J Neurophysiol 115: 2014 –2020, 2016.
First published February 10, 2016; doi:10.1152/jn.00938.2015.
Locomotor sequence learning in visually guided walking
Julia T. Choi,1,2 Peter Jensen,2 and Jens Bo Nielsen2
1
Department of Kinesiology, University of Massachusetts, Amherst, Massachusetts; and 2Neural Control of Movement
Research Group, Department of Neuroscience and Pharmacology and Department of Nutrition, Exercise and Sport,
University of Copenhagen, Copenhagen, Denmark
Submitted 8 October 2015; accepted in final form 3 February 2016
human; learning; locomotion; vision; walking
is critical to our daily
activities. We normally do not consciously think about walking
unless we encounter novel situations where we have to make
voluntary gait modifications (e.g., hiking across stepping
stones). On the other hand, voluntary control of stepping
patterns can become more automatic with practice (e.g., line
dancing). Locomotor skills can be learned via visual cues from
the environment or proprioceptive cues from the limbs during
obstacle avoidance tasks (Erni and Dietz 2001; Lajoie et al.
2012). Moreover, there is some evidence that visual cue training could improve gait patterns that transfer to walking without
cues in people with Parkinson’s disease (Spaulding et al.
2013).
The classic serial reaction-time (SRT) task, first introduced
by Nissen and Bullemer (1987), may be used to assess to what
an extent a motor task has become automatic. The SRT task
requires subjects to rapidly respond to different spatial cues by
pressing corresponding buttons as fast and accurately as possible. With practice, subjects demonstrate learning by perform-
THE AUTOMATIC CONTROL OF WALKING
Address for reprint requests and other correspondence: J. Choi, Dept. of
Kinesiology, Univ. of Massachusetts Amherst, 30 Eastman Lane, Amherst MA
01003 (e-mail: [email protected]).
2014
ing faster on the repeating sequence than on random sequences.
This difference can only be accounted for by sequence-specific
learning, which can occur with or without explicit knowledge
(Cohen et al. 1990; Curran and Keele 1993; Nissen and
Bullemer 1987).
The primary objective of this study was to test whether
spatial sequence learning could be integrated with a highly
automatic task such as walking. We challenged walking control by presenting stepping targets of varying spatial complexities (e.g., random vs. ordered). To our knowledge, this is the
first study to investigate sequence learning in the lower limbs
during walking. Our second objective was to determine how
age (i.e., healthy young adults vs. children) and biomechanical
factors (i.e., walking speed) affect the rate and magnitude of
locomotor sequence learning. Cortical control of gait is not
fully developed in children under the age of 10 yr (Petersen et
al. 2010), so we therefore predicted that visuomotor control of
walking might be more difficult in younger children compared
with adults. Furthermore, biomechanical constraints may supersede the goal of precision, in which case we would see a
drop in performance as we challenge subjects to walk at faster
speeds.
METHODS
Subjects. Twenty young adults (age 24 ⫾ 5 yr) and 18 children
(mean age 11 ⫾ 3 yr, range 6 –15 yr) with no known neurological
disorders participated in this study. The study was approved by the
local ethics committee (protocol #H-A-2008-029). All methods conformed to the Declaration of Helsinki. All subjects gave written
informed consent prior to participation.
Motion capture. Reflective markers were placed bilaterally on the
toe (5th metatarsal head, 5MT), ankle (lateral malleolus), knee (lateral
joint space), hip (greater trochanter), pelvis (iliac crest), and shoulder
(acromion process) to capture walking kinematics (Fig. 1A). A sixcamera ProReflex motion capture system (Qualisys, Gothenburg,
Sweden) was used to collect three-dimensional (3-D) marker data at
100 Hz. Identification of markers was performed using the AIM
module in the Qualisys Track Manager software.
Experimental setup and visual feedback. We created a visuomotor
walking task where subjects must change their step length to hit visual
targets while walking on a treadmill (Fig. 1A). Stepping targets and
foot position were projected on a screen in front of the treadmill. The
projector (Toshiba TDP-T 355) was mounted on the ceiling. The
projection distance was 148 cm away from the screen, which resulted
in a screen size of 125 cm high by 167 cm wide. This setup allowed
us to manipulate the sequence of target locations step by step and to
provide real-time visual feedback to the subject during the experiment. A custom-made computer program controlled the position of
the targets while the QTM real-time server provided the position of
the foot (i.e., 5MT marker) during the walking task.
