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Volume 17, Number 1,
Issue of January 1, 1997
pp. 409-419
Copyright ©1997 Society for Neuroscience
Functional Stages in the Formation of Human Long-Term Motor
Memory
Reza Shadmehr1 and
Thomas Brashers-Krug2
Departments of 1 Biomedical Engineering and
2 Psychiatry and Behavioral Sciences, The Johns Hopkins
University, Baltimore, Maryland 21205-2195
ABSTRACT
- INTRODUCTION
- MATERIALS AND METHODS
- RESULTS
- DISCUSSION
- FOOTNOTES
- REFERENCES
ABSTRACT 
Previous research has demonstrated that the primate CNS has the
ability to learn and store multiple and conflicting visuo-motor maps.
Here we studied the ability of human subjects to learn to make reaching
movements while interacting with one of two conflicting mechanical
environments as produced by a robotic manipulandum. We demonstrate that
two motor maps may be learned and retained, but only if the training
sessions in the tasks are separated by an interval of ~5 hr. If the
interval is shorter, learning of the second map begins with an internal
model appropriate for the first task and performance in the second task
is significantly impaired. Analysis of the after-effects suggests that
with a short temporal distance, learning of the second task leads to an
unlearning of the internal model for the first. With the longer
temporal distance, learning of the second task starts with an unbiased internal model, and performance approaches that of naives. Furthermore, the memory of the consolidated skill lasts for at least 5 months after
training. These results argue for a distinct change in the state of
resistance of motor memory (to disruption) within a few hours after
acquisition. We suggest that motor practice results in memories that
have at least two functional components: soon after completion of
practice, one component fades while another is strengthened. A further
experiment suggests that the hypothetical first stage is not merely a
gateway to long-term memory, but also temporary storage for items of
information, whether new or old, for use in the near-term. Our results
raise the possibility that there are distinct neuronal mechanisms for
representation of the two functional stages of motor memory.
Key words:
motor learning;
motor memory;
consolidation;
short-term
memory;
long-term memory;
reaching movements;
internal models;
virtual
environments;
motor control
INTRODUCTION
In novelty stores, one can find an object that
appears to be a heavy brick but is actually constructed of light
plastic. When one is asked to rapidly move the "brick," the result
is a flailing-like arm motion. This observation suggests that in
programming the motor output to the muscles of the arm, the CNS uses an
internal model (Wolpert et al., 1995b
) to predict the mechanical
dynamics of the task (Gottlieb, 1994). In theory, the internal model
(IM) is an association from a desired trajectory for the hand (Wolpert et al., 1995a) to a pattern of muscle torques (Shadmehr and
Mussa-Ivaldi, 1994). Because, in principle, this map is unique for the
objects that we have learned to interact with, "motor memory" may
be thought to contain, at least in part, a collection of IMs where
visual information serves as an identifying cue that allows for binding of an appropriate association (Gordon et al., 1993), i.e., recall.
Because we routinely use our hands to interact with a remarkably
diverse variety of mechanical systems, the ability to learn and recall
IMs is likely a fundamental property of the motor system. Indeed,
practice of arm movements with a novel mechanical system leads to
formation of an IM for that system. The evidence for this comes from
EMG studies (Milner and Cloutier, 1993; Gottlieb, 1994; Thoroughman and
Shadmehr, 1996), and from studies that have quantified movement
trajectories when the mechanical system's dynamics have been
unexpectedly changed (Sanes, 1986; Lackner and Dizio, 1994; Shadmehr
and Mussa-Ivaldi, 1994; Gandolfo et al., 1996). Once the IM is
acquired, it becomes available for "recall"; performance is
significantly improved when tested 24-48 hr later (Shadmehr et al.,
1995). In this report, we show that the improvement in performance
persists for at least 5 months, suggesting the formation of long-term
motor memories.
Although we know little about the processes that culminate in long-term
motor memory formation (Halsband and Freund, 1993; Salmon and Butters,
1995), a feature of memory across the animal kingdom is that it
continues to develop after practice has stopped (Seeds et al., 1995).
In general, memory appears to progress functionally from a short-lived
fragile form to a long-lasting stable form (Bailey and Kandel, 1995;
DeZazzo and Tully, 1995). Phases of memory are often distinguished with
respect to their sensitivity to new experiences and susceptibility to
interference and injury (Tully et al., 1994; Hammer and Menzel, 1995).
The progression to long-term memory is referred to as consolidation,
and the time during which information becomes consolidated has been
used to functionally define short-term memory (Fuster, 1995).
Does formation of motor memory progress from a short-term, fragile form
to a long-term, stable form? The distinction is not merely semantic.
Differences in the functional properties of phases that culminate in
long-term memory have suggested that training sets in motion events
that develop in parallel biochemical pathways (Tully et al., 1994) in
possibly distinct anatomical sites (Rose, 1991; de Belle and
Heisenberg, 1994). For example, during storage of "declarative"
information (Squire, 1992), distinct regions of the brain are believed
to encode the memory during the short-term and long-term stages (Squire
et al., 1984a; Alvarez and Squire, 1994; Guigon and Burnod, 1995;
McClelland et al., 1995). The hippocampus (Zola-Morgan and Squire,
1990; Kim et al., 1995) and its inputs (Rashidy-Pour et al., 1996)
appear to play a time-limited role in consolidation of declarative
memory. Learning of visuo-motor skills, however, does not appear to
depend on the integrity of medial temporal lobe structures (Corkin,
1968; Gabrieli et al., 1993). Furthermore, interventions that interfere
with formation of long-term declarative memory appear to spare
retention of visuo-motor skills (Squire et al., 1984b). However, motor
memories are also vulnerable (Lewis et al., 1951; Lewis and Miles,
1956; Heilman and Gonzalez-Rothi, 1985; Clark et al., 1994). Our
results have suggested recently that retention of a newly acquired IM
could be disrupted when a second IM, anticorrelated to the first, was learned (Brashers-Krug et al., 1995b). However, if IM2 was
learned beyond a critical time window (~4 hr) after acquisition of
IM1, it had little effect on recall of IM1
(Brashers-Krug et al., 1996). In other words, within hours, the
representation of IM1 became gradually less vulnerable to
the "intervention" caused by learning of IM2.
Furthermore, the ability to learn IM2 became progressively better with temporal distance from IM1 (Brashers-Krug et
al., 1996). That is, subjects had an easier time learning
IM2 when 4 hr had passed since learning IM1.
This is surprising; if learning of IM2 involves some form
of unlearning of IM1, then one would expect from the
initial labile form of IM1 that it should be easier to
learn IM2 when the temporal interval between the two
training sessions is short. The opposite was observed. To understand
this paradox, we report on further experiments that use the concept of
after-effects to quantify the effect of time on representation of a
recently acquired motor skill.
MATERIALS AND METHODS
The purpose of our experiment was to reveal functional
properties of processes that lead to formation of long-term motor
memory. The motor task we considered was one in which human subjects
learned to make reaching movements while holding the handle of a robot manipulandum (Fig. 1). A mathematical model was
developed to provide a framework for the human/robot force
interaction.
Fig. 1.
The robot manipulandum and the experimental setup.
The manipulandum is a very low-friction, planar mechanism powered by
two high-performance torque motors. The subject grips the handle of the
robot. The handle houses a force transducer. The video monitor facing
the subject displays a cursor corresponding to the position of the
handle. A target position is displayed, and the subject makes a
reaching movement. With practice, the subject learns to compensate for
the forces produced by the robot.
[View Larger Version of this Image (71K GIF file)]
Experimental setup. Sixty right-handed subjects with no
known neurological history, ranging in age from 19 to 37 years,
participated in this study. The procedures were approved by the Johns
Hopkins University Joint Committee on Clinical Investigation, and all subjects signed an informed consent form.
Subjects learned to make reaching movements while interacting with a
force producing manipulandum. A schematic and photo of the measurement
apparatus are shown in Figure 1. Each subject was seated on a chair
that was bolted onto an adjustable positioning mechanism and instructed
to grip the handle of a robot manipulandum with the right hand. The
right upper-arm was supported in the horizontal plane by a rope
attached to the ceiling.
The Hopkins manipulandum is a two degree of freedom, portable,
lightweight (0.8 kg for the shoulder link and 1.3 kg for the elbow
link, including the force transducer), low-friction (0.02 and 0.06 N·m·sec viscous friction for shoulder and elbow joints) mechanism
built based on the mechanical design principles of the MIT device
(Faye, 1986; Charnnarong, 1991; Hogan et al., 1992) used in our
previous works. Two low-inertia, DC brushless torque motors (Kollmorgen
Corp., model RBEH-3003) driven by a pair of digital
pulse-width-modulated servoamplifiers (Kollmorgen, model FAST Drive)
were mounted on the base of the robot and independently delivered
torque to the robot's shoulder and elbow joints via a parallelogram
configuration. Robot's shoulder and elbow joint position measurements
were made using absolute optical encoders (Gurley Precision
Instruments) with a resolution of 0.0055°. Robot's shoulder and
elbow joint velocity measurements were made using a system composed of
incremental optical encoders, interpolators, and digital integrators
that resulted in a resolution of better than 440,000 counts per
revolution. The handle of the robot housed a 6-axis force/torque
transducer (Assurance Technologies, Inc).
Experimental procedures. The experimental task was similar
to that described in our previous reports (Shadmehr and Mussa-Ivaldi, 1994; Brashers-Krug et al., 1996). Subjects moved the cursor
corresponding to the position of their hand to a target position that
would appear at 10 cm in one of eight directions: four directions
starting from the center of the monitor (0°, 45°, 90°, and
135°) and the four corresponding directions back to the center from
each of those targets (180°, 225°, 270°, and 315°). Subjects
were instructed that there was a timing goal for the task. The timer
started as soon as subjects began their movement and stopped when they
ended their movement at the target. If they reached the target in the allotted time (500 ± 50 msec), the target would make a
distinctive sound. If they reached it too late, the target would turn
blue, and if they reached it too soon, it would turn red. Generally, after 400 targets subjects were able to move at the required pace. There were no perturbing forces during these movements and no further
analysis was made of this data. We refer to this situation when the
robot motors were inactive as the null field condition. Visual feedback regarding hand trajectory was provided throughout the
entire experiment.
Subjects returned on a subsequent day and were tested on the null
field. The trajectories recorded during this condition are referred to
as baseline trajectories; they are straight-line movements with "bell-shaped" linear velocity profiles and represent what we
consider to be the desired trajectory of the biological adaptive control system (after introduction of perturbing forces, movement kinematics generally converge back to this trajectory). After measurement of baseline trajectories, a brief period of rest was provided (2-3 min), after which the subjects were told that the robot
motors would now produce forces on their hand. Subjects were asked to
move the handle (at their own pace and without any targets) and
experience the forces for 10-15 sec, after which we began a target
set. A target set consisted of 192 targets, all in a
force field, except for 33 randomly chosen targets during which no
forces were present. The latter group allowed us to quantify subjects'
after-effects.
To produce a forcefield, the motors were programmed as a function of
hand velocity, 
, and a viscous matrix B.
The effective force acting on the subject's hand was:
f = B, where B was
either equal to B1,
or B2, where B2 = 
B1. The method used for producing such fields
has been described elsewhere (Shadmehr and Mussa-Ivaldi, 1994).
Subjects were assigned into one of six groups. All groups except
for group 1 practiced for three target sets in each of the two fields
(group 1 practiced for 3 target sets only in
B1). The difference among the remaining five
groups was the temporal distance between the practice sessions in
B1 and B2: this temporal
distance was 5 min, 30 min, 2.5 hr, 5.5 hr, or 24 hr.
Group 1 subjects returned 5 months after their initial training and
were again tested briefly in the null field (~20 targets) and then
for a target set in B1. The remaining groups
returned 1 week after completion of training in
B2 and were tested in B1. The group that had a temporal distance of 24 hr between
B1 and B2 was tested 1-3
d later on B2.
