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1.  Motor Costs and the Coordination of the Two Arms 
The Journal of Neuroscience  2014;34(5):1806-1818.
We have two arms, many muscles in each arm, and numerous neurons that contribute to their control. How does the brain assign responsibility to each of these potential actors? We considered a bimanual task in which people chose how much force to produce with each arm so that the sum would equal a target. We found that the dominant arm made a greater contribution, but only for specific directions. This was not because the dominant arm was stronger. Rather, it was less noisy. A cost that included unimanual noise and strength accounted for both direction- and handedness-dependent choices that young people made. To test whether there was a causal relationship between unimanual noise and bimanual control, we considered elderly people, whose unimanual noise is comparable in the two arms. We found that, in bimanual control, the elderly showed no preference for their dominant arm. We noninvasively stimulated the motor cortex to produce a change in unimanual strength and noise, and found a corresponding change in bimanual control. Using the noise measurements, we built a neuronal model. The model explained the anisotropic distribution of preferred directions of neurons in the monkey motor cortex and predicted that, in humans, there are changes in the number of these cortical neurons with handedness and aging. Therefore, we found that coordination can be explained by the noise and strength of each effector, where noise may be a reflection of the number of task-related neurons available for control of that effector in the motor cortex.
PMCID: PMC3905146  PMID: 24478362
aging; handedness; optimal control; signal-dependent noise; tDCS
2.  Vigor of Movements and the Cost of Time in Decision Making 
The Journal of Neuroscience  2014;34(4):1212-1223.
If we assume that the purpose of a movement is to acquire a rewarding state, the duration of the movement carries a cost because it delays acquisition of reward. For some people, passage of time carries a greater cost, as evidenced by how long they are willing to wait for a rewarding outcome. These steep discounters are considered impulsive. Is there a relationship between cost of time in decision making and cost of time in control of movements? Our theory predicts that people who are more impulsive should in general move faster than subjects who are less impulsive. To test our idea, we considered elementary voluntary movements: saccades of the eye. We found that in humans, saccadic vigor, assessed using velocity as a function of amplitude, was as much as 50% greater in one subject than another; that is, some people consistently moved their eyes with high vigor. We measured the cost of time in a decision-making task in which the same subjects were given a choice between smaller odds of success immediately and better odds if they waited. We measured how long they were willing to wait to obtain the better odds and how much they increased their wait period after they failed. We found that people that exhibited greater vigor in their movements tended to have a steep temporal discount function, as evidenced by their waiting patterns in the decision-making task. The cost of time may be shared between decision making and motor control.
PMCID: PMC3898284  PMID: 24453313
impulsivity; motor control; reward; saccade; temporal discounting; vigor
3.  Decay of motor memories in the absence of error 
When motor commands are accompanied by an unexpected outcome, the resulting error induces changes in subsequent commands. However, when errors are artificially eliminated, changes in motor commands are not sustained, but show decay. Why does the adaptation-induced change in motor output decay in the absence of error? A prominent idea is that decay reflects the stability of the memory. We show results that challenge this idea and instead suggest that motor output decays because the brain actively disengages a component of the memory. Humans adapted their reaching movements to a perturbation and were then introduced to a long period of trials in which errors were absent (error-clamp). We found that, in some subjects, motor output did not decay at the onset of the error-clamp block, but a few trials later. We manipulated the kinematics of movements in the error-clamp block and found that as movements became more similar to subjects’ natural movements in the perturbation block, the lag to decay onset became longer and eventually reached hundreds of trials. Furthermore, when there was decay in the motor output, the endpoint of decay was not zero, but a fraction of the motor memory that was last acquired. Therefore, adaptation to a perturbation installed two distinct kinds of memories: one that was disengaged when the brain detected a change in the task, and one that persisted despite it. Motor memories showed little decay in the absence of error if the brain was prevented from detecting a change in task conditions.
PMCID: PMC3712872  PMID: 23637163
motor control; motor learning; decay; error-based learning; dynamic optimization; forgetting; retention; error-clamp
4.  Motor Learning Relies on Integrated Sensory Inputs in ADHD, but Over-Selectively on Proprioception in Autism Spectrum Conditions 
Autism Research  2012;5(2):124-136.
Lay Abstract
Children with autism spectrum disorder (ASD) show deficits in development of motor skills, in addition to core deficits in social skill development. In a previous study (Haswell et al., 2009) we found that children with autism show a key difference in how they learn motor actions, with a bias for relying on joint position rather than visual feedback; further, this pattern of motor learning predicted impaired motor, imitation and social abilities. We were interested in finding out whether this altered motor learning pattern was specific to autism. To do so, we examined children with Attention Deficit Hyperactivity Disorder (ADHD), who also show deficits in motor control. Children learned a novel movement and we measured rates of motor learning, generalization patterns of motor learning, and variability of motor speed during learning. We found children with ASD show a slower rate of learning and, consistent with previous findings, an altered pattern of generalization that was predictive of impaired motor, imitation, and social impairment. In contrast, children with ADHD showed a normal rate of learning and a normal pattern of generalization; instead, they (and they alone), showed excessive variability in movement speed. The findings suggest that there is a specific pattern of altered motor learning associated with autism.
