Ebrahimi S, Ostry DJ (2022) Persistence of adaptation
following visuomotor training. J Neurophysiol
128:1312-1323.
Abstract
PDF
Retention tests
conducted after sensorimotor adaptation frequently
exhibit a rapid return to baseline performance once
the altered sensory feedback is removed. This
so-called washout of learning stands in contrast with
other demonstrations of retention, such as savings on
re-learning and anterograde interference effects of
initial learning on new learning. In the present
study, we tested the hypothesis that washout occurs
when there is a detectable discrepancy in retention
tests between visual information on the target
position and somatosensory information on the position
of the limb. Participants were tested following
adaptation to gradually rotated visual feedback (15°
or 30°). Two different types of targets were used for
retention testing, a point target in which a
perceptual mismatch is possible, and an arc-target
that eliminated the mismatch. It was found that,
except when point targets were used, retention test
movements were stable throughout aftereffect trials,
indicating little loss of information. Substantial
washout was only observed in tests with a single point
target, following adaptation to a large amplitude 30°
rotation. In control studies designed to minimize the
use of explicit strategies during learning, we
observed similar patterns of decay when participants
moved to point targets that suggests that the effects
observed here relate primarily to implicit learning.
The results suggest that washout in aftereffect trials
following visuomotor adaptation is due to a detectable
mismatch between vision and somatosensation. When the
mismatch is removed experimentally, there is little
evidence of loss of information.NEW & NOTEWORTHY
Aftereffects following sensorimotor adaptation are
important because they bear on the understanding of
the mechanisms that subserve forgetting. We present
evidence that information loss previously reported
during retention testing occurs only when there is a
detectable discrepancy between vision and
somatosensation and, if this mismatch is removed, the
persistence of adaptation is observed. This suggests
that washout during aftereffect trials is a
consequence of the experimental design rather than a
property of the memory system itself.
Kumar N, Sidarta A, Smith C, Ostry DJ (2022)
Ventrolateral prefrontal cortex contributes to human
motor learning. eNeuro
Abstract
PDF
This study
assesses the involvement in human motor learning, of
the ventrolateral prefrontal cortex (BA 9/46v), a
somatic region in the middle frontal gyrus. The
potential involvement of this cortical area in motor
learning is suggested by studies in nonhuman primates
which have found anatomic connections between this
area and sensorimotor regions in frontal and parietal
cortex, and also with basal ganglia output zones. It
is likewise sug- gested by electrophysiological
studies which have shown that activity in this region
is implicated in somatic sensory memory and is also
influenced by reward. We directly tested the
hypothesis that area 9/46v is in- volved in
reinforcement-based motor learning in humans.
Participants performed reaching movements to a hidden
target and received positive feedback when successful.
Before the learning task, we applied continu- ous
theta burst stimulation (cTBS) to disrupt activity in
9/46v in the left or right hemisphere. A control group
received sham cTBS. The data showed that cTBS to left
9/46v almost entirely eliminated motor learning,
whereas learning was not different from sham
stimulation when cTBS was applied to the same zone in
the right hemisphere. Additional analyses showed that
the basic reward-history-dependent pattern of
movements was preserved but more variable following
left hemisphere stimulation, which suggests an overall
deficit in so- matic memory for target location or
target directed movement rather than reward processing
per se. The re- sults indicate that area 9/46v is part
of the human motor learning circuit.
Sidarta A, Komar J, Ostry DJ (2022) Clustering analysis
of movement kinematics in reinforcement learning. J
Neurophysiol 127:341-353.
Abstract
PDF
Reinforcement
learning has been used as an experimental model of
motor skill acquisition, where at times movements are
suc- cessful and thus reinforced. One fundamental
problem is to understand how humans select exploration
over exploitation during learning. The decision could
be influenced by factors such as task demands and
reward availability. In this study, we applied a
clustering algorithm to examine how a change in the
accuracy requirements of a task affected the choice of
exploration over ex- ploitation. Participants made
reaching movements to an unseen target using a planar
robot arm and received reward after each successful
movement. For one group of participants, the width of
the hidden target decreased after every other training
block. For a second group, it remained constant. The
clustering algorithm was applied to the kinematic data
to characterize motor learning on a trial-to-trial
basis as a sequence of movements, each belonging to
one of the identified clusters. By the end of
learning, movement trajectories across all
participants converged primarily to a single cluster
with the greatest number of suc- cessful trials.
Within this analysis framework, we defined exploration
and exploitation as types of behavior in which two
succes- sive trajectories belong to different or
similar clusters, respectively. The frequency of each
mode of behavior was evaluated over the course of
learning. It was found that by reducing the target
width, participants used a greater variety of
different clusters and displayed more exploration than
exploitation. Excessive exploration relative to
exploitation was found to be detrimental to subsequent
motor learning. NEW & NOTEWORTHY The choice of
exploration versus exploitation is a fundamental
problem in learning new motor skills through
reinforcement. In this study, we employed a
data-driven approach to characterize movements on a
trial-by-trial basis with an unsupervised clustering
algorithm. Using this technique, we found that changes
in task demands and, in particular, in the required
accuracy of movements, influenced the ratio of
exploration to exploitation. This analysis framework
provides an attractive tool to investigate mechanisms
of explorative and exploitative behavior while
studying motor learning.
Sedda G, Ostry DJ
(2021) Self-operated stimuli improve subsequent visual
motion integration. J Vision 21:13,1-15.
Abstract
PDF
Evidences of
perceptual changes that accompany motor activity have
been limited primarily to audition and
somatosensation. Here we asked whether motor learning
results in changes to visual motion perception. We
designed a reaching task in which participants were
trained to make movements along several directions,
while the visual feedback was provided by an
intrinsically ambiguous moving stimulus directly tied
to hand motion. We find that training improves
coherent motion perception and that changes in
movement are correlated with perceptual changes. No
perceptual changes are observed in passive training
even when observers were provided with an explicit
strategy to facilitate single motion perception. A
Bayesian model suggests that movement training
promotes the fine-tuning of the internal
representation of stimulus geometry. These results
emphasize the role of sensorimotor interaction in
determining the persistent properties in space and
time that define a percept.
Ohashi H, Ostry DJ (2021) Neural development of speech
sensorimotor learning. J Neurosci 41:4023-4035.
Kumar N, vanVugt FT, Ostry DJ (2021) Recognition memory
for human motor learning. Curr Biol, In Press.
Abstract
PDF
van Vugt FT, Near J, Hennessy T, Doyon J, Ostry DJ
(2020) Early stages of sensorimotor map acquisition:
neurochemical signature in primary motor cortex and
its relation to functional connectivity. J
Neurophysiol 122: 1708–1720.
Abstract
PDF
Ito T, Bai
J, Ostry DJ (2020) Contribution of sensory memory to
speech motor learning. J Neurophysiol 124:1103-1109.
Abstract
PDF
Patri JF,
Ostry DJ, Diard J, Schwartz JL, Trudeau-Fisette P,
Savariaux C, Perrier P (2020) Speakers are able to
categorize vowels based on tongue somatosensation.
Proc Natl Acad Sci U S A. 117:6255-6263.
Abstract
PDF
Vahdat S,
Darainy M, Thiel A, Ostry DJ (2019) A single session
of robot-controlled proprioceptive training modulates
functional connectivity of sensory-motor networks in
chronic stroke. Neurorehabil Neural Repair 33:70-81.
Abstract
PDF
Ohashi H,
Gribble PL, Ostry DJ (2019) Somatosensory cortical
excitability changes precede those in motor cortex
during human motor learning. J Neurophysiol
122:1397-1405.
Abstract
PDF
Darainy M,
Vahdat S, Ostry DJ (2019) Neural basis of sensorimotor
learning in speech motor adaptation. Cereb Cortex
29:2876-2889.
Abstract
PDF
Ohashi H,
Valle-Mena R, Gribble P, Ostry DJ (2019) Movements
following force-field adaptation are aligned with
altered sense of limb position. Exp Brain Res
237:1303-1313.
Abstract
PDF
van Vugt
FT, Ostry DJ (2019) Early stages of sensorimotor map
acquisition: learning with free exploration, without
active movement or global structure. J Neurophysiol
122:1708-1720.
Abstract
PDF
Kumar N,
Manning TF, Ostry DJ (2019) Somatosensory cortex
participates in the consolidation of human motor
memory. PLOS Biol 17(10).
Abstract
PDF
Van Vugt FT,
Ostry DJ (2019) Early stages of sensorimotor map
acquisition: learning with free exploration, without
active movement or global structure. J Neurophysiol
122:1708-1720.
Abstract
PDF
Ohashi H, Gribble PL, Ostry DJ (2019) Somatosensory
cortical excitability changes precede those in motor
cortex during human motor learning. J Neurophysiol 122:1397-1405.
Abstract
PDF
Darainy M, Vahdat S, Ostry DJ (2019) Neural basis of
sensorimotor learning in speech motor adaptation.
Cereb Cortex 29:2876-2889.
Abstract
PDF
Ohashi H, Valle-Mena R, Gribble P, Ostry DJ (2019) Movements following
force-field adaptation are aligned with altered sense
of limb position. Exp Brain Res
237:1303-1313.
Abstract
PDF
Vahdat S, Darainy M, Thiel A, Ostry DJ (2018) A single session of
robot-controlled proprioceptive training modulates
functional connectivity of sensory motor networks and
improves reaching accuracy in chronic stroke.
Neurorehabil Neural Repair 33:70-81.
Abstract
PDF
Sidarta A, van Vugt FT, Ostry DJ (2018) Somatosensory
working memory in human reinforcement-based motor
learning. J Neurophysiol 120:3275-3286.
Abstract
PDF
Bernardi NF, Van Vugt FT, Valle-Mena R, Vahdat S, Ostry
DJ (2018) Error-related persistence of motor activity in
resting-state networks. J Cogn Neurosci 20:1-19.
Abstract
PDF
Milner TE, Firouzimehr Z, Babadi S, Ostry DJ (2018)
Different adaptation rates to abrupt and gradual changes
in environmental dynamics. Exp Brain Res 236:2923-2933.
Abstract
PDF
Van Vugt FT and Ostry DJ (2018) The Structure and
Acquisition of Sensorimotor Maps. J Cognitive Neurosci.
30:3, 290-306.
Abstract
PDF
One of the puzzles of learning to talk or play
a musical instrument is how we learn which movement
produces a particular sound: an audiomotor map.
Existing research has used mappings that are already
well learned such as controlling a cursor using a
computer mouse. By contrast, the acquisition of novel
sensorimotor maps was studied by having participants
learn arm movements to auditory targets. These sounds
did not come from different directions but, like
speech, were only distinguished by their frequencies.
It is shown that learning involves forming not one but
two maps: a point map connecting sensory targets with
motor commands and an error map linking sensory errors
to motor corrections. Learning a point map is possible
even when targets never repeat. Thus, although
participants make errors, there is no opportunity to
correct them because the target is different on every
trial, and therefore learning cannot be driven by
error correction. Furthermore, when the opportunity
for error correction is provided, it is seen that
acquiring error correction is itself a learning
process that changes over time and results in an error
map. In principle, the error map could be derived from
the point map, but instead, these two maps are
independently acquired and jointly enable sensorimotor
control and learning. A computational model shows that
this dual encoding is optimal and simulations based on
this architecture predict that learning the two maps
results in performance improvements comparable with
those observed empirically
Sidarta A, Vahdat S, Bernardi NF, Ostry DJ (2016)
Somatic and reinforcement-based plasticity in the
initial stages of human motor learning. J Neurosci.
36:11682-11692.
Abstract
PDF
As one learns to dance or play tennis, the
desired somatosensory state is typically unknown.
Trial and error is important as motor behavior is
shaped by successful and unsuccessful movements. As an
experimental model, we designed a task in which human
participants make reaching movements to a hidden
target and receive positive reinforcement when
successful. We identified somatic and
reinforcement-based sources of plasticity on the basis
of changes in functional connectivity using
resting-state fMRI before and after learning. The
neuroimaging data revealed reinforcement-related
changes in both motor and somatosensory brain areas in
which a strengthening of connectivity was related to
the amount of positive reinforcement during learning.
Areas of prefrontal cortex were similarly altered in
relation to reinforcement, with connectivity between
sensorimotor areas of putamen and the reward-related
ventromedial prefrontal cortex strengthened in
relation to the amount of successful feedback
received. In other analyses, we assessed connectivity
related to changes in movement direction between
trials, a type of variability that presumably reflects
exploratory strategies during learning. We found that
connectivity in a network linking motor and
somatosensory cortices increased with trial-to-trial
changes in direction. Connectivity varied as well with
the change in movement direction following incorrect
movements. Here the changes were observed in a somatic
memory and decision making network involving
ventrolateral prefrontal cortex and second
somatosensory cortex. Our results point to the idea
that the initial stages of motor learning are not
wholly motor but rather involve plasticity in somatic
and prefrontal networks related both to reward and
exploration.
Lametti DR, Rochet-Capellan A, Neufeld E, Shiller DM,
Ostry DJ (2014) Plasticity in the human speech motor
system drives changes in speech perception. J Neurosci
34:10339-10346.