Data analysis. We sampled the manipulandum's joint angles,
joint velocities, and forces at the handle at a rate of 100 Hz and
computed hand positions and velocities. Trajectories were aligned using
a velocity threshold at the onset of movement. The performance measure
was the similarity between the hand trajectory in the force field and a
"typical" baseline trajectory (in the null field). This similarity
was defined as a correlation between two time series of hand velocity
vectors (Shadmehr and Mussa-Ivaldi, 1994). A typical baseline
trajectory for a subject was found by correlating each trajectory with
all the other trajectories for that target direction and finding the
one with the highest average correlation.
An after-effect is the trajectory that results when a
subject is expecting a force field but the robot is producing a null field. After-effects were analyzed using two indices. The first index
was a distance measure that quantified how far the hand path had
deviated from a straight line to the target. This distance was measured
300 msec into the movement. At this interval, the aftereffect is near
its maximum deviation from a straight line. The second index was a
force measure that quantified the difference between the force produced
by the subject during an after-effect and the force recorded from the
same subject for a typical movement in the baseline condition. This
variable is a time series of force vectors. Because the force fields
were always perpendicular to the direction of target, we computed the
component of the force measure that was perpendicular to the target.
The result was a time dependent scalar force variable.
Mathematical modeling. The purpose of the mathematical
modeling was to predict force and position trajectories that result as
an adaptive controller learns an internal model of the mechanical dynamics produced by the robot. The adaptive controller was modeled to
reasonably estimate the biomechanical behavior of the human arm. We
built on the ideas introduced in our previous work (Shadmehr and
Mussa-Ivaldi, 1994). The current model takes into account the passive
dynamics of the robot manipulandum as well as the passive dynamics of
the subject's arm. This allows us to predict fairly accurately the
patterns of motion and force generation in the case where we assume
that the subject has learned a specific internal model of the task.
When we position a force transducer at the interaction point between
the robot and the subject (i.e., the handle), we can write the dynamics
of the four link system in Figure 1 in terms of the following
coupled-vector differential equation:
|
(1)
|
|
(2)
|
where I and G are inertial and
coriolis/centripetal matrix functions, E is the torque field
produced by the robot's motors, i.e., the environment, F is
the force measured at the handle of the robot, C is the
controller implemented by the motor system of the subject,
q*(t) is the reference trajectory planned by the motor control system of the subject, J is the Jacobian
matrix describing the differential transformation of coordinates from endpoint to joints, q and p are column vectors
representing joint positions (e.g., q1 and
q2) of the subject and the robot (Fig. 1), and
the subscripts s and r denote subject or robot matrices of parameters,
respectively.
In the null field, (i.e., E = 0) in Eq. 1, assume that
a solution to this coupled system is q =
q*(t), i.e., the arm follows the reference trajectory
(typically a straight hand path with a Gaussian tangential velocity
profile). Let us name the controller that accomplishes this task
C = C0 in Eq. 2. When the robot
motors are producing a force field, i.e., E 
0, the
arm's motion converges back to the reference trajectory if the new
controller in Eq. 2 is C = C1 =
C0 JsTJrs
TÊ,
where Ê is an estimate of the force field environment
as learned by the controller. The internal model composed by the subject is C1 C0,
i.e., the change in the controller after some training period.
We have suggested previously that a reasonable lumped model of the
subject's biomechanical controller in the case of these targetted
movements is (Shadmehr and Mussa-Ivaldi, 1994):
|
(3)
|
where K and V are linear estimates of the
subject's joint stiffness (at posture) and viscosity matrices
(Mussa-Ivaldi et al., 1985). In this model, muscle forces produced by
the arm are dependent on a feedforward model of the subject's passive
dynamics (e.g., inertia of the arm) (Gomi and Kawato, 1996). The
controller is stabilized around the desired trajectory
q*(t) (presumably a smooth, straight-line motion
to the target) by the stiffness and viscosity of the muscles and the
spinal reflex pathways (Shadmehr et al., 1993).
We used the model of the controller in Eq. 3 coupled with the dynamics
of the manipulandum and a typical subject's passive dynamics to
simulate performance before and after adaptation. This allowed us to
predict the forces that a subject's controller should produce if it
had acquired an internal model of the forcefield. Parameter values for
the model of the subject's arm were the same as that described in our
previous report (Shadmehr and Mussa-Ivaldi, 1994). Parameter values for
the robot were determined by using a derivation of the kinetic energy
of the system in terms of the link lengths, masses, and center of
masses of the four bars of the parallelogram. Despite the 12 unknowns,
the mass parameters combine in the inertia matrix and reduce to 3 composite parameters (Slotine and Li, 1991). These parameters (along
with friction and viscous parameters, which are comparatively small and
were not used here), were estimated using a system identification
technique. We estimated the inertia matrix of the robot to be:
with p1 and
p2 as Robot's joint angles (Fig. 1),
a1 = 0.46, a2 = 0.34, and
a3 = 0.094 kg/m2, and link lengths
of 0.460 and 0.344 m for the upper arm and forearm of the robot. We
estimated the coriolis matrix of the robot to be:
The desired trajectory in the simulations was assumed to be
minimum jerk (Flash and Hogan, 1985) with a period of 0.5 sec.
RESULTS
We report on experiments in which subjects learned to make
reaching movements in two distinct dynamic environments. We find that
the ability of subjects to learn movements in a second environment, and
the ability to recall the skill acquired by practicing in the first
environment, are influenced by the temporal distance between learning
the first and second environments.
Learning control of a novel dynamic system
A typical subject's hand trajectory in the null field is shown in
Figure 2A. Without the disturbing
forces, subjects could readily make rapid and accurate movements to the
targets. As previously noted (Flash, 1987), these movements were
approximately in a straight line with a symmetric tangential velocity
profile. However, once a field was introduced, movements became highly
distorted. An example of the imposed force field and the resulting
movements are shown in Figure 2, B and C. The
force field (named B1 in Materials and Methods)
pushed the hand in a direction perpendicular to the direction of the
target. The magnitude of the imposed force was a linear, increasing
function of hand velocity. The resulting motion of the hand had a
characteristic "hooking" pattern. In previous simulations, we
observed similar patterns of motion when we assumed a biomechanical
controller of the form detailed in Eq. 3 (Shadmehr and Mussa-Ivaldi,
1994). Note that our model controller produces a pattern of torques
based on expectations of the dynamics of the task and is stabilized by
the stiffness of the arm about the desired trajectory. This had led us
to suggest that the hooks are not indicative of a second, corrective
movement, but are attributable to the interaction between the stiffness
and inertial characteristics of the subject's arm and the imposed
force field.