Scientific Abstract
The brain builds an association between action and sensory feedback to predict the sensory consequence of self-generated motor commands. This internal model of action is central to our ability to adapt movements, and may also play a role in our ability to learn from observing others. Recently we reported that the spatial generalization patterns that accompany adaptation of reaching movements were distinct in children with Autism Spectrum Disorder (ASD) as compared to typically developing (TD) children. To test whether the generalization patterns are specific to ASD, here we compared the patterns of adaptation to those in children with Attention Deficit Hyperactivity Disorder (ADHD). Consistent with our previous observations, we found that in ASD the motor memory showed greater than normal generalization in proprioceptive coordinates compared with both TD children and children with ADHD; children with ASD also showed slower rates of adaptation compared with both control groups. Children with ADHD did not show this excessive generalization to the proprioceptive target, but did show excessive variability in the speed of movements with an increase in the exponential distribution of responses (τ) as compared with both TD children and children with ASD. The results suggest that slower rate of adaptation and anomalous bias towards proprioceptive feedback during motor learning is characteristic of autism; whereas increased variability in execution is characteristic of ADHD.
PMCID: PMC3329587  PMID: 22359275
5.  Evidence for hyperbolic temporal discounting of reward in control of movements 
Suppose that the purpose of a movement is to place the body in a more rewarding state. In this framework, slower movements may increase accuracy and therefore improve probability of acquiring reward, but the longer durations of slow movements produce devaluation of reward. Here we hypothesize that the brain decides the vigor of a movement (duration and velocity) based on the expected discounted reward associated with that movement. We begin by showing that durations of saccades of varying amplitude can be accurately predicted by a model in which motor commands maximize expected discounted reward. This result suggests that reward is temporally discounted even in timescales of tens of milliseconds. One interpretation of temporal discounting is that the true objective of the brain is to maximize the rate of reward – which is equivalent to a specific form of hyperbolic discounting. A consequence of this idea is that the vigor of saccades should change as one alters the inter-trial intervals between movements. We find experimentally that in healthy humans, as inter-trial intervals are varied, saccade peak velocities and durations change on a trial-by-trial basis precisely as predicted by a model in which the objective is to maximize the rate of reward. Our results are inconsistent with theories in which reward is discounted exponentially. We suggest that there exists a single cost, rate of reward, which provides a unifying principle that may govern control of movements in timescales of milliseconds, as well as decision making in timescales of seconds to years.
PMCID: PMC3462010  PMID: 22915115
motor control; saccades; vigor; reward; temporal discounting
6.  Preparing to reach: selecting an adaptive long-latency feedback controller 
In a voluntary movement, the nervous system specifies not only the motor commands, but also the gains associated with reaction to sensory feedback. For example, suppose that during reaching a perturbation tends to push the hand to the left. With practice, the brain not only learns to produce commands that predictively compensate for the perturbation, but also increases the long-latency reflex gain associated with leftward displacements of the arm. That is, the brain learns a feedback controller. Here, we wondered whether during the preparatory period before the reach the brain engaged this feedback controller in anticipation of the upcoming movement. If so, its signature might be present in how the motor system responds to perturbations in the preparatory period. Humans trained on a reach task in which they adapted to a force field. During the preparatory period before the reach we measured how the arm responded to a pulse to the hand that was either in the direction of the upcoming field, or in the opposite direction. Reach adaptation produced an increase in the long-latency (45–100ms delay) feedback gains with respect to baseline, but only for perturbations that were in the same direction as the force field that subjects expected to encounter during the reach. Therefore, as the brain prepares for a reach, it loads a feedback controller specific to the upcoming reach. With adaptation, this feedback controller undergoes a change, increasing the gains for the expected sensory feedback.
PMCID: PMC3407880  PMID: 22787039
7.  Cerebellar Contributions to Reach Adaptation and Learning Sensory Consequences of Action 
The Journal of Neuroscience  2012;32(12):4230-4239.
When we use a novel tool, the motor commands may not produce the expected outcome. In healthy individuals, with practice the brain learns to alter the motor commands. This change depends critically on the cerebellum as damage to this structure impairs adaptation. However, it is unclear precisely what the cerebellum contributes to the process of adaptation in human motor learning. Is the cerebellum crucial for learