they performed one of two
alternating conditions (RANDOM and COMBINED), for six sessions
each. The order of the two conditions was counter-balanced across
subjects. Each condition lasted for 120 s (12 trials of a sequence).
Thus the total number of trials to learn a sequence (12 trials ⫻ 6
sessions) was equated to that in experiment1 (18 trials ⫻ 4 sessions).
The number of scans given for each condition (4 scans ⫻ 12 subjects)
was also matched with those in experiment1 (6 scans ⫻ 8 subjects),
thus enabling us to compare the results across the two experiments.
The subjects had prescan training and postscan sessions in the same
way as in experiment1.
Image data acquisition
The subjects lay supine in the scanner. Head movements were
minimized by an adjustable helmet. PET images covering the whole
brain were collected using an ECAT Exact HR ⫹ PET scanner (CTI
Siemens, Knoxville, TN) in a three-dimensional (3D) mode with the
inter-detector collimating septa removed. Relative regional cerebral
blood flow (rCBF) was measured by recording the regional distribution of cerebral radioactivity using H215O as tracer.
For each session, 9 mCi H215O in 3 ml saline solution was injected
intravenously over 20 s at a rate of 10 ml/min through a cannula
placed in the left cubital vein. Subjects started the behavioral task 30 s
before each tracer administration. Each PET scan began with a 30-s
background period acquired before the delivery of the tracer. Thereafter, emission data were acquired in one 90-s frame, beginning 5 s
before the rising phase of the count curve. The interval between
successive administrations of the radioactive tracer was 8 min. Correction for radiation attenuation was made by means of a transmission
scan collected for the first emission scan. The corrected data were then
reconstructed by 3D filtered back projection (Hanning filter, cutoff
frequency of 0.5 cycles/pixel) and scatter correction. Sixty-three
transverse planes (2.4-mm thickness) were obtained, each with a
128 ⫻ 128-pixel image matrix (size 2.1 mm), giving a resolution of 6
mm at full width half-maximum.
We also collected structural magnetic resonance images (MRIs) of
all the subjects using a 2-tesla magnetic resonance scanner (Vision,
Siemens, Erlagen, Germany) with a T1 MPRAGE sequence (repeti-
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learning occurred. In the FINGER condition, the order of the position
of the asterisks for the eight-stimulus sequence was fixed across trials,
whereas the timing of stimulus presentation was varied. Thus subjects
repeated the same sequence of finger movements with different timing
for each trial. Learning occurred only in terms of the order of finger
movements. In the TIMING condition, the timing of the presentation
of the eight stimuli was fixed across trials, whereas the order of the
positions of asterisks was varied between trials. Thus subjects repeated the same sequence of timing using different fingers for each
trial. Learning occurred only in terms of the timing of movements. In
the COMBINED condition, the position of the asterisks and the timing
of the presentation of the eight stimuli were fixed across trials.
Subjects repeated the same sequence of finger movements at the same
timing. Learning occurred both in terms of the order of finger movements and timing of movements. Thus in the COMBINED condition,
the subjects were able to execute specific finger movements at specific
timing—i.e., to code a sequence at an action level—whereas in the
FINGER and TIMING conditions, the subjects were not able to
specify an action because either the timing or finger remained unpredictable—i.e., to code a sequence at an abstract level.
In the three learning conditions (FINGER, TIMING, and COMBINED), we gave explicit instructions to the subjects to learn a finger
sequence and/or timing sequence and to make the best use of the
knowledge to press buttons as quickly as possible. Thus the learning
was explicit in all the three conditions, although implicit learning may
have occurred in parallel.
Our aim was to investigate the difference in brain activation between the three learning conditions (FINGER, TIMING, and COMBINED) and the control condition (RANDOM). However, in a behavioral pilot experiment, we found that learning a FINGER sequence
hindered learning a COMBINED sequence when the two learning
sessions were tested 3 min apart, but this interference was not found
if the two conditions were tested on different days. Comparing the
within-day and between-day presentations, the three subjects tested
showed an increase in the number of wrong button presses (⫹7%) and
an increase in the reaction times (⫹44 ms) for the within-day presentation. We could not, therefore test the FINGER and COMBINED
conditions in the same positron emission tomography (PET) experiment. For the same reason, we could not test the TIMING and
COMBINED conditions in the same PET experiment.
By contrast, learning of a FINGER sequence did not have adverse
effects on learning of a TIMING sequence and vice versa. In another
behavioral pilot experiment, we required five subjects to learn a
FINGER sequence, and 3 min later, learn a TIMING sequence. On
different days, we also required the same subjects to learn another
FINGER sequence, and on the next day, learn another TIMING
sequence. Comparing the within-day and between-day presentations,
there was no significant difference in terms of accuracy and quickness
of the performance: increase in the number of wrong button presses
and in the reaction times was ⫺1.2% and ⫹5 ms, respectively, for the
within-day presentation compared with between-day presentation.
There was no significant difference in the performance when the same
subjects were tested on a TIMING sequence first and then on a
FINGER sequence: increase in the number of wrong button presses
and in the reaction times was ⫹1% and ⫺10 ms, respectively.
Based on these behavioral pilot experiments, we concluded that the
learning of FINGER and TIMING sequences did not interfere with
each other and can be tested in the same PET experiment. However
learning of a COMBINED sequence could not be tested in the same
experiment with learning of a FINGER or a TIMING sequence
because of interference. We therefore conducted two PET experiments using different set of subjects, and tested the RANDOM,
FINGER, and TIMING conditions in experiment1, and the RANDOM
and COMBINED conditions in experiment2.
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K. SAKAI, N. RAMNANI, AND R. E. PASSINGHAM
tion time, 9.5 s; echo time, 4 ms; inversion time, 600 ms; voxel size
1 ⫻ 1 ⫻ 1.5 mm, 108 axial slices).
Image data analysis
The data were analyzed with SPM99 (http://www.
fil.ion.ucl.ac.uk/spm) in Matlab (Version 5; Mathworks, Sherborn,
MA). The PET images were realigned with respect to the first scan of
the time series to correct for head movement. Six parameters (3
translations and 3 rotations) were extracted from the rigid body
transformation that minimized the difference between each image and
the reference image. A mean rCBF image was also computed. Then,
for each subject, the structural T1 image was corregistered to the mean
rCBF image. The structural image was then spatially normalized into
the system of reference of Talairach and Tournoux (1988) using as
template the averaged image from the Montreal Neurological Institute
(MNI) series (Cocosco et al. 1997). Twelve linear parameters (translation, rotation, zoom, and shear) were estimated for correcting the
position and size of the structural images with respect to the template
image. Residual differences between each pair of images were corrected using nonlinear basis functions (Friston et al. 1995). The
normalization parameters were then applied to the PET images. Finally, the PET images were subsampled to a voxel size of 2 mm and
were smoothed with an isotropic Gaussian kernel of 12 mm full-width
half-maximum, to conform to the multivariate Gaussian assumptions
of the data on which SPM is based and also to reduce the variance due
to individual anatomical variability.
PREPROCESSING.
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that showed no changes with learning, we excluded areas that showed
significant condition-by-session interactions.
For all the models, confounding covariates were removed by modeling the differences in rCBF between subjects as effects of no
interest. The effect of global differences in rCBF between scans was
also removed in the following way. Global flow (gCBF) was measured as the mean rCBF over all intra-cerebral voxels. The activity at
each voxel was then scaled such that the grand mean of all voxels was
50 ml/min/dl. Analysis of covariance was then used to model subjectspecific gCBF values as a confounding covariate.
For each comparison, a statistical parametric map of the t statistics,
SPM{T}, was created using a general linear model with a threshold of
P ⬍ 0.001, uncorrected. Only activities involving contiguous clusters
of at least five voxels were reported.
Because subjects were not treated as a random effect, it could be
argued that the obtained results may only reflect the brain activation
pattern of the subjects studied and cannot be generalized at a population level. However, the scan-to-scan variability within a PET
session and the session-by-contrast interactions are about the same,
and thus in PET, the difference between inferences based on fixedand random-effects analyses is greatly attenuated (Friston et al. 1999).
RESULTS
Behavioral data
The mean reaction times (RTs) of the key presses to visual
stimuli are shown in Fig. 2A (left, experiment 1; right, experiment 2). The trials with incorrect button presses, which accounted for only 2.8% of the total, were excluded from the
analysis. As shown in the figure, the RTs decreased from
session 1 to session 4 (or session 6) (from left to right in the
figure) in all three learning conditions (FINGER, red; TIMING, green; COMBINED, cyan), as well as in the RANDOM
condition (white). There was a significant condition-by-session
interaction (P ⬍ 0.05), indicating the difference in the rate of
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We compared each of the
three learning conditions with the RANDOM condition: FINGER
versus RANDOM and TIMING versus RANDOM in experiment1 and
COMBINED versus RANDOM in experiment 2.
We analyzed the data using a fixed-effects model that included
covariates representing effects of interest and confounds. Separate
covariates were given to the four learning sessions (separately for
each of FINGER and TIMING conditions in experiment 1) or six
learning sessions (COMBINED in experiment2), in the order these
sessions were performed. Another set of covariates were given to the
four (experiment1) or six (experiment 2) control RANDOM sessions,
also in the order performed.
First, we used a statistical model that looked for a condition-bysession interaction, in which the activity in a learning condition was
initially similar to the activity in RANDOM condition but became
increasingly higher over sessions than in the activity in RANDOM
(learning-related increment of activation). The covariates for the
learning condition were given linearly increasing weightings according to their position in the time series, reflecting an increase of activity
over time; correspondingly, the covariates for the RANDOM condition were given linearly decreasing weightings. Thus the model identifies voxels in which activity in the learning condition is similar to the
activity in the random condition at the start of learning, but becomes
significantly different from it in the final session of learning. Since
learning and control conditions were given alternately and their order
was counter-balanced across subjects, we were able to control for
changes in the brain activation over time, which were not related to
learning. As will be shown in RESULTS and Fig. 2, the decrement of
reaction time was approximately linear during the learning sessions.
Second, we used a statistical model that looked for a condition-bysession interaction, in which the activity in a learning condition was
initially similar to the activity in RANDOM condition but became
increasingly smaller over sessions (learning-related decrement of
activation). The covariates for the learning condition were given
linearly decreasing weightings according to their position in the time
series; correspondingly, the covariates for the RANDOM condition
were given linearly increasing weightings.
Third, we used a statistical model that looked for a main effect of
condition, in which the activity in a learning condition was higher than
the activity in RANDOM condition. To identify sustained activations
STATISTICAL MODEL AND INFERENCE.
FIG. 2. Reaction times (A) and SD of reaction times (B). Data from experiment1 (left) and experiment 2 (right). Mean and SE across subjects are shown
for each session for each condition (FINGER, red; TIMING, green; COMBINED, blue; RANDOM, white). For each condition, sessions progressed
from left to right (sessions 1–5 in experiment1 and sessions 1–7 in experiment2). The last postscan sessions were shown in light shading.
ACTION REPRESENTATION IN FRONTAL LOBE
2039
inter-stimulus intervals of the original sequence (P ⬎ 0.1).
These results suggest that the subjects had acquired explicit
knowledge of the sequence during scanning session.
Learning-related increment of activation
decrease in RTs across conditions. The rate of decrease was
significantly larger in FINGER and TIMING than in RANDOM (experiment 1; P ⬍ 0.05) and was significantly larger in
COMBINED than in RANDOM (experiment 2; P ⬍ 0.05). In
the last scan sessions, RTs were significantly smaller in COMBINED (268 ms) than in FINGER (292 ms) and TIMING (324
ms) (P ⬍ 0.05).
The RTs in the postscan sessions (shown in light shading in
Fig. 2A, session 5 in experiment 1 and session 7 in experiment
2) were not significantly different from the RTs in the last scan
sessions (session 4 in experiment 1 and session 6 in experiment
2; P ⬎ 0.1). The shortening of RT had reached a plateau stage
during the scanning sessions for the three learning conditions.
The across-trial variability in RTs also decreased concomitantly with learning. We calculated the SD of the RTs within
each session. Figure 2B shows its mean across subjects. There
was a significant condition-by-session interaction (P ⬍ 0.05),
and the SD of RTs decreased significantly more in the three
learning conditions than in RANDOM (P ⬍ 0.05). Such shortening of RT variability suggests an improvement of subjects’
performance not only in terms of quickness but also in terms of
stability of the performance. In the last scan sessions, the SD of
RTs was significantly smaller in COMBINED (46 ms) than in
FINGER (62 ms) and TIMING (70 ms) (P ⬍ 0.05).
After the scanning sessions, our subjects were able to reproduce the learned sequence without visual cues. In FINGER, all
the subjects correctly reproduced the order of fingers with
100% accuracy. In TIMING, the reproduced time intervals
between the eight moves did not differ significantly from those
of the presented sequence (P ⬎ 0.1). In COMBINED, subjects
correctly reproduced the order of fingers (100% accuracy). The
reproduced time intervals did not differ significantly from the
J Neurophysiol • VOL
TABLE
1. Increment of activation
Area
FINGER—RANDOM
SPL
IPS
IPS
Precuneus
TIMING—RANDOM
IPS
IPL
Inferior temporal gyrus
Cerebellum
COMBINED—RANDOM
DLPF
DLPF
PMd
PMd
PMv
PreSMA
Anterior cingulate cortex
IPS
IPS
Precuneus
Cerebellum
Coordinate
t-value
(⫺16, ⫺64, 46)
(⫺34, ⫺66, 36)
(46, ⫺48, 54)
(⫺6, ⫺58, 54)
3.95
3.51
3.61
3.85
(⫺38, ⫺64, 54)
(⫺52, ⫺56, 44)
(64, ⫺52, ⫺20)
(48, ⫺64, ⫺50)
3.65
3.84
4.02
3.72
(⫺38, 40, 20)
(24, 44, 20)
(⫺22, ⫺10, 58)
(34, 2, 60)
(⫺54, ⫺12, 44)
(⫺10, 6, 56)
(⫺6, 28, 36)
(⫺34, ⫺62, 48)
(50, ⫺52, 50)
(⫺6, ⫺74, 44)
(40, ⫺46, ⫺44)
4.13
3.48
4.65
3.18
3.70
5.00
3.78
3.54
3.66
3.72
3.30
Coordinates in MNI template brain (Cocosco et al. 1997) and Z scores for
peak activation foci are shown. SPL, superior parietal lobule; IPS, intraparietal
sulcus; IPL, inferior parietal lobule; DLPF, mid-dorsolateral prefrontal cortex;
PMd, dorsal premotor cortex; PMv, ventral premotor cortex; PreSMA, presupplementary motor area.
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FIG. 3. Areas showing learning-related increment of activation compared
with RANDOM. Statistical parametric maps indicating significant conditionby-session interaction (P ⬍ 0.001, uncorrected), in which activity in a learning
condition was initially similar to the activity in RANDOM condition, but
became increasingly higher over sessions. From top to bottom, active areas on
the left and right hemisphere in FINGER, TIMING, and COMBINED conditions are shown.
We first looked for a condition-by-session interaction, where
the difference in brain activation between the learning and
control conditions became increasingly larger as the learning
sessions progressed. Figure 3 shows brain areas with significant learning-related increment of activation compared with
RANDOM. Activations in FINGER, TIMING, and COMBINED are shown from top to bottom. The coordinates and the
t values for the active areas are listed in Table 1.
We found several brain areas that were specific to the
learning of a finger sequence or to the learning of a timing
sequence. The right intraparietal sulcus regions (IPS; red circle
in Fig. 3) and medial parietal cortex (precuneus) showed an
increment of activity in FINGER but not in TIMING. In
contrast, the lateral part of the right cerebellar hemisphere
showed an increment of activity in TIMING but not in FINGER (green circle in Fig. 3). The right IPS, precuneus, and
cerebellum showed an increment of activity in COMBINED,
where both finger sequence and timing sequence were learned.
In the left panels of Fig. 4 are shown the activation in the
right IPS on a axial section (A) and activation in the cerebellum
on a coronal section (B). Regions showing significant increment of activity in FINGER, TIMING, and COMBINED are
shown in red, green, and blue, respectively. Activation in
FINGER and COMBINED overlapped in the right IPS (shown
in pink in Fig. 4A), whereas activation in FINGER and COMBINED overlapped in the cerebellum (shown in cyan in Fig.
4B). The adjusted rCBF data for the activation in the right IPS
(coordinate: 50, ⫺50, 54) and cerebellum (48, ⫺64, ⫺50) are
2040
K. SAKAI, N. RAMNANI, AND R. E. PASSINGHAM
shown in the center (experiment 1) and on the right (experiment 2) in Fig. 4, A and B. The rCBF in RANDOM, FINGER,
TIMING, and COMBINED are colored in white, red, green,
and blue, respectively. As the learning sessions progressed
(from left to right in each panel), the rCBF in the right IPS
gradually increased in FINGER and COMBINED but not in
RANDOM and TIMING. The rCBF in the cerebellum decreased in RANDOM and FINGER but stayed constant in
TIMING and COMBINED. Thus relative to RANDOM, the
cerebellum in TIMING and COMBINED showed an increment
of activation.
In contrast to these activations that are specific to the effector or temporal domain, the left intraparietal sulcus region
(IPS) showed an increment of activity in all of the three
learning conditions (yellow circle in Fig. 3). In the left panel of
Fig. 4C, activation in FINGER, TIMING, and COMBINED
overlapped in the left IPS (shown in white). As shown in the
center and right panels, the rCBF in the left IPS (⫺38, ⫺64,
54) decreased in RANDOM and increased in FINGER, TIMING, and COMBINED.
The prefrontal cortex and medial and lateral premotor areas
showed a different pattern of activation. They showed an
increment of activity only in COMBINED, but not in the other
conditions (blue circle in Fig. 3). The activity in the prefrontal
cortex, medial premotor area, and lateral premotor area is
shown in Fig. 5, A–C. The activity in the prefrontal cortex was
observed bilaterally within the middle frontal gyrus (DLPF)
(Fig. 5A, left), and this was confirmed from the structural
images of each subject. This area was considered to be BrodJ Neurophysiol • VOL
mann’s area 46 (Rajkowska and Goldman-Rakic 1995). The
adjusted rCBF for the activation of DLPF (⫺38, 40, 20) did not
change significantly across sessions in FINGER and TIMING
compared with RANDOM (Fig. 5A, center), but showed an
increment over sessions in COMBINED (Fig. 5A, right). The
activity in the medial premotor area had its peak anterior to the
anterior commissure and above the cingulate sulcus, and thus
was considered to be the presupplementary motor area (PreSMA; Fig. 5B). The activity in the lateral premotor area had its
peak around the precentral sulcus at Z ⬎50, and thus was
considered to be the dorsal premotor area (PMd; Fig. 5C). As
for the DLPFC, the rCBF for the Pre-SMA (⫺10, 6, 56) and
PMd (⫺22, ⫺10, 58) showed a learning-related increment
only in COMBINED.
Learning-related decrement of activation
We also looked for condition-by-session interactions, where
brain activation in learning conditions became progressively
smaller compared with that in the control condition (coordinates shown in Table 2). We found that the medial temporal
lobe area (MTL) and the inferior temporal lobe region (IT) on
the right showed a learning-related decrement of activation.
The left panels of Fig. 6 show activation in MTL (A) and IT
(B). The decrement of activation is shown by the rCBF plots on
the center and the right in Fig. 6. The MTL showed a learningrelated decrement of activation in FINGER and COMBINED
(note overlap of red and blue regions in Fig. 6A, left). The peak
of MTL activation (24, ⫺22, ⫺18) lay within the parahip-
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FIG. 4. Segregation and overlap of areas showing
learning-related increment of activation in learning
effector and temporal sequences; A: right intraparietal
sulcus region; B: cerebellum; C: left intraparietal sulcus region. Left: activation superimposed onto a section of the MNI template brain. Active areas in the 3
learning conditions are coded in red, green, and cyan
for FINGER, TIMING, and COMBINED conditions,
respectively. The overlaps between FINGER and
TIMING, FINGER and COMBINED, and TIMING
and COMBINED are coded in magenta, yellow, and
cyan, respectively. The overlaps between the 3 conditions are shown in white. The left side of the brain is
shown on the left. Center and right: adjusted rCBF for
the voxel indicated by cross hairs on the left. Their
coordinates are (50, ⫺50, 54; A), (48, ⫺64, ⫺50; B),
and (⫺38, ⫺64, 54; C). Bars in red, green, blue, and
white indicate rCBF in FINGER, TIMING, COMBINED, and RANDOM, respectively. The sessions
progressed from left to right.
ACTION REPRESENTATION IN FRONTAL LOBE
2041
pocampal cortex. The area also showed a learning-related
decrement of activation in TIMING at a lower threshold (P ⬍
0.005). In contrast, IT (52, ⫺34, ⫺30) showed a decrement of
activation only in COMBINED but not in FINGER and TIMING (Fig. 6B, center and right).
Sustained activation
We then looked for a main effect of learning without condition-by-session interactions. Table 3 shows coordinates of
TABLE 2. Decrement of activation
Area
FINGER—RANDOM
MTL
OFC
Medial occipital region
TIMING—RANDOM
MTL
STG
PMv
OFC
COMBINED—RANDOM
MTL
IT
Posterior cingulate cortex
Lateral occipital region
Medial occipital region
Coordinate
t-value
(24, ⫺22, ⫺18)
(38, 34, ⫺22)
(14, ⫺94, 22)
3.83
3.75
3.68
(24, ⫺22, ⫺16)
(54, ⫺22, 6)
(62, 2, 22)
(4, 50, ⫺10)
3.18*
3.92
3.53
3.35
(30, ⫺26, ⫺18)
(52, ⫺34, ⫺30)
(⫺2, ⫺16, 42)
(44, ⫺80, 8)
(6, ⫺88, 0)
3.73
3.54
3.76
3.91
3.28
MTL, medial temporal lobe region; OFG, orbitofrontal cortex; STG, superior temporal gyrus; IT, inferior temporal lobe region. * MTL activation in
TIMING—RANDOM is just below the threshold (P ⬍ 0.001).
J Neurophysiol • VOL
areas in which activations were significantly higher in the
learning conditions than in RANDOM, but were constant
across sessions. We found that the anterior fronto-polar region
(APF; Fig. 7A) and the ventrolateral prefrontal cortex (VLPF;
Fig. 7B) showed such sustained activation.
For the APF, the overlap between the three conditions (colored in white in Fig. 7A, left) lay around the inferior frontal
sulcus, extending anterior to the sulcus but not extending to the
orbital surface. The area was well anterior and inferior to area
46 (Rajkowska and Goldman-Rakic 1995), and thus was considered to be area 10. On the center and right in Fig. 7A is
plotted the adjusted rCBF for APF (⫺30, 60, 4). As shown, the
APF was more active in all of the three learning conditions
than in RANDOM, and the activity did not show linear trend of
changes across learning sessions.
In contrast, the left VPLF showed sustained activation only
in COMBINED (Fig. 7B). The peak lay in the pars triangularis
of the inferior frontal gyrus, anterior to the ascending branch of
the lateral fissure. The area was considered to be area 45
(Amunts et al. 1999). The rCBF at this peak (⫺48, 22, 12)
showed a sustained increase of activation only in COMBINED
(Fig. 7B, center and right).
DISCUSSION
Our results show partial segregation of the neural mechanisms in learning of finger and timing sequences. Moreover,
the results suggest that the two learning mechanisms interact in
the frontal lobe. The prefrontal and premotor areas were active
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FIG. 5. Areas showing learning-related increment
of activation specifically in learning a combined sequences. A: dorsolateral prefrontal cortex; B: presupplementary motor area; C: dorsal premotor cortex.
Left: activation superimposed onto a section of the
MNI template brain. Color codes are the same as in
Fig. 4. Center and right: adjusted rCBF for the voxel
indicated by cross hairs on the left. Their coordinates
are (⫺38, 40, 20; A), (⫺10, 6, 56; B), and (⫺22, ⫺10,
58; C).
2042
K. SAKAI, N. RAMNANI, AND R. E. PASSINGHAM
FIG. 6. Areas showing learning-related decrement
of activation. A: medial temporal lobe; B: inferior
temporal gyrus. Left: activation superimposed onto a
section of the MNI template brain. Color codes are the
same as in Fig. 4. Center and right: adjusted rCBF for
the voxel indicated by cross hairs on the left. Their
coordinates are (24, ⫺22, ⫺18; A) and (52, ⫺34,
⫺30; B).
Separate and overlapping mechanisms for learning effector
and temporal sequences
We found that the learning of a finger sequence was associated with an increment of activity in the right intraparietal
sulcus region (IPS) and medial parietal area (precuneus),
whereas learning of a timing sequence was associated with an
increment of activity in the lateral part of the cerebellum. The
result suggests distinctive learning mechanisms for effector
and temporal information.
Activation in the right IPS and precuneus in effector sequence learning has been shown by a number of previous
imaging studies (Doyon et al. 2002; Grafton et al. 1995;
Hazeltine et al. 1997; Honda et al. 1998; Jenkins et al. 1994;
Jueptner et al. 1997; Sakai et al. 1998a; Toni and Passingham
1999; Toni et al. 1998). The IPS, especially on the right side,
TABLE
3. Sustained activation
Area
FINGER—RANDOM
APF
Insula
IPS
IPS
TIMING—RANDOM
APF
IPS
Cerebellum
COMBINED—RANDOM
APF
VLPF
PMd
IPS
Coordinate
t-value
(⫺26, 58, 6)
(⫺32, 8, 10)
(50, ⫺56, 50)
(⫺32, ⫺66, 40)
4.19
4.25
4.05
3.94
(⫺40, 60, 2)
(⫺40, ⫺64, 54)
(38, ⫺72, ⫺28)
4.36
4.24
3.43
(⫺24, 60, 4)
(⫺48, 22, 12)
(46, 2, 46)
(44, ⫺74, 52)
4.04
4.35
5.02
3.98
APF, anterior prefrontal cortex; VLPF, ventrolateral prefrontal cortex.
J Neurophysiol • VOL
is thought to process spatial information (Corbetta et al. 2000;
Culham and Kanwisher 2001). In our task, the relationship
between the fingers and buttons was spatially compatible and a
finger sequence may have been learned as a spatial sequence.
The precuneus may also be related to spatial components of
sequence processing (Sadato et al. 1996). Alternatively, the
precuneus may be involved in visual imagery processes
(Fletcher et al. 1996) in anticipation of an upcoming stimulus,
which developed during learning. When a spatial component
was removed by using a color-motor association or auditorymotor association, there was no activation in the right IPS and
precuneus (Hazeltine et al. 1997; Sakai et al. 2000).
Cerebellar activation in learning of a timing sequence is
consistent with a previous study by Ramnani and Passingham
(2001), which has shown an increment of cerebellar activation
concomitant with the progress of rhythm learning. The cerebellum has been regarded as one of the key structures for
timing processing (Ivry and Keele 1989; Sakai et al. 1998b).
Interestingly, the increment of cerebellar activation was relative rather than absolute. The area showed sustained activation
during learning, but decreased over time in the control. A
similar finding was also observed in Ramnani and Passingham
(2001). We consider the decrement of cerebellar activation in
control as nonspecific time effects and the sustained activation
in TIMING as reflecting learning-related activation. Another
possibility is that in RANDOM and FINGER the brain initially
tried to detect and learn a temporal pattern although it was
impossible to learn. As a sequence was repeated, the cerebellar
learning mechanisms were disengaged because there was no
fixed temporal pattern. In TIMING, the cerebellar learning
mechanisms kept operating throughout the sessions. It remains
undetermined whether the cerebellar activation in this study
reflects the representations of the learned timing or learning
processes per se. Recently, Penhune and Doyon (2002) have
shown a decrease of cerebellar activation when a timing sequence was learned extensively over 5 days. As they have
suggested, the cerebellar activation in the present study may
reflect an early phase of the timing learning mechanism, in
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only when subjects learned both. Based on the results, we
argue for the role of the frontal lobe in integrating the effector
and timing information and implementing action-oriented representations.
ACTION REPRESENTATION IN FRONTAL LOBE
2043
FIG. 7. Areas showing sustained activation. A: anterior fronto-polar cortex; B: ventrolateral prefrontal
cortex. Left: activation superimposed onto a section of
the MNI template brain. Color codes are the same as
in Fig. 4. Center and right: adjusted rCBF for the
voxel indicated by cross hairs on the left. Their coordinates are (⫺30, 60, 4; A) and (⫺48, 22, 12; B).
Frontal areas were not active in learning at an abstract
level
Surprisingly, we did not find significant activation in the
frontal lobe when subjects learned either a sequence of fingers
(FINGER) or a sequence of timing (TIMING) alone. This is in
marked contrast to the previous studies that showed activation
in prefrontal and premotor areas for both learning of the order
of a finger sequence (Grafton et al. 1995; Hazeltine et al. 1997;
Honda et al. 1998) and learning of the timing of movements
(Ramnani and Passingham 2001). In those studies, effector
learning was tested while timing of movements was set conJ Neurophysiol • VOL
stant, and timing learning was tested while the order of the
fingers was set constant. Under these conditions, as subjects
learned a sequence, they knew which finger to be used and at
which timing to execute the movements. Thus subjects were
able to prepare a specific action sequence. In contrast, our
experiment 1 used random timing for learning of a finger
sequence and random order of fingers for learning of a timing
sequence. Under these conditions, subjects did not know at
which timing to execute a FINGER sequence or with which
finger to execute a TIMING sequence. Thus subjects were not
able to prepare a specific action sequence; they learned the
sequence only at an abstract level. Thus the frontal lobe takes
part when subjects can prepare for a specific action sequence,
in other words, when a sequence is learned at an actionoriented level rather.
The idea is consistent with the finding in the previous studies
that used a simple reaction time task (Deiber et al. 1991, 1996;
Jahanshahi et al. 1995; Jenkins et al. 2000). The prefrontal
cortex was not active either when the finger to be used was
indicated by unpredictable cues (Deiber et al. 1991, 1996;
Jahanshahi et al. 1995) or when the timing of finger movements was externally triggered by unpredictable cues (Jenkins
et al. 2000). Instead, the prefrontal cortex was active when the
finger to be used was chosen by the subjects and the timing of
finger movements was predictable (Deiber et al. 1991, 1996;
Jahanshahi et al. 1995) or when the timing was chosen by the
subjects and the same finger was used (Jenkins et al. 2000).
Thus the critical determinant of prefrontal activation is whether
the subjects knew which finger to move and when to make the
finger movement. This idea was supported by the significant
activation in frontal areas when subjects learned a finger sequence and a timing sequence (COMBINED).
Interaction between effector and temporal sequence learning
We found that the mid-part of the DLPF, as well as the PMd
and Pre-SMA, was specifically active in COMBINED. As
discussed above, only in this condition were the subjects able
to prepare specific action in terms of effector and timing. The
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which adjustment of movement timing to visual cues is thought
to occur.
In COMBINED where learning occurred both in terms of
finger and timing sequences, the right IPS, precuneus and the
cerebellum were active, consistent with the idea that the right
IPS and precuneus were involved in learning of a finger sequence and that the cerebellum was involved in learning of a
timing sequence. The two sets of brain areas were simply
added in learning both types of sequences.
We also found that the learning of an effector sequence and
a temporal sequence share increment of activation in the left
IPS, suggesting an overlap between the two learning mechanisms. The finding is consistent with Sakai et al. (2000), which
showed common involvement of the left IPS in both effector
selection and timing adjustment in a choice reaction time task.
In fact, this area has been shown to be active in finger sequence
learning (Doyon et al. 2002; Grafton et al. 1995; Hazeltine et
al. 1997; Honda et al. 1998; Jenkins et al. 1994; Jueptner et al.
1997; Sakai et al. 1998a; Toni and Passingham 1999; Toni et
al. 1998) and also in timing sequence learning (Ramnani and
Passingham 2001; Sakai et al. 2000; Schubotz and von Cramon
2001). As suggested by Ramnani and Passingham (2001), this
parietal region may play roles in sensori-motor mapping of
temporal relations (converting timing cues to timed finger
movements) as well as spatial relations (converting spatial cues
to spatially compatible finger movements).
2044
K. SAKAI, N. RAMNANI, AND R. E. PASSINGHAM
J Neurophysiol • VOL
stages. Instead, the difference in RT reflects the difference in
the level of sequence coding; a sequence coded at an actionoriented level (in COMBINED) can be performed quicker than
a sequence coded at an abstract level (in FINGER and TIMING) because subjects can prepare a specific finger movements
at a specific timing.
The DLPF and frontal premotor areas may play roles in
integrating the information in effector (finger) and temporal
(timing) domains. Our results suggest that the initial processing
in the two domains may be carried out independently: the right
IPS and precuneus for the effector information and the cerebellum for the temporal information. The DLPF may receive
and integrate the two kinds of information from these domain
specific posterior areas (Cavada and Goldman-Rakic 1989;
Middleton and Strick 2001; Petrides and Pandya 1984) and
construct action-oriented representations for a motor sequence.
By “action-oriented representations,” we do not intend to imply that the DLPF is involved in coding of specific movements.
There is other evidence that the information is integrated in the
DLPF: Prabhakaran et al. (2000) have shown that there is more
activation in the dorsolateral prefrontal cortex when letters and
positions have to be integrated rather than being remembered
separately.
Time course of prefrontal activation
The DLPF was less active at the beginning of learning, but
became more active as the subjects learned the sequence. The
direction of the time course of prefrontal activation, however,
differed in different studies. Some studies on motor sequence
learning have shown an increment of activation in DLPF as in
the present study (Grafton et al. 1995; Hazeltine et al. 1997;
Honda et al. 1998), whereas other studies have shown a decrement of activation in similar regions (Jenkins et al. 1994;
Jueptner et al. 1997; Sakai et al. 1998a; Toni and Passingham
1999; Toni et al. 1998). This difference results from the difference in the strategies to learn a motor sequence. A learningrelated increment of prefrontal activation was observed when
subjects initially responded to externally presented stimuli, but
later gained explicit knowledge of a motor sequence and became able to prepare for the next movements (Grafton et al.
1995; Hazeltine et al. 1997; Honda et al. 1998). In contrast, a
decrement of prefrontal activation was observed when subjects
initially learned a sequence by trial and error, and later the task
performance became automatic (Jenkins et al. 1994; Jueptner
et al. 1997; Sakai et al. 1998a; Toni and Passingham 1999;
Toni et al. 1998). In the present study, the task was not learned
by trial and error. Our subjects were required to maintain
attention throughout learning sessions so as to respond as
accurately and quickly as possible. It has been shown that
attention to the performance of a well-learned sequence results
in reactivation of the DLPF (Jueptner et al. 1997).
DLPF differs from APF and VLPF
The DLPF was distinguished from anterior fronto-polar region (APF), in which activity was found both at an action level
of sequence learning (COMBINED) and at an abstract level of
sequence learning (FINGER and TIMING). This area also
differed from the DLPF in that it was active throughout the
learning sessions. APF may play a role in forward planning of
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increment of activity in prefrontal and premotor areas may
reflect increased level of preparatory activity in these regions.
Both the PMd and Pre-SMA show motor preparatory activity
in monkeys and humans (Kurata 1993; Matsuzaka et al. 1992;
Weilke et al. 2001). Other reports also suggest that the DLPF
is involved in the active preparation for forthcoming action
(Pochon et al. 2001; Quintana and Fuster 1999). There are
massive projections from DLPF to the PMd and Pre-SMA
(Bates and Goldman-Rakic 1993; Lu et al. 1994), and they may
transmit action control signals to enable quick and accurate
movements.
The prefrontal cortex is also active when subjects imagine or
mentally simulate movements (Deiber et al. 1998; Gerardin et
al. 2000). Such activation is thought to reflect active maintenance of action representations. The increment of the frontal
activation observed in this study can also be taken to reflect the
development of action representations during learning.
Because we used different subjects for experiments 1 and 2,
it could be argued that the frontal activation observed in
COMBINED may only reflect the difference in the subjects
group. However, unlike fMRI studies, the between-subjects
variability in rCBF in PET studies is similar to the withinsubject, inter-session variability in rCBF (Friston et al. 1999).
In addition, as shown in Fig. 5, the time course of prefrontal
activation in RANDOM were similar between experiments 1
and 2. We believe that the difference in subjects groups accounted little for the different pattern of frontal lobe activation.
There could be a confound due to the difference in the experimental context; subjects in experiment 1 learned two sequences, FINGER and TIMING, whereas subjects in experiment 2 learned only one sequence, COMBINED. Thus in
experiment 1, there may be interference effects from learning
one sequence over learning of the other. However, as far as the
behavioral data were concerned, there was no interference
between learning of a finger sequence and learning of a timing
sequence. We believe that the contextual difference between
experiments 1 and 2 had little effect on the learning processes.