Targets were represented by 16 ⫻ 16-cm open squares on the
visual display (Fig. 1B). The swing leg’s foot position was displayed
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Choi JT, Jensen P, Nielsen JB. Locomotor sequence learning in
visually guided walking. J Neurophysiol 115: 2014 –2020, 2016. First
published February 10, 2016; doi:10.1152/jn.00938.2015.—Voluntary limb modifications must be integrated with basic walking patterns
during visually guided walking. In this study we tested whether
voluntary gait modifications can become more automatic with practice. We challenged walking control by presenting visual stepping
targets that instructed subjects to modify step length from one trial to
the next. Our sequence learning paradigm is derived from the serial
reaction-time (SRT) task that has been used in upper limb studies.
Both random and ordered sequences of step lengths were used to
measure sequence-specific and sequence-nonspecific learning during
walking. In addition, we determined how age (i.e., healthy young
adults vs. children) and biomechanical factors (i.e., walking speed)
affected the rate and magnitude of locomotor sequence learning. The
results showed that healthy young adults (age 24 ⫾ 5 yr, n ⫽ 20)
could learn a specific sequence of step lengths over 300 training steps.
Younger children (age 6 –10 yr, n ⫽ 8) had lower baseline performance, but their magnitude and rate of sequence learning were the
same compared with those of older children (11–16 yr, n ⫽ 10) and
healthy adults. In addition, learning capacity may be more limited at
faster walking speeds. To our knowledge, this is the first study to
demonstrate that spatial sequence learning can be integrated with a
highly automatic task such as walking. These findings suggest that
adults and children use implicit knowledge about the sequence to plan
and execute leg movement during visually guided walking.
LOCOMOTOR SEQUENCE LEARNING
2015
as an 8-cm-diameter circle. Stepping targets for the left foot and right
foot appeared on the left and right of midline, respectively. The targets
first appeared near the top of the screen and moved down at a speed
corresponding to the treadmill speed (arrow). The subject saw both the
current target (red square) and the next target (gray square). The
vertical distance between the two targets indicated the required step
length (Fig. 1B, 1). The position of the swing leg appears at ipsilateral
toe off (Fig. 1B, 2). Subjects were instructed to step on the targets as
accurately as possible. A successful hit was one where the center of
the foot (circle) lay within 8 cm of the center of the target (square)
after heel strike (Fig. 1B, 3). The current target turned white on each
successful hit. The score was updated and displayed on the top right
corner of the screen. The current target and foot position disappeared
after ipsilateral heel strike (Fig. 1B, 4). The next target turned red and
the foot position of the contralateral leg appeared. See Supplementary
Video S1 for a replay of the visual display during the visuomotor task.
(Supplemental material for this article is available online at the
Journal of Neurophysiology website.)
Learning paradigm. Each sequence was composed of three different step lengths (e.g., short, medium, long). The step lengths were
adjusted proportionally to each subject’s leg length. The medium step
length was defined as two-thirds of leg length. The short step length
was set at 80% of medium step length, and the long step length at
120% of medium step length. The average leg length was 0.85 ⫾ 0.06
m in adults and 0.77 ⫾ 0.12 m in children who participated in this
study.
Two speed conditions were tested in the adult subjects. Ten adult
subjects walked at slower treadmill speeds (2.3 ⫾ 0.16 km/h), and
another 10 adult subjects walked at faster treadmill speeds (3.2 ⫾ 0.16
km/h). All children were tested at slower treadmill speeds (1.8 ⫾ 0.32
km/h). The treadmill speeds for individuals were set to match cadence
between adults and children.
Subjects performed a total of 7 blocks that each consisted of 100
steps (Fig. 1C). In random blocks, the targets appeared randomly at
locations that required different step lengths (i.e., short, medium,
long). In sequence blocks, subjects were presented with a repeating
sequence of step lengths (i.e., short-long-medium-long-short-medium). The first random block (R1) was used to familiarize the subject
to the task. The second random block (R2) provided a measure of final
baseline performance. In subsequent training blocks (S1–S3), subjects were presented with the repeating sequence. Sequence-specific learning was calculated as the difference in performance
between the last training block (S3) and the last random block (R3);
nonspecific learning was calculated as the difference between
blocks R2 and R3. The final block (S4) tested whether subjects
could immediately recall the learned sequence after reexposure to
the random sequence (R3). At the end, subjects were asked whether
they “noticed any patterns or repeating sequence?”
Sequence-by-sequence success rate was calculated as the fraction
of hits within each six-step sequence. The score (number of successful
hits) and mean error were calculated to measure performance within
each block. Error was defined as the absolute difference between the
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Fig. 1. A: visual feedback was projected on a screen in front of the treadmill. A 12-marker setup was used for motion capture. MT, metatarsal. B: target (open
square) and foot position of the swing leg (filled circle) were displayed during the walking task. The targets moved down the screen at a speed corresponding
to the treadmill speed (arrows). Stance leg foot position (open circle) was not visible on the display. The vertical distance between the current target (red square)
and the next target (gray square) indicated the desired step length (1). The position of the swing leg appeared after ipsilateral toe off (2). The current target turned
from red to white color on a successful hit (3). Scores were displayed on the upper right corner of the screen. The current target and foot position disappeared
after ipsilateral heel strike (4). C: the order of random (R1–R3) and sequence blocks (S1–S4) in the sequence learning paradigm. Each block consisted of 100
steps. Sequence-specific learning was calculated as the difference in performance between S3 and R3 (solid line); nonspecific learning was calculated as the
difference between R2 and R3 (dotted line).
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LOCOMOTOR SEQUENCE LEARNING
foot position and target position (i.e., | center of the circle ⫺ center of
the square |) and was normalized to each subject’s leg length. Error in
the anterior-posterior directions was analyzed. To evaluate sequencespecific learning, we subtracted the mean score in R3 from the mean
score in the S3 block (Perez et al. 2007; Willingham et al. 2000).
Nonspecific learning was measured by subtracting the mean score in
R3 from the mean score in R2 (Perez et al. 2007).
Statistical analysis. Between-groups repeated-measures ANOVA
was used to compare performance changes over the experimental
blocks between groups (e.g., 7 blocks ⫻ 2 age groups) and speed
conditions (e.g., 7 blocks ⫻ 2 speeds). When ANOVAs showed
significant effects, the Tukey’s honest significant difference test was
used for post hoc pairwise analysis (i.e., R3 and S3 for sequencespecific learning, R2 and R3 for nonspecific learning).
RESULTS
Fig. 2. Locomotor sequence learning. Each sequence
consisted of 6 steps, and each block consisted of 16
complete sequences. A: performance in a typical subject
over 7 blocks. Score (left y-axis, maximum score ⫽
100) is the total number of hits in each block (top
x-axis). Success rate (right y-axis) is the fraction of hits
within each 6-step sequence (bottom x-axis). Score (bar
graph) and success rate (line graph) for the same subject
are overlaid on the plot to show variability within each
block. B: mean success rate for each sequence averaged
across subjects (n ⫽ 10). Shaded area of the curve
represents standard error (SE). C: mean score (number
of hits) over 7 blocks of testing averaged across subjects
(n ⫽ 10). D: nonspecific learning (R3 ⫺ R2) and
sequence-specific learning (S3 ⫺ R3) for individual
subjects. E: group average mean error (n ⫽ 10) in the
anterior-posterior (AP) direction, calculated as the
foot position (5th MT) relative to the target position.
Error was normalized to each subject’s leg length. F:
group average cadence (n ⫽ 10). *P ⬍ 0.05; ns, not
significant.
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Locomotor sequence learning. Subjects improved their score
over the seven blocks of testing, performing better in sequence
blocks compared with random blocks. A typical subject hit
about 50% of the targets in the first random block, R1, and
improved his score on the second random block, R2, when he
became more familiar with the task (Fig. 2A). Subjects had
more difficulty shortening than lengthening step length. The
number of hits on short steps was significantly lower compared
with long steps (P ⬍ 0.001). Learning continued over the
subsequent sequence blocks, S1–S3, as the score improved
further. To determine whether the learning was sequence
specific, we exposed the subject to another random block, R3.
A drop in score in R3 compared with S3 indicates that the
learning was sequence specific. That is, the subject did not
simply learn to react to the visual stimuli. Moreover, the
subject was able to recall the learned sequence on reexposure
in S4. We also quantified sequence-by-sequence performance
as the rate of success within each six-step sequence. Sequenceby-sequence performance is plotted on top of the score from
the same subject to show variability in performance within a
block (Fig. 2A).
Learning was evident in both the average sequence-bysequence success rate (Fig. 2B) and the average change in
score across subjects (Fig. 2C). Statistical analysis was performed on the change in score across blocks. Significant
performance changes were observed over the seven blocks of
testing [repeated-measures ANOVA: effect of block,
F(6,54) ⫽ 28.7, P ⬍ 0.001]. Importantly, the score decreased
from S3 to R3, indicating that the learning was sequence
specific (P ⫽ 0.03). Moreover, the amount of nonspecific
learning (i.e., R3 ⫺ R2) was not significant (P ⫽ 0.1), sug-
LOCOMOTOR SEQUENCE LEARNING
mature in children under the age of 10 yr (Petersen et al. 2010).
Figure 3A shows the average sequence-by-sequence success
rate across the two age groups. Younger children had lower
success rate in baseline (R1, R2), but they were able to improve
performance over each block of training. Children in both age
groups showed similar success rates by the third sequence
block, S3.
Statistical analysis was performed on the group average
change in score (Fig. 3B). The results showed a significant
difference between age groups [repeated-measures ANOVA:
F(2,15) ⫽ 6.01, P ⫽ 0.02; block ⫻ age, F(12,150) ⫽ 0.9, P ⫽
0.5]. Post hoc analysis showed a significant difference between
younger and older children (P ⬍ 0.001) and between younger
children and adults (P ⬍ 0.001). All age groups (6 –10 yr,
11–16 yr, adults) showed sequence-specific learning (P ⬍
0.001), as indicated by a decrease in score from S3 to R3. There
was no increase in score from R3 to R2 (P ⱖ 0.5), which
indicates little nonspecific learning beyond R2.
The magnitude of error was significantly reduced over the
seven blocks of training [repeated-measures ANOVA: effect of
blocks, F(6,144) ⫽ 32.5, P ⬍ 0.001]. Error was normalized to
each subject’s leg length; the average leg length is 0.6 ⫾ 0.05
Fig. 3. Locomotor sequence learning in healthy adults vs. children. A: mean success rate for each sequence averaged across children in two age groups: 6 –10
yr (n ⫽ 8) and 11–16 yr (n ⫽ 10). Shaded area of the curve represents SE. B–D: group average mean score (B), normalized AP error (C), and cadence (D). Bars
represent average data across 7 blocks of testing within each age group: 6 –10 yr (n ⫽ 8), 11–16 yr (n ⫽ 10), and adults (n ⫽ 10). E–G: individual data for each
child plotted against age. Pearson’s correlation (r) is shown for baseline score (E), nonspecific learning (F) and sequence-specific learning (G). *P ⬍ 0.05.
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gesting that improved performance (after becoming accustomed to the walking task in R1) was mostly accounted for by
sequence-specific learning. The amounts of nonspecific and
sequence-specific learning for individual subjects are illustrated in Fig. 2D. At the end, subjects were asked whether they
“noticed any patterns or repeating sequence?” None of the
subjects gave perfect descriptions of the six-step repeating
sequence. Thus the step length sequence appears to be learned
without conscious awareness.
The group average (n ⫽ 10) mean error is plotted in Fig. 2E.
The magnitude of errors decreased as the score improved
[repeated-measures ANOVA: F(6,54) ⫽ 16.8, P ⬍ 0.001; Fig.
2E]. Post hoc analysis showed a significant decrease in error
from R1 to R2 (P ⬍ 0.001). The same cadence was maintained
across all blocks [repeated-measures ANOVA: F(6,54) ⫽ 0.2,
P ⫽ 0.9; Fig. 2F].
Younger children performed worse in baseline but showed
the same amount of sequence learning compared with older
children. To compare the learning rates from early to late
childhood, children were divided into two age groups: 6 –10
and 11–16 yr old (Fig. 3, A and B). The groups reflect the
development in cortical control of gait, which is not fully
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LOCOMOTOR SEQUENCE LEARNING
at two different speeds. The score was lower at the fast walking
speed [repeated-measures ANOVA: effect of speed, F(1,18) ⫽
19.1, P ⬍ 0.001]. The score improved over seven blocks of
testing at both walking speeds [repeated-measures ANOVA:
effect of block, F(6,108) ⫽ 46.7, P ⬍ 0.001; block ⫻ speed,
F(6,108) ⫽ 1.2, P ⫽ 0.3]. Post hoc analysis showed a significant amount of sequence-specific learning (P ⬍ 0.01) at both
speeds. There was no significant difference between blocks R3
and R2 (P ⫽ 0.1), indicating little nonspecific learning beyond
the first random block R2. Subjects reached a lower final score
at the fast speed than at the slow speed, which suggests that
learning capacity may be more limited when the task becomes
more challenging.