Abstract Article in PDF format (390
KB)
Recent studies of human speech motor learning
suggest that learning is accompanied by changes in
auditory perception. But what drives the perceptual
change? Is it a consequence of changes in the motor
system? Or is it a result of sensory inflow during
learning? Here, subjects participated in a speech
motor-learning task involving adaptation to altered
auditory feedback and they were subsequently tested
for perceptual change. In two separate experiments,
involving two different auditory perceptual continua,
we show that changes in the speech motor system that
accompany learning drive changes in auditory speech
perception. Specifically, we obtained changes in
speech perception when adaptation to altered auditory
feedback led to speech production that fell into the
phonetic range of the speech perceptual tests.
However, a similar change in perception was not
observed when the auditory feedback that subjects'
received during learning fell into the phonetic range
of the perceptual tests. This indicates that the
central motor outflow associated with vocal
sensorimotor adaptation drives changes to the
perceptual classification of speech sounds.
Lametti DR, Krol SA, Shiller DM, Ostry DJ (2014) Brief
periods of auditory perceptual training can determine
the sensory targets of speech motor learning. Psychol
Sci. 25:1325-1336.
Abstract Article in
PDF format (693 KB)
The perception of speech is notably malleable
in adults, yet alterations in perception seem to have
little impact on speech production. However, we
hypothesized that speech perceptual training might
immediately influence speech motor learning. To test
this, we paired a speech perceptual-training task with
a speech motor-learning task. Subjects performed a
series of perceptual tests designed to measure and
then manipulate the perceptual distinction between the
words head and had. Subjects then produced head with
the sound of the vowel altered in real time so that
they heard themselves through headphones producing a
word that sounded more like had. In support of our
hypothesis, the amount of motor learning in response
to the voice alterations depended on the perceptual
boundary acquired through perceptual training. The
studies show that plasticity in adults' speech
perception can have immediate consequences for speech
production in the context of speech learning.
Ito T, Johns AR, Ostry DJ (2014) Left lateralized
enhancement of orofacial somatosensory processing due to
speech sounds. J Speech Lang Hear Res. 56:1875-81.
Abstract Article in PDF format
(199 KB)
PURPOSE:
Somatosensory information associated with speech
articulatory movements affects the perception of
speech sounds and vice versa, suggesting an intimate
linkage between speech production and perception
systems. However, it is unclear which cortical
processes are involved in the interaction between
speech sounds and orofacial somatosensory inputs. The
authors examined whether speech sounds modify
orofacial somatosensory cortical potentials that were
elicited using facial skin perturbations.
METHOD:
Somatosensory event-related potentials in EEG were
recorded in 3 background sound conditions (pink noise,
speech sounds, and nonspeech sounds) and also in a
silent condition. Facial skin deformations that are
similar in timing and duration to those experienced in
speech production were used for somatosensory
stimulation.
RESULTS:
The authors found that speech sounds reliably enhanced
the first negative peak of the somatosensory
event-related potential when compared with the other 3
sound conditions. The enhancement was evident at
electrode locations above the left motor and premotor
area of the orofacial system. The result indicates
that speech sounds interact with somatosensory
cortical processes that are produced by
speech-production-like patterns of facial skin
stretch.
CONCLUSION:
Neural circuits in the left hemisphere, presumably in
left motor and premotor cortex, may play a prominent
role in the interaction between auditory inputs and
speech-relevant somatosensory processing.
Vahdat S, Darainy M, Ostry DJ (2014) Structure of
plasticity in human sensory and motor networks due to
perceptual learning. J Neurosci 34:2451-63.
Abstract -Article in PDF format (2.25
MB)
As we begin to acquire a new motor skill, we
face the dual challenge of determining and refining
the somatosensory goals of our movements and
establishing the best motor commands to achieve our
ends. The two typically proceed in parallel, and
accordingly it is unclear how much of skill
acquisition is a reflection of changes in sensory
systems and how much reflects changes in the brain's
motor areas. Here we have intentionally separated
perceptual and motor learning in time so that we can
assess functional changes to human sensory and motor
networks as a result of perceptual learning. Our
subjects underwent fMRI scans of the resting brain
before and after a somatosensory discrimination task.
We identified changes in functional connectivity that
were due to the effects of perceptual learning on
movement. For this purpose, we used a neural model of
the transmission of sensory signals from perceptual
decision making through to motor action. We used this
model in combination with a partial correlation
technique to parcel out those changes in connectivity
observed in motor systems that could be attributed to
activity in sensory brain regions. We found that,
after removing effects that are linearly correlated
with somatosensory activity, perceptual learning
results in changes to frontal motor areas that are
related to the effects of this training on motor
behavior and learning. This suggests that perceptual
learning produces changes to frontal motor areas of
the brain and may thus contribute directly to motor
learning.
Darainy M, Vahdat S, Ostry DJ (2013) Perceptual
learning in sensorimotor adaptation. J Neurophysiol 110:
2152-2162.
Abstract -
Article in PDF format (935
KB)
Motor learning often involves situations in
which the somatosensory targets of movement are
initially, poorly defined, as for example, in learning
to speak or learning the feel of a proper tennis
serve. Under these conditions, motor skill acquisition
presumably requires perceptual as well as motor
learning. That is, it engages both the progressive
shaping of sensory targets and associated changes in
motor performance. In the present paper, we test the
idea that perceptual learning alters somatosensory
function and in so doing produces changes to motor
performance and sensorimotor adaptation. Subjects in
these experiments undergo perceptual training in which
a robotic device passively moves the arm on one of a
set of fan shaped trajectories. Subjects are required
to indicate whether the robot moved the limb to the
right or the left and feedback is provided. Over the
course of training both the perceptual boundary and
acuity are altered. The perceptual learning is
observed to improve both the rate and extent of
learning in a subsequent sensorimotor adaptation task
and the benefits persist for at least 24 hours. The
improvement in the present studies is obtained
regardless of whether the perceptual boundary shift
serves to systematically increase or decrease error on
subsequent movements. The beneficial effects of
perceptual training are found to be substantially
dependent upon reinforced decision-making in the
sensory domain. Passive-movement training on its own
is less able to alter subsequent learning in the motor
system. Overall, this study suggests perceptual
learning plays an integral role in motor learning.
Bernardi NF, Darainy M, Bricolo E, Ostry DJ (2013)
Observing motor learning produces somatosensory change.
J Neurophysiol 110: 1804-1810.
Abstract
- Article in PDF format
(264 KB)
Observing the actions of others has been shown
to affect motor learning, but does it have effects on
sensory systems as well? It has been recently shown
that motor learning that involves actual physical
practice is also associated with plasticity in the
somatosensory system. Here, we assessed the idea that
observational learning likewise changes somatosensory
function. We evaluated changes in somatosensory
function after human subjects watched videos depicting
motor learning. Subjects first observed video
recordings of reaching movements either in a clockwise
or counterclockwise force field. They were then
trained in an actual force-field task that involved a
counterclockwise load. Measures of somatosensory
function were obtained before and after visual
observation and also following force-field learning.
Consistent with previous reports, video observation
promoted motor learning. We also found that
somatosensory function was altered following
observational learning, both in direction and in
magnitude, in a manner similar to that which occurs
when motor learning is achieved through actual
physical practice. Observation of the same sequence of
movements in a randomized order did not result in
somatosensory perceptual change. Observational
learning and real physical practice appear to tap into
the same capacity for sensory change in that subjects
that showed a greater change following observational
learning showed a reliably smaller change following
physical motor learning. We conclude that effects of
observing motor learning extend beyond the boundaries
of traditional motor circuits, to include
somatosensory representations.
Ito S, Darainy M, Sasaki M, Ostry DJ (2013)
Computational model of motor learning and perceptual
change. Biol Cybern 107:653-667.
Abstract -
Article in PDF format (1.28 MB)
Motor learning in the context of arm reaching
movements has been frequently investigated using the
paradigm of force-field learning. It has been recently
shown that changes to somatosensory perception are
likewise associated with motor learning. Changes in
perceptual function may be the reason that when the
perturbation is removed following motor learning, the
hand trajectory does not return to a straight line
path even after several dozen trials. To explain the
computational mechanisms that produce these
characteristics, we propose a motor control and
learning scheme using a simplified two-link system in
the horizontal plane:We represent learning as the
adjustment of desired joint-angular trajectories so as
to achieve the reference trajectory of the hand. The
convergence of the actual hand movement to the
reference trajectory is proved by using a
Lyapunov-like lemma, and the result is confirmed using
computer simulations. The model assumes that changes
in the desired hand trajectory influence the
perception of hand position and this in turn affects
movement control. Our computer simulations support the
idea that perceptual change may come as a result of
adjustments to movement planning with motor learning.
Nasir SM, Darainy M, Ostry DJ (2013) Sensorimotor
adaptation changes the neural coding of somatosensory
stimuli. J. Neurophysiol. 109:2077-85.
Abstract -
Article in PDF format (692 KB)
Motor learning is reflected in changes to the
brain’s functional organization as a result of
experience. We show here that these changes are not
limited to motor areas of the brain and indeed that
motor learning also changes sensory systems. We test
for plasticity in sensory systems using somatosensory
evoked potentials (SEPs). A robotic device is used to
elicit somatosensory inputs by displacing the arm in
the direction of applied force during learning. We
observe that following learning there are short
latency changes to the response in somatosensory areas
of the brain that are reliably correlated with the
magnitude of motor learning: subjects who learn more
show greater changes in SEP magnitude. The effects we
observe are tied to motor learning. When the limb is
displaced passively, such that subjects experience
similar movements but without experiencing learning,
no changes in the evoked response are observed.
Sensorimotor adaptation thus alters the neural coding
of somatosensory stimuli.
Mattar AAG, Darainy M, Ostry DJ (2013) Motor learning
and its sensory effects: The time course of perceptual
change, and its presence with gradual introduction of
load. J. Neurophysiol. 109:782-91.
Abstract -
Article in PDF format (413 KB)
A complex interplay has been demonstrated
between motor and sensory systems. We showed recently
that motor learning leads to changes in the sensed
position of the limb (Ostry DJ, Darainy M, Mattar AA,
Wong J, Gribble PL. J Neurosci 30: 5384–5393, 2010).
Here, we document further the links between motor
learning and changes in somatosensory perception. To
study motor learning, we used a force field paradigm
in which subjects learn to compensate for forces
applied to the hand by a robotic device. We used a
task in which subjects judge lateral displacements of
the hand to study somatosensory perception. In a first
experiment, we divided the motor learning task into
incremental phases and tracked sensory perception
throughout. We found that changes in perception
occurred at a slower rate than changes in motor
performance. A second experiment tested whether
awareness of the motor learning process is necessary
for perceptual change. In this experiment, subjects
were exposed to a force field that grew gradually in
strength. We found that the shift in sensory
perception occurred even when awareness of motor
learning was reduced. These experiments argue for a
link between motor learning and changes in
somatosensory perception, and they are consistent with
the idea that motor learning drives sensory change.
Lametti DR, Nasir S, Ostry DJ (2012) Sensory
preference in speech production revealed by simultaneous
alteration of auditory and somatosensory
feedback. J Neurosci 32:9351-9359.
Abstract -
Article in PDF format (1403 KB)
The idea that humans learn and maintain
accurate speech by carefully monitoring auditory
feedback is widely held. But this view neglects the
fact that auditory feedback is highly correlated with
somatosensory feedback during speech production.
Somatosensory feedback from speech movements could be
a primary means by which cortical speech areas monitor
the accuracy of produced speech. We tested this idea
by placing the somatosensory and auditory systems in
competition during speech motor learning. To do this,
we combined two speech-learning paradigms to
simultaneously alter somatosensory and auditory
feedback in real time as subjects spoke. Somatosensory
feedback was manipulated by using a robotic device
that altered the motion path of the jaw. Auditory
feedback was manipulated by changing the frequency of
the first formant of the vowel sound and playing back
the modified utterance to the subject through
headphones. The amount of compensation for each
perturbation was used as a measure of sensory
reliance. All subjects were observed to correct for at
least one of the perturbations, but auditory feedback
was not dominant. Indeed, some subjects showed a
stable preference for either somatosensory or auditory
feedback during speech.
Rochet-Capellan A, Richer L, Ostry DJ (2012)
Non-homogeneous transfer reveals specificity in speech
motor learning, J Neurophysiol 107(6):1711-1717.
Abstract -
Article in PDF format (461 MB)
Does motor learning generalize to new
situations that are not experienced during training,
or is motor learning essentially specific to the
training situation? In the present experiments, we use
speech production as a model to investigate
generalization in motor learning. We tested for
generalization from training to transfer utterances by
varying the acoustical similarity between these two
sets of utterances. During the training phase of the
experiment, subjects received auditory feedback that
was altered in real time as they repeated a single
consonant vowel-consonant utterance. Different groups
of subjects were trained with different
consonant-vowel-consonant utterances, which differed
from a subsequent transfer utterance in terms of the
initial consonant or vowel. During the adaptation
phase of the experiment, we observed that subjects in
all groups progressively changed their speech output
to compensate for the perturbation (altered auditory
feedback). After learning, we tested for
generalization by having all subjects produce the same
single transfer utterance while receiving unaltered
auditory feedback. We observed limited transfer of
learning, which depended on the acoustical similarity
between the training and the transfer utterances. The
gradients of generalization observed here are
comparable to those observed in limb movement. The
present findings are consistent with the conclusion
that speech learning remains specific to individual
instances of learning.