Interestingly, when FINGER and COMBINED were tested in
the same session, we observed significant interference effects.
This is probably because the two sequences used different
sequences of finger movements. Thus it seems that interference
occurs when the two sequences contain information of the
same domain (effector or temporal information), but interference does not occur when the two sequences contain information of different domains.
The activity in frontal areas cannot be explained in terms of
task difficulty because the activity increased as RTs shortened.
It is also unlikely that the frontal activation is associated with
quick performance per se, because the activity did not increase
in FINGER and TIMING where the RTs decreased with learning (see Fig. 5). There could be a difference in the progress of
learning stages between FINGER, TIMING, and COMBINED;
in COMBINED learning may have occurred faster than in
FINGER and TIMING. However, the shortening of reaction
times reached a plateau in the final session in FINGER and
TIMING as in COMBINED. As far as the reaction times were
concerned, the learning stage seems to have reached an advanced level during the scanning session similarly in all the
three conditions. We do not think that the difference in RT at
the final session of learning between COMBINED and other
two learning conditions is due to the difference in the learning
ACTION REPRESENTATION IN FRONTAL LOBE
Learning-related decrement of activation
We found that the MTL showed a decrement of activation in
FINGER and COMBINED relative to RANDOM. In FINGER
and COMBINED, the eight patterns of visual stimuli were
presented in the same order for every trial. As subjects learn
the sequence, they were able to predict the next stimulus
pattern, i.e., the arrangement of one asterisk and two horizontal
bars. In contrast, the stimulus pattern was unpredictable for
every trial of RANDOM. The decrement of MTL activation
may reflect an increase in the predictability of the stimulus
pattern. The decrement of MTL activation was also found in
TIMING at a lower threshold, which may suggest an involvement of this area in the prediction of timing (N. Ramnani and
R. E. Passingham, unpublished observation). The decrement of
MTL activation may also reflect less demanding encoding
processes or more efficient retrieval processes concomitant
with the learning.
We also found a decrement of activation in the IT, but only
in COMBINED. IT has been shown to respond to perception
and memory of visual patterns (Miyashita 1993). Only in
COMBINED did stimulus occurrence become predictable both
in terms of the stimulus pattern and timing of stimulus presentation. IT may be sensitive to the predictability of both visual
pattern and timing. Ramnani and Passingham (2001) also
J Neurophysiol • VOL
found a decrement of activation in a similar region. In their
study, the timing of stimulus presentation was learned and the
sequence of stimulus pattern had been overlearned.
Action-oriented representation in the frontal lobe
An action-oriented representation of a motor sequence requires both effector information (with which finger to execute
the motor sequence) and temporal information (at which timing
to execute the motor sequence). We only have an abstract
representation for the motor sequence when we learn either of
them. Our results suggest that the frontal lobe is specifically
involved in action-oriented representations, whereas the posterior areas (parietal and cerebellar) are involved in abstract
representations. This is consistent with the idea that the frontal
areas select and implement specific motor action while the
parietal areas maintain the spatial representation of the potential action (Kalaska et al. 1997; Scott et al. 1997). Furthermore,
we have added the new finding that the frontal action representation also requires the timing information with which the
motor action is carried out. An action-oriented representation
may be an integrative product of spatial/effector and temporal
information. It remains open how an abstract representation for
finger order sequence and an abstract representation for temporal sequence are transformed into an action-oriented representation. We predict that dynamic interactions between the
frontal lobe and posterior regions are the key to achieve this
transformation.
This study was supported by the Wellcome Trust. K. Sakai was supported by
the Human Frontier Science Program.
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sequence performance, which is necessary for performance of
an action sequence as well as performance of an abstract
sequence. A similar region was active in Tower of London task
(Baker et al. 1996), as well as in a range of prospective
memory tasks (Burgess et al. 2000). Especially in sequence
learning, it is essential that subjects hold in mind the entire
sequence information (a main goal) while processing individual button presses (concurrent subgoals). APF activation in this
study may reflect such branching processes (Koechlin et al.
1999) or the set-up of multiple processing nodes during sequence performance (Tanji and Hoshi 2001). Alternatively, the
APF activation may reflect episodic retrieval of the learned
sequence representations regardless of whether they are abstract or action-oriented representations (see Buckner and
Koutstall 1998).
There was also a sustained activation in the VLPF, but it
differed from the APF in that it was active only in COMBINED, suggesting its specific involvement in an action-oriented representation. Passingham et al. (2000) argued that the
VLPF represents the association between visual cues and action. Its sustained activation in this study might reflect active
maintenance of over-learned visuo-motor association to enable
quick and accurate action performance. This region showed a
sustained activation across sessions when subjects performed
an overlearned visuo-motor association task (Passingham et al.
2000).
A similar regional dissociation between an increment of
activation and sustained activation was observed in the lateral
premotor cortex: The more dorsal part of the premotor cortex
(Z ⫽ 58 on the left and 60 on the right hemisphere) showed an
increment of activation in COMBINED, whereas the more
ventral part of the premotor cortex (Z ⫽ 46) showed sustained
activation in COMBINED. This may reflect their separate
inputs from the DLPF and VLPF (Lu et al. 1994; Pandya and
Yeterian 1996; Preuss and Goldman-Rakic 1989).
2045
2046
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