Subjects made larger errors at the fast walking speed [repeatedmeasures ANOVA: effect of speed, F(1,18) ⫽ 539.6, P ⬍0.001;
Fig. 4C]. The magnitude of the error decreased over seven blocks
of training [repeated-measures ANOVA: effect of block,
F(6,108) ⫽ 25.8, P ⬍ 0.001; block ⫻ speed, F(6,108) ⫽ 3.4,
P ⫽ 0.004]. Cadence was higher at the fast walking speed
[repeated-measures ANOVA: effect of speed, F(1,18) ⫽
18,285.6, P ⬍ 0.001; Fig. 4D]. This is expected, because the
same step length was imposed at the slow and fast walking
speeds. Subjects used the same cadence across the seven
blocks of testing [repeated-measures ANOVA: effect of
block, F(6,108) ⫽ 0.2, P ⫽ 0.9].
DISCUSSION
We used a novel paradigm to study spatial sequence learning
during walking. The results showed that visually guided step
length modification was improved through training. After
training, subjects performed better on the repeating sequence
compared with random sequences, suggesting that subjects
Fig. 4. Locomotor sequence learning at different walking speeds. A: mean success rate for each sequence
averaged across subjects for two walking speeds: slow
(2.3 ⫾ 0.16 km/h) and fast (3.2 ⫾ 0.16 km/h). B–D:
group average mean score (B), normalized AP error (C),
and cadence (D). Bars represent average data across 7
blocks of testing at 2 different walking speeds: slow
(n ⫽ 10) and fast (n ⫽ 10). *P ⬍ 0.05.
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m in younger children, 0.79 ⫾ 0.08 m in older children, and
0.89 ⫾ 0.05 m in adults. The normalized errors were largest in
younger children [repeated-measures ANOVA: effect of age,
F(2,24) ⫽ 21.0, P ⬍0.001; blocks ⫻ age, F(12,144) ⫽ 2.2,
P ⫽ 0.01; Fig. 3C]. Post hoc analysis showed a significant
difference in normalized error between younger and older
children (P ⬍ 0.001) and between younger children and adults
(P ⬍ 0.001).
Subjects maintained the same cadence across all blocks
[repeated-measures ANOVA: effect of blocks, F(6,150) ⫽ 1.0,
P ⫽ 0.4; Fig. 3D]. Note that cadence was matched across age
groups [repeated-measures ANOVA: effect of age, F(2,25) ⫽
0.07, P ⫽ 0.8].
Regression analysis of individual data was performed to
confirm the results from group average data. The baseline (R2)
score for each child is plotted against age (Fig. 3E). There was
a significant correlation between age and baseline score (r ⫽
0.71, P ⫽ 0.001); performance improved from early to late
childhood. There was no significant correlation between age
and nonspecific learning (Fig. 3F; r ⫽ 0.22, P ⫽ 0.37) or
sequence-specific learning (Fig. 3G; r ⫽ 0.05, P ⫽ 0.85). Thus
younger children were able to learn and improve performance,
despite starting with lower baseline score compared with older
children.
Learning capacity is dependent on walking speed. Locomotor patterns are characterized by distinct regularity in spatial
and temporal parameters; the percentage of stride time spent in
stance phase decreases as walking speed increases (Dietz et al.
1994). At faster walking speeds, subjects have less time to
increase (or decrease) push-off forces to shorten (or lengthen)
a step. Therefore, we predicted that subjects would perform
worse during fast walking compared with slow walking.
Figure 4, A and B, shows the performance of adults walking
LOCOMOTOR SEQUENCE LEARNING
In humans, the corticospinal tract directly modulates leg
muscle activation during steady-state walking (Petersen et al.
2001, 2010). Increase in muscular coherence in developing
children reflects the maturation level of the corticospinal drive
(Petersen et al. 2010). In addition, corticospinal input facilitates ankle muscle activation during foot placements in visual
walking tasks (Schubert et al. 1999). Thus the human corticospinal tract likely contributes to the precision of foot placement
control during human walking.
Locomotor patterns in young children (age ⬍ 4 yr) develop rapidly, resulting in greater specificity in muscle activation patterns on foot contact (Dominici et al. 2011). More
gradual development in walking occurs until 8 –10 yr of age
(Lacquaniti et al. 2012). In general, the walking pattern of
children shows greater variability compared with that of adults
(Dominici et al. 2010; Petersen et al. 2010). The corticospinal
drive is not fully mature in children under the age of 10 yr
(Petersen et al. 2010). We therefore predicted that visuomotor
control of walking might be more difficult in younger children
compared with adults.