Mattar AAG, Nasir SM, Darainy M, Ostry DJ
(2011) Sensory change following motor
learning. in Green AM, Chapman CE, Kalaska JF
and Lepore F (Eds), Progress in Brain Research, Volume
191 (pp 29-42).
Abstract -
Article in PDF format (1.10 MB)
Here we describe two studies linking perceptual
change with motor learning. In the first, we document
persistent changes in somatosensory perception that
occur following force field learning. Subjects learned
to control a robotic device that applied forces to the
hand during arm movements. This led to a change in the
sensed position of the limb that lasted at least 24 h.
Control experiments revealed that the sensory change
depended on motor learning. In the second study, we
describe changes in the perception of speech sounds
that occur following speech motor learning. Subjects
adapted control of speech movements to compensate for
loads applied to the jaw by a robot. Perception of
speech sounds was measured before and after motor
learning. Adapted subjects showed a consistent shift
in perception. In contrast, no consistent shift was
seen in control subjects and subjects that did not
adapt to the load. These studies suggest that motor
learning changes both sensory and motor function.
Vahdat S, Darainy M, Milner TE, Ostry DJ
(2011) Functionally specific changes in
resting-state sensorimotor networks after motor
learning. J Neurosci. 31:16907–16915.
Abstract -
Article in PDF format
(603 KB)
Motor learning changes the activity of cortical
motor and subcortical areas of the brain, but does
learning affect sensory systems as well? We examined
inhumansthe effects of motor learning using fMRI
measures of functional connectivity under resting
conditions and found persistent changes in networks
involving both motor and somatosensory areas of the
brain. We developed a technique that allows us to
distinguish changes in functional connectivity that
can be attributed to motor learning from those that
are related to perceptual changes that occur in
conjunction with learning. Using this technique, we
identified a new network in motor learning involving
second somatosensory cortex, ventral premotor cortex,
and supplementary motor cortex whose activation is
specifically related to perceptual changes that occur
in conjunction with motor learning. We also found
changes in a network comprising cerebellar cortex,
primary motor cortex, and dorsal premotor cortex that
were linked to the motor aspects of learning. In each
network, we observed highly reliable linear
relationships between neuroplastic changes and
behavioral measures of either motor learning or
perceptual function. Motor learning thus results in
functionally specific changes to distinct
resting-state networks in the brain.
Rochet-Capellan A,Ostry DJ (2011) Simultaneous
acquisition of multiple auditory-motor transformations
in speech. J Neurosci. 31:2648-2655.
Abstract -
Article in PDF format
(629 KB)
The brain easily generates the movement that is
needed in a given situation. Yet surprisingly, the
results of experimental studies suggest that it is
difficult to acquire more than one skill at a time. To
do so, it has generally been necessary to link the
required movement to arbitrary cues. In the present
study, we show that speech motor learning provides an
informative model for the acquisition of multiple
sensorimotor skills. During training, subjects were
required to repeat aloud individual words in random
order while auditory feedback was altered in real-time
in different ways for the different words. We found
that subjects can quite readily and simultaneously
modify their speech movements to correct for these
different auditory transformations. This multiple
learning occurs effortlessly without explicit cues and
without any apparent awareness of the perturbation.
The ability to simultaneously learn several different
auditory-motor transformations is consistent with the
idea that, in speech motor learning, the brain
acquires instance-specific memories. The results
support the hypothesis that speech motor learning is
fundamentally local.
Ito T, Ostry DJ (2010) Somatosensory contribution
to motor learning due to facial skin
deformation. J Neurophysiol 104:1230-1230.
Abstract -
Article in PDF format (248 KB)
Motor learning is dependent on kinesthetic
information that is obtained both from cutaneous
afferents and from muscle receptors. In human arm
movement, information from these two kinds of
afferents is largely correlated. The facial skin
offers a unique situation in which there are plentiful
cutaneous afferents and essentially no muscle
receptors and, accordingly, experimental manipulations
involving the facial skin may be used to assess the
possible role of cutaneous afferents in motor
learning. We focus here on the information for motor
learning provided by the deformation of the facial
skin and the motion of the lips in the context of
speech. We used a robotic device to slightly stretch
the facial skin lateral to the side of the mouth in
the period immediately preceding movement. We found
that facial skin stretch increased lip protrusion in a
progressive manner over the course of a series of
training trials. The learning was manifest in a
changed pattern of lip movement, when measured after
learning in the absence of load. The newly acquired
motor plan generalized partially to another speech
task that involved a lip movement of different
amplitude. Control tests indicated that the primary
source of the observed adaptation was sensory input
from cutaneous afferents. The progressive increase in
lip protrusion over the course of training fits with
the basic idea that change in sensory input is
attributed to motor performance error. Sensory input,
which in the present study precedes the target
movement, is credited to the target-related motion,
even though the skin stretch is released prior to
movement initiation. This supports the idea that the
nervous system generates motor commands on the
assumption that sensory input and kinematic error are
in register.