Fig. 2.
A, Hand path of a typical subject
in the null field (the points in all hand paths are 10 msec apart). B, An example of a force field produced by
the robot. The field is a linear function of hand velocity, and the
x- and y-coordinates refer to that of
Figure 1. C, Hand path of an untrained subject in the
field. D, Hand path after 300 movements in the field.
The trajectory in the field converges to the trajectory observed in the
null field. E, Forces produced by a typical trained
subject to counter the effect of the force field as a function of hand
position for each movement. These forces are the projection of the
forces measured at the interaction point between the subject and robot
onto a direction perpendicular to the direction of target.
F, While training in the field, random targets are
presented with null field conditions. The results are
after-effects.
[View Larger Version of this Image (20K GIF file)]
With practice, the hooks diminish and the hand trajectory in the field
(Fig. 2D) becomes similar to that observed in the
null field (Fig. 2A). Force measured at the
interaction point of the robot and the subject suggest that, with
practice, subjects learn to produce forces perpendicular to the
direction of the target as the hand moves toward the target (Fig.
2E). These forces essentially cancel the imposed
force field, allowing the hand to move along the desired trajectory. In
principle, two biomechanical mechanisms may be responsible for this
adaptation. By increasing stiffness of the arm, i.e., global muscular
co-contraction, the subject can cancel most perturbing forces
regardless of their direction. Alternatively, the subject may learn to
activate muscles so that in addition to the forces necessary to move
the hand toward the target, perpendicular forces are generated to
compensate for the expected dynamics of the force field. Only in the
later scenario would we expect that a sudden removal of the force field
should result in after-effects. Typical after-effects are shown in
Figure 2F. This is an indication that the subject is
learning to command a novel pattern of muscle forces in order to reach
a target location. In the language of control theory, the subject is
learning an IM that predicts a pattern of forces for a desired
trajectory.
The after-effects give us a window through which we can examine the
content of the IM. Normally, when moving in a null field, the amount of
force that is produced by the subject perpendicular to the direction of
target is rather small (Fig. 3A; this amount is nonzero because the inertia of the manipulandum and the arm is not
isotropic). To make a straight-line movement in the field, the subject
needs to produce significantly larger perpendicular forces. An example
of forces produced by the trained subject is shown in Figure
3B. If this change in force production is achieved through
learning of an internal model, then through simulation we can predict
the pattern of forces that will result if we unexpectedly remove the
force field. When the biomechanical controller has incorporated an IM
of the field of Figure 2B, the change in the output
of the system (force at the interaction point as compared to before
adaptation conditions) is predicted to be a distinct pattern of
counter-clockwise forces (Fig. 3C). These forces will be
largest for targets at 90° and 270° and smallest for targets at
0° and 180°. The reason for this nonuniform pattern is the anisotropic behavior of the stiffness of the arm (Mussa-Ivaldi et al.,
1985; Shadmehr et al., 1993). This stiffness has the largest influence
on stabilizing the hand on movements to 0° and 180° and the
smallest influence on movement to 90° and 270°. Indeed, we found
that after practice (300 targets), the motor output of subjects (e.g.,
Fig. 3D-F) had changed by roughly the
same pattern and magnitude as our simulation had predicted. This
suggests that subjects were incorporating an IM of the novel dynamics
in programming their motor output. We note, however, that the
simulation was in agreement with the recorded forces only for the
initial 200-250 msec into the movement. Beyond this, it is possible
that long-loop reflexes (which are not modeled) or voluntary action
begins to significantly influence the pattern of force generation.
Fig. 3.
The component of the interaction force
perpendicular to the direction of motion plotted as a function of time
along straight-line paths to the targets. A, Interaction
forces while moving in a null field. B, Interaction
forces of a trained subject while moving in a field. C,
Results for a simulation in which the controller of Eq. 3 had learned
an IM of the force field and, unexpectedly, the field was removed,
i.e., force predicted for the after-effects in the case that the
controller had learned a perfect model of the field. Forces are plotted
for the first 250 msec of movement. D-F, Forces
recorded from three typical subjects during their after-effects (first
250 msec of movement). The motor output of subjects changed by roughly
the same pattern and magnitude as the simulation had predicted. The
vectors in all paths are 10 msec apart.
[View Larger Version of this Image (32K GIF file)]
Long-term motor memories
Learning of an IM allows the subject to move his/her hand along a
desired trajectory. We assumed that the desired trajectory for each
subject was their pattern of motion in the null field (baseline
trajectories, as in Fig. 2A). Our performance index was a correlation between a subject's typical movement before imposition of the field with movements in the field (Shadmehr and
Mussa-Ivaldi, 1994). Figure 4A shows
the change in this index as a function of practice in all subjects. It
is apparent that the majority of the improvement is occurring in the
first 150 movements (the first target set).
Fig. 4.
Performance during initial training in a force
field and subsequent tests of recall. Performance index is a
correlation between the hand trajectory in the force field and the hand
trajectory in the null field (baseline trajectories, as in Fig.
2A). A, Mean performance ± 95% confidence intervals for all subjects (n = 60). For each subject, groups of eight consecutive movements are binned together (there were 8 different directions of movement, and target directions were presented in random order). B, Initial
performance in a field and performance 24 hr later. All lines are mean
performance ± 95% confidence intervals. Thin
line, Performance of naive subjects (n = 18) in field B1. Thick black
line, Performance of a subset of these subjects
(n = 8) in a novel field,
B2, measured 24 hr after training in
B1. Thick gray line,
Performance of the remaining subjects at 24 hr on field
B1. C, Summary performance
scores ± 95% confidence intervals for the two groups of
subjects. Gray line represents subjects that were tested
on the field in which they were trained. Black line
represents subjects that at 24 hr were tested on a novel field.
[View Larger Version of this Image (25K GIF file)]
Does practice lead to long-term storage of the acquired internal
model? We have shown previously that there is a significant improvement
in the performance index when subjects are tested 24 hr after they are
trained in a given field (Brashers-Krug et al., 1995b). Here we trained
subjects on field B1 (n = 18)
and had them return at 24 hr to be tested on either the same field that
they were trained in previously (control group, n = 10)
or on a novel field B2 (n = 8).
Subjects in the control group also returned 5 months later and were
tested on the same field in which they were trained. Results are shown
in Figure 4, B and C: performance in the trained
field was significantly higher when probed at 24 hr (Fig.
4B; F(1,9) = 17.99, p < 0.005) and continued to be significantly higher at
5 months after the initial practice (p < 0.005;
Fig. 4C). In comparison, performances of subjects that
trained on B1 and were tested 24 hr later on
B2 were not significantly different than the
levels achieved by the naive subjects on B2
(F(1,7) = 3.2, p > 0.1). This
suggests that the improvement in performance of the control group was
not attributable to general familiarity with the experiment, but
learning of an IM specific to the presented force field. This learning
resulted in a long-term memory of the IM.
To determine whether subjects who practiced in two different fields
(B1 and B2, training
sessions for the fields separated by 24 hr) formed long-term
representations of both fields, we had subjects (n = 6)
tested on field B1 at an interval of 2 weeks and
field B2 at 3 weeks beyond completion of
training. With respect to the performance during training, there was a
significant improvement in performance during the recall sessions: mean
performance index ± 95% confidence interval = 0.89 ± 0.007 vs 0.92 ± 0.007 for field B1 during
training and recall (F(1,5) = 16.81 p < 0.01), rejecting the null hypothesis that there is
no improvement in performance during recall of
B1 as compared to initial training, and
0.87 ± 0.008 versus 0.900 ± 0.008 for field
B2 during training and recall
(F(1,5) = 40.63 p < 0.002),
rejecting the null hypothesis that there is no improvement in
performance during recall of B2 as compared to
initial training. Therefore, when the training sessions were separated
by 24 hr, subjects retained the IMs for both fields
B1 and B2.
Time course of consolidation
The idea that memories undergo a process of consolidation relies
strongly on the observation that there are periods after acquisition of
information during which the representation of the recently acquired
material is fragile. With time, the representation becomes less
susceptible to an intervention. For example, post-training treatments
such as electric shocks (Squire et al., 1975), removal of key
anatomical sites (Kim et al., 1995), or protein synthesis inhibition
(Tully et al., 1994) retard this progression and often result in loss
of the recently acquired information (Squire et al., 1981). These
interventions, however, have little effect on recall once a window of
time has passed since acquisition.
We tested for the stability of the acquired IM of field
B1 as a function of temporal distance to
training in field B2. Subjects trained in field
B1, and then trained in
B2 at 5 min (n = 9), 30 min
(n = 6), 2.5 hr (n = 7), 5.5 hr
(n = 10), or 24 hr (n = 8). They then
returned 1 week later and were tested in field
B1. Figure 5A shows
the change in performance in B1 during the
recall session as compared to the initial learning for two groups: the group that learned B2 5 min after
B1, and the group that learned B2 5.5 hr after B1.
Whereas the 5 min group shows no recall of B1
(mean performance not significantly different in recall vs initial
learning, paired t test, p > 0.4), the 5.5 hr group shows significant recall (paired t test,
p < 0.02). The data for all groups are summarized in
Figure 5B. There is a significant effect of time on
retention of B1 (F(49,44) = 2.46, p < 0.05). If B2 is
practiced 5-30 min after B1, we find no
evidence for recall of B1. Recall becomes
significant at 5.5 hr but approaches the level of recall observed in
the control group only at 24 hr.
Fig. 5.
Performance during the test of recall for
B1 as a function of temporal distance
between learning of B1 and
B2. B1 was tested for recall 1 week after B1 and
B2 were learned. A, Mean ± SE improvement in performance for two groups of subjects.
Thin line is for the group (n = 9)
that practiced in B2 at 5 min after
completion of practice in B1. Thick
line is for the group (n = 10) that
practiced in B2 at 5.5 hr after
B1. B, The ability to recall
B1 is significantly dependent on temporal
distance between B1 and
B2. Each bar is the mean ± 95%
confidence interval of change in performance as measured for a target
set during the recall test versus during initial practice.
[View Larger Version of this Image (30K GIF file)]
The time interval at which learning of field B2
does not impair recall of B1 is similar to what
we had observed previously in a different group of 70 subjects
(Brashers-Krug et al., 1996). In our previous work, this interval was
estimated at 4 hr. Here, we find significant recall at 5.5 hr. There
are two differences in the protocol of the current study and the
previous work: (1) in the current setup, the subjects practiced 3 times
longer on field B1 before being exposed to
B2, and (2) in the current study recall was
measured 1 week after original training rather than at 24 hr (as in our
previous work). The increased training on B1 was
chosen in the current protocol to ensure that the performance plateaued
before B2 was introduced (Fig.
4A). Recall was tested at 1 week rather than at 24 hr
to ensure against any anterograde interfering effects that might be
present after learning of B2. This testing of
recall at 1 week is important because of a phenomenon called "release
from inhibition": it has been observed that in learning associations
between pairs of words, learning to associate A with B followed by
association of A with C leads to poor recall of A-B when tested at a
short interval (hours after training) but leads to good recall at
longer intervals (1 week; as compared to a group that only learned
A-B) (Koppenaal, 1963). Therefore, it is possible that our previous
observation regarding the poor recall of field
B1 (Brashers-Krug et al., 1996) might be
attributable to a lingering anterograde interference from
B2 (see Fig. 8). The current study was designed
with this concern in mind. The results of Figure 5 show that recall of
B1, as measured a week after original learning,
is significantly influenced by the time at which
B2 was learned.
Fig. 8.
Performance of subjects in field
B2 as a function of time since learning
B1. A, When
B2 is introduced 5 min after completion of
training in B1, performance is worse than
that recorded from B1. Plotted are mean ± SE of correlations. B, Performance in
B2 is significantly dependent on temporal
distance between B1 and B2. Each bar is the mean ± 95%
confidence interval of change in performance as measured for the
initial target set in B1 and
B2.
[View Larger Version of this Image (36K GIF file)]
Although the time of learning of B2
influences the recall of B1, the link to
consolidation would be strengthened if there was evidence that learning
of B2 within close temporal proximity of
learning B1 results in an unlearning of the IM
for B1. The computational model for this kind of
forgetting has termed the phenomenon "catastrophic interference"
(Sutton, 1986). In this computational model of memory, forgetting
occurs because the memories that represent the internal model of field
B1 (associating a desired trajectory to a
specific pattern of muscle torques) are used for learning
B2 (Shadmehr et al., 1995). Because the two
fields are anticorrelated, learning of B2 would
lead to a loss of memory for B1 (e.g., massive
changes in the weights of the network or patterns of activity). A
prediction of this computational model is that at close temporal
proximity, learning of B2 takes place with an
instantiated IM of B1, rather than a "tabula
rasa."
The after-effects give us a unique window into the contents of the IM
being used to learn a field. For example, in a naive subject that is
just beginning a target set in B2, there are no after-effects. It is with practice that after-effects develop. We
quantified the size of an after-effect by measuring the distance that
the trajectory deviated from a straight-line path to the target. This
distance was measured at 300 msec into the movement. The sign of this
vector was positive if the after-effect was a counter-clockwise
deviation from the straight line (appropriate for an IM of
B1, as in Fig. 2F), and
negative if it was a clockwise deviation. Figure 6 shows
the progression of after-effect development for a group of naive
subjects on field B2 (the control group). This
figure also shows the development of after-effects for the group of
subjects that practiced in B2 at 5 min after
completion of practice in B1. In the 5 min
group, subjects begin learning B2 with an IM
appropriate for B1. The rates of change in the
after-effects are not different among the groups: the top four lines in
Figure 6 are approximately parallel. The main difference between the four groups is the starting point. With temporal distance, the starting
point gradually shifts toward that of the naives so, at 5.5 hr, there
are no significant after-effects as learning of
B2 initiates. In other words, whereas in the 5 min group learning of B2 starts with an IM of
B1, in the 5.5 hr group, the IM is close to a
"tabula rasa."
Fig. 6.
Size and direction of after-effects as subjects
learn field B2 at different time intervals
after practicing in B1. Size is determined
as the distance from a straight line (from the previous to the next
target) at 300 msec into the movement. Direction is positive for an
after-effect appropriate for field B1 (i.e.,
counter-clockwise, as in Fig. 2) and negative for an after-effect
appropriate for field B2. Plotted are the
means and 95% confidence intervals. Each point represents the average
after-effect for a group of subjects at a given movement number (bin
size is 4). Because the size of after-effects depends on direction of
target (e.g., Fig. 2F), the change for a
given curve is not expected to be monotonic. However, the sequence of
targets for all subjects is the same. Therefore, after-effects at a
movement number may be directly compared among the different groups.
The figure shows that at 5 min after learning
B1, subjects begin learning
B2 with after-effects that are in the
direction of B1. Control subjects who never
learned B1 begin learning
B2 with no after-effects, i.e., unbiased.
With temporal distance between training sessions, initial after-effects begin to resemble those of the control group.
[View Larger Version of this Image (21K GIF file)]
Another way to quantify the contents of the IM that the subject is
using to learn a given field is to measure the interaction forces
between the subject and the robot. As Figure 3 demonstrates, in a given
subject one can compare the interaction forces recorded during baseline
movements (i.e., in a null field before introduction of the forces)
with those during after-effects. The difference is an estimate of the
change in the motor output of the subject, which is presumably
attributable to adaptation. In our model of the biomechanical control
system (Eq. 3), this variable is an estimate of the output of the
subject's IM. As we noted earlier, our measure of this change will
very likely be an underestimation of the true learning because the
stiffness properties of the arm will reduce the size of the
after-effects. However, one can measure this variable and, assuming
that arm stiffness is roughly equal among the subjects of all the
different groups (Mussa-Ivaldi et al., 1985; Shadmehr et al., 1993),
compare its time course.
We computed an estimate of the output of the IM for the subjects in all
groups during the after-effects of the first 80 targets in fields
B1 and B2. The sign of
the output was set positive if the force vector was pointing
counter-clockwise from a straight line to the target (appropriate for
an IM of field B1, as in Fig. 3D-F) and negative if it was clockwise
(appropriate for B2). The result is shown in
Figure 7. Lines 1 and 7 show the output of the IM for
the naive subjects in fields B1 and
B2, respectively. These lines give us an
estimate of what an unbiased IM will output after it has been trained
with movements to 80 targets. The remaining lines are all measured
while subjects were learning B2 and are differentiated based on the temporal distance to learning
B1. In the 5 min group, the IM used to learn
B2 is still mainly composed of
B1. This evidence supports our contention that
learning B2 in close temporal proximity to
B1 takes place with an instantiated IM of
B1. With temporal distance, subjects learn
B2 with an IM that can better estimate
B2 after a given number of movements. This
predicts that performance should be better in B2
as a function of temporal distance to B1.
Results shown in Figure 8 demonstrate that this is
indeed the case: performance in B2 is
significantly worse than in B1 when temporal
distance is 5 min (Fig. 8A). With time, the ability
to learn the second field gradually improves (Fig.
8B; F(39,35) = 4.155, p < 0.01).
Fig. 7.
A measure of forces recorded at the interaction
point between subjects and robot as a function of time into an
after-effect. Each line is an average change in the interaction force
during after-effects (n = 5) to targets at 270°
and 225° for the first 80 movements in a given force field with
respect to those recorded from the same group of subjects in the
baseline conditions. This representation of the interaction force is
the same as the measure shown in Figure
3D-F, with the difference that time is
explicitly represented and the sign of the force is positive for a
counter-clockwise vector and negative otherwise. Line 1
represents mean ± 95% confidence interval of the force produced
by naive subjects that learned field B1.