Our results are consistent with the hypothesis that the development of the corticospinal tract contributes to the control
of foot placement. Children up to 10 yr of age performed worse
at the task compared with older children. Interesting, we found
that the ability to learn in healthy children aged 6 –15 yr is
comparable to that of healthy adults during visually guided step
length modifications. There was no correlation in the amount
of sequence-specific learning and nonspecific learning with
age. We speculate that this may be related to participation in
sports and other activities, which often involve changes in step
length (e.g., soccer, hopscotch). The result is interesting because it suggests that mechanisms that underlie the planning
and execution of voluntary gait modifications are intact before
the locomotor system is fully mature. The ability to make gait
modifications may facilitate the learning of new walking patterns. In fact, our results showed that the magnitude and rate of
locomotor sequence learning are similar between children and
adults.
Conclusions. In summary, we have developed and used a
novel paradigm to study sequence learning during visually
guided walking in healthy subjects. Our results demonstrate
that implicit sequence learning can be integrated with a highly
automatic task such as walking. We conclude that visuomotor
coordination in walking is dependent on the development of
the corticospinal tract in children.
GRANTS
This work was supported by the Danish Medical Research Council (11–
107721/FSS) and the Whitaker International Program.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the authors.
AUTHOR CONTRIBUTIONS
J.T.C. and J.B.N. conception and design of research; J.T.C. and P.J.
performed experiments; J.T.C. and P.J. analyzed data; J.T.C., P.J., and J.B.N.
interpreted results of experiments; J.T.C. prepared figures; J.T.C. drafted
manuscript; J.T.C., P.J., and J.B.N. edited and revised manuscript; J.T.C., P.J.,
and J.B.N. approved final version of manuscript.
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used knowledge about the sequence to plan and control foot
placement (rather than simply reacting to the visual stimuli).
Moreover, none of the subjects were able to describe the
repeating sequence correctly, suggesting that the step length
sequence could be learned implicitly without conscious awareness.
Visuomotor coordination in locomotion. Vision provides
information about the position of objects within the environment and our own movement within the environment. The
nervous system makes anticipatory adjustments to the control
of walking in response to visual cues (Matthis and Fajen 2014;
Warren et al. 1986). Patla et al. (1989) have reported the effects
of the timing of visual cues to regulate step length during
overground walking. Subjects were able to shorten step length
when the visual cue was given before the contralateral heel
contact, but not when the visual cue was given after the
contralateral heel contact (Patla et al. 1989). When the visual
cue was given at or before the contralateral heel contact,
subjects had enough time to regulate step length by changing
the contralateral push-off force. Subjects reduced push-off
force to shorten step length and increased push-off force to
increase step length (Patla et al. 1989). Consistent with previous reports on step length modification (Patla et al. 1989),
subjects had more difficulty shortening step length during our
visuomotor walking paradigm. Moreover, we found that subjects were less accurate at the fast walking speeds because
there is less time to change push-off forces to shorten (or
lengthen) a step.
Locomotor sequence learning. We applied principles of
sensorimotor learning to establish a new paradigm for studying
visually guided sequence learning during walking. Learning
can be defined as the storage of new motor patterns through
repeated practice. Once a movement is learned, it could be
immediately used in the appropriate context. Motor sequence
learning involves integrating a series of movements that are
executed in a specific order. The SRT task from the original study
by Nissen and Bullemer (1987) has been used to study age-related
changes in this type of procedural learning (Janacsek et al. 2012;
King et al. 2013; Thomas et al. 2004). However, there are no
studies in the literature that have explored sequence learning in
lower limb movements during walking. We found that healthy
adults and children improved their performance over 7 blocks
of testing and learned a specific walking sequence over 300
training steps. In this case, improved performance and accuracy were achieved without changing movement timing (e.g.,
cadence). This demonstrates that sequence learning can be
integrated with human locomotor pattern generation.
Neural control of gait modifications. Visually guided limb
modifications must be integrated into the locomotor pattern
during ongoing movements (Georgopoulos and Grillner 1989;
Rossignol 1996). Cortical mechanisms are involved in the
planning and execution of movements during visually guided
walking (Drew et al. 2008; Drew and Marigold 2015). The
motor cortex of cats is not strongly modulated during unobstructed level walking but becomes more active when precise
adjustments in paw placement or end-point control is required
(Beloozerova and Sirota 1993; Drew 1993). Lesions of the
pyramidal tract or the corticospinal tract can cause marked
deficits in voluntary modifications to the walking pattern, such
as in obstacle avoidance (Drew et al. 2002).
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