Lametti DR, Ostry DJ (2010) Postural constraint on
movement variability. J Neurophysiol
104:1061-1067.
Abstract -
Article in PDF format (2625 KB)
Movements are inherently variable. When we move
to a particular point in space, a cloud of final limb
positions is observed around the target. Previously we
noted that
patterns of variability at the end of movement to a
circular target were not circular, but instead
reflected patterns of limb stiffness—in directions
where limb stiffness was high, variability in end
position was low, and vice versa. Here we examine the
determinants of variability at movement end in more
detail. To do this, we have subjects move the handle
of a robotic device from different starting positions
into a circular target. We use position
servocontrolled displacements of the robot’s handle to
measure limb stiffness at the end of movement and we
also record patterns of end position variability. To
examine the effect of change in posture on movement
variability, we use a visual motor transformation in
which we change the limb configuration and also the
actual movement target, while holding constant the
visual display. We find that, regardless of movement
direction, patterns of variability at the end of
movement vary systematically with limb configuration
and are also related to patterns of limb stiffness,
which are likewise configuration dependent. The result
suggests that postural configuration determines the
base level of movement variability, on top of which
control mechanisms can act to further alter
variability.
Mattar AAG, Ostry DJ (2010) Generalization of
dynamics learning across changes in movement
amplitude. J Neurophysiol 104:426-438.
Abstract -
Article in PDF format (552 KB)
Studies on generalization show the nature of
how learning is encoded in the brain. Previous studies
have shown rather limited generalization of dynamics
learning across changes in movement direction, a
finding that is consistent with the idea that learning
is primarily local. In contrast, studies show a
broader pattern of generalization across changes in
movement amplitude, suggesting a more general form of
learning. To understand this difference, we performed
an experiment in which subjects held a robotic
manipulandum and made movements to targets along the
body midline. Subjects were trained in a
velocitydependent force field while moving to a 15 cm
target. After training, subjects were tested for
generalization using movements to a 30 cm target. We
used force channels in conjunction with movements to
the 30 cm target to assess the extent of
generalization. Force channels restricted lateral
movements and allowed us to measure force production
during generalization. We compared actual lateral
forces to the forces expected if dynamics learning
generalized fully. We found that, during the test for
generalization, subjects produced reliably less force
than expected. Force production was appropriate for
the portion of the transfer movement in which
velocities corresponded to those experienced with the
15 cm target. Subjects failed to produce the expected
forces when velocities exceeded those experienced in
the training task. This suggests that dynamics
learning generalizes little beyond the range of one’s
experience. Consistent with this result, subjects who
trained on the 30 cm target showed full generalization
to the 15 cm target. We performed two additional
experiments that show that interleaved trials to the
30 cm target during training on the 15 cm target can
resolve the difference between the current results and
those reported previously.
Ostry DJ, Darainy M, Mattar AAG, Wong J, Gribble PL
(2010) Somatosensory plasticity and motor learning. J
Neurosci 30:5384-5393.
Abstract -
Article in PDF format (1016 KB)
- J Neurosci Journal Club
Commentary PDF format (210 KB) - J Neurophysiol Neuro Forum
Commentary PDF format (106 KB)
Motor learning is dependent upon plasticity in
motor areas of the brain, but does it occur in
isolation, or does it also result in changes to
sensory systems? We examined changes to somatosensory
function that occur in conjunction with motor
learning. We found that even after periods of training
as brief as 10 min, sensed limb position was altered
and the perceptual change persisted for 24 h. The
perceptual change was reflected in subsequent
movements; limb movements following learning deviated
from the prelearning trajectory by an amount that was
not different in magnitude and in the same direction
as the perceptual shift. Crucially, the perceptual
change was dependent upon motor learning. When the
limb was displaced passively such that subjects
experienced similar kinematics but without learning,
no sensory change was observed. The findings indicate
that motor learning affects not only motor areas of
the brain but changes sensory function as well.
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