Line 7 represents mean ± 95% confidence interval
for naive subjects that learned field B2.
Lines 2-6 are the forces produced by
subjects that were learning B2 at 5 min, 30 min, 2.5 hr, 5.5 hr, or 24 hr after learning
B1, respectively.
[View Larger Version of this Image (26K GIF file)]
The data on after-effect development (Fig. 6) suggest that the
rate of learning an IM of B2 was
similar across the different groups; the difference was the initial
bias from which the learning began: in computational terms, a first
approximation would suggest that the IM used to learn
B2 had weights that were strongly initialized toward those appropriate for representation of
B1. With temporal distance, learning of
B2 began with weights less biased toward B1, approaching the "tabula rasa" of the
naives in the control group. In other words, with time, there was a
fading of an aspect of representation of B1.
However, we know that long-term memory of B1 did
not fade with time (at least within a few months; Fig. 4C),
suggesting that this fading component is not related to long-term memory of B1. In Figure 9 we
combined the data on retrograde and anterograde effects of learning the
two fields and show the two hypothesized aspects of memory for learning
an IM for B1. The fading component has data
points that are biases of the IM used to learn field
B2. The rising component has data points that
are the memories of B1 that were retained after
learning of B2. This represents a hypothesized
time course for formation of long-term memory of
B1.
Fig. 9.
The data from Figures 5 and 8 have been combined
to produce two hypothetical stages in formation of long-term motor
memory. The data points that constitute the decaying stage are the
amount of anterograde interference that was recorded (as a percentage of that recorded at 5 min) as subjects attempted learning of a second
field at different times. The data points that constitute the rising
curve are a measure of memory retained from the first field after
learning of a second field at different times.
[View Larger Version of this Image (14K GIF file)]
The fading component of the memory of recently learned
B1 is presumably the reason why subjects at 5.5 hr can readily learn B2. To determine whether
there is a relationship between this fading component and the
consolidation process for formation of long-term memory of
B1, we performed one last experiment. We
recruited a new group of subjects (n = 10) and trained
them in B1 (3 target sets) on day 1 and had them
return on day 2. On day 2, subjects were given a target set in
B1, and then they practiced in three target sets
in B2. When performance in
B2 was compared to performance in
B1 (as recorded 24 hr earlier), we found a
significant reduction in performance (F(1,9) = 34.85, p < 0.001, a comparison of performance in the
first target set). Furthermore, the mean change in performance, 0.058, was not significantly different than the change in performance observed when subjects' only exposure to B1 was
5 min before B2 (the mean change for this group
was 0.0725, as shown in Fig. 8). Therefore, the ability to learn a
new field was not related to when B1 was
originally learned but, rather, when it was last practiced.
Although learning of B2 is affected by the
recently instantiated B1, if
B1 was originally learned 24 hr ago, then its
long-term memory should not be affected by learning of
B2. We tested for recall of
B1 on day 3. The subjects showed significantly
improved performance compared to initial training
(F(1,9) = 8.757, p < 0.02).
Mean improvement in this group (+0.0316) was not significantly different than the improvement that we had seen in our control subjects
(+0.034, shown in Fig. 5). The long-term memory of
B1 was intact even though
B2 was learned immediately after
B1 was performed. This suggests a functional
independence for the two hypothesized stages of motor memory.
DISCUSSION
The ability of the central nervous system to learn and store
multiple and conflicting visuomotor maps has been demonstrated in both
monkeys and man (Flook and McGonigle, 1977; McGonigle and Flook, 1978;
Welch et al., 1993; Cunningham and Welch, 1994). For example, it has
been shown that the CNS can learn and retain two conflicting visuomotor
maps associated with left and right displacing prisms (McGonigle and
Flook, 1978) and two different gains associated with the
vestibulo-ocular reflex (Baker et al., 1987; Shelhamer et al., 1992;
Tiliket et al., 1993). Here we demonstrated that two conflicting motor
skills (what we have termed internal models) may also be learned and
retained, but only if the training sessions in the two tasks are
separated by a critical time interval of ~4-5 hr. This time interval
is in agreement with the data on the prism studies: in monkeys,
adaptation was obtained only when alternate maps were presented far
apart in time (24 hr) (Flook and McGonigle, 1977). In humans, after a
single training session with a given prism, learning of a second
visuomotor map (with a second prism) at close temporal proximity (10 sec) was significantly inhibited compared to naives, and a test of
recall with the first prism at 3 d later showed no evidence of
improvement (McGonigle and Flook, 1978).
In this study, we suggested that there is a critical time interval
required for learning and retention of two distinct IMs. Our results
show that recall of IM1 is affected by the temporal distance to learning of IM2. However, recall is the
culmination of a chain of processes (e.g., perception of the task,
integration of proprioceptive information, activation of motor memory,
and action), and poor performance in a test of recall may not imply that the motor memory component has been affected (Bower et al., 1994);
there is evidence that retrograde amnesia is sometimes not the result
of consolidation failure (Miller and Marlin, 1984). This argument is
based on two reports: (1) when reminder trials involving apparatus or
other cues were presented during the retention interval after
administration of an amnesic agent, retrograde amnesia was reduced
(Lewis et al., 1968; Quartermain et al., 1970), and (2) performance
improved when the experimenter provided cues regarding the correct
response during test of recall (Postman and Stark, 1969; Bower and
Mann, 1992).
This line of thinking suggests that poor recall may be attributable to
inaccessibility of stored information, rather than its loss, and that
with time or appropriate cues, information that once was inaccessible
might become available (Koppenaal, 1963; Squire et al., 1981). Although
we cannot rule out this possibility, there are four pieces of data from
our study that argue for the idea that representation of a motor skill
does undergo profound functional changes within a short window of time
after acquisition.
(1) If subjects are presented with B2
shortly after learning B1, they learn
B2 with an IM appropriate for
B1. The contents of the IM being used to learn
B2 (as inferred from the after-effects) suggests
an unlearning of B1. With temporal distance, the
learning of B2 begins with an IM that approaches
the tabula rasa of the naives.
(2) Recall of IM1 as measured 1 week after original
learning shows a significant dependence on when IM2 was
acquired. This period of 7 d was chosen because it is
significantly longer (~7 times) than the interval at which we
detected an anterograde interference from IM1 onto
IM2.
(3) Making movements in a force field provides continuous haptic,
proprioceptive, and visual feedback to the subject regarding the nature
of the forces present in the field. Yet when fields are learned in
close temporal proximity, there is no evidence for recall as measured
in a target set that included 192 movements.
(4) Recall of IM1 is not affected when it was learned
24 hr before learning IM2, even though subjects performed
movements in B1 moments before learning
B2.
Taken as a whole, the above evidence, in our view, argues for a
distinct change in the state of resistance of motor memory within a few
hours after acquisition. Because the vulnerability to an intervention
and the ability to learn a second task depend on time since
acquisition, it is possible that the neuronal basis of motor memory
changes after acquisition.
A number of mechanisms have been proposed to underlie memory
formation in the central nervous system. These include long-lasting changes in synaptic efficacy (Bliss and Collingridge, 1993) and reverberation of activity in a collection of excitatory neurons (Hebb,
1949; Zipser et al., 1993). Hebb (1949) was the first to suggest a
neural basis for the time dependent success of retrograde amnesic
agents. In his framework, memories are stored for a period of time in a
labile form of neuronal firing patterns generated through reverberating
circuits. The firing pattern persists after completion of practice and
leads to a more gradual development of synaptic plasticity, mediating
long-term memory. A prominent example of synaptic plasticity is
long-term potentiation (LTP). It has been shown that after inducing
LTP, certain low-frequency stimuli can depotentiate the synapse (Fujii
et al., 1991), effectively reducing the synapse's efficacy to near
baseline levels. These stimuli, however, are only effective if they are
given within a small time window after potentiation of the synapse: 20 min after induction of LTP, the low-frequency stimuli depotentiate the
synapse by 70%, whereas at 100 min, the depotentiation is only at
30%. There is a wealth of evidence for LTP (Asanuma and Keller, 1991;
Kimura et al., 1994) and LTD (Castro-Alamancos et al., 1995) in the
motor areas of the cortex and the cerebellum.
A first-approximation model of learning might begin with Hebb's ideas
regarding the initial representation of memory as a labile form of
neuronal firing patterns, and synaptic plasticity as the means for
representing long-term memory. These two types of representations may
form the neuronal basis of the two hypothesized stages of motor memory
in Figure 9; according to this model, practice leads to recruitment of
activity in neuronal circuits and establishes a reverbrating pattern as
the training comes to an end. This pattern gradually decays, but it
serves as the teacher for a slower but more resistant form of memory
storage (Alvarez and Squire, 1994), e.g., synaptic plasticity. We would
expect that the initial stage to have a finite life and decay after
completion of motor practice in task 1. If task 2 is attempted while
the neuronal firing pattern is present, there will be interference;
learning of task 2 will begin with a pattern appropriate for task 1, and performance will be impaired compared to naives. If time is allowed
to pass after learning of task 1, changes in synaptic efficacy gain
stability and serve as a more permanent representation of the motor
memory for task 1. It is important to note that a model of memory that relies only on synaptic plasticity (e.g., LTP) would have trouble explaining our data: because the changes induced in synaptic efficacy are most fragile soon after they are established, it should be easy to
learn IM2 soon after learning IM1. However, we
find that the opposite is true. The utility of a two-stage learning
system has been elaborated recently in a formal computational model
(McClelland et al., 1995).
Our last experiment shed some light on the role of the hypothesized
initial stage. We noted that learning of IM2 was impaired if movements were performed in B1 just before
B2, i.e., this impairment was just as severe for
the case where IM1 was just acquired versus the case where
IM1 was acquired 24 hr ago but was just recalled. It seems
likely, therefore, that the hypothetical initial stage is not merely a
gateway to long-term memory but also, at least in part, a temporary
storage area for items of information, whether new or old, for use in
the near-term. This is the description that has been used to define
"working memory" (Fuster, 1995). A major function of this kind of
memory is to hold information and update current information on a
real-time basis (Goldman-Rakic, 1994).
It is possible that the neuronal basis of the hypothetical initial
stage is mediated by regions distinct from that of the second stage,
i.e., there may be a time-limited role associated with certain regions
of the brain in maintaining motor memory (Mishkin et al., 1984). It has
been argued that brain regions active during acquisition of motor
memory are not necessarily the same as regions that will eventually
store the memory (Pavlides et al., 1993). For example, although the
cortico-cortical projections from the somatosensory to the motor cortex
play an important role in learning new motor skills, they may not be
required for execution of existing motor skills (Aizawa et al., 1991).
In humans, there is now mounting evidence from functional imaging
studies of motor learning that indicate distinct motor areas are active
during initial learning versus subsequent recall trials of a motor task (Grafton et al., 1994; Karni et al., 1995; Kawashima et al., 1995). It
will be important to ask whether changes in centers of neuronal activity correlate with functional changes in the stability of the
recently acquired memory (Brashers-Krug et al., 1995a; Shadmehr and
Holcomb, 1996). However, in all likelihood, classification of motor
memory into only two discrete phases will turn out to be naive, because
it has been argued that formation of stable memory is analogous to a
developmental process in which extracellular signals initiate cascades
of events, gradually modifying neuronal representation on a time scale
of seconds to years (Dudai, 1989).
FOOTNOTES
Received July 23, 1996; revised Oct. 3, 1996; accepted Oct. 8, 1996.
This work was funded in part by grants from the Office of Naval
Research and the Whitaker Foundation to R.S. This work has been greatly
enriched because of our interactions with Dr. Emilio Bizzi. Kurt
Thoroughman, Maurice Smith, and Kasra Akhavan-Toyserkani provided
technical support of the Hopkins manipulandum.
Correspondence should be addressed to Dr. Reza Shadmehr, Traylor 419, The Johns Hopkins Univeristy School of Medicine, Balimore, MD
21205-2195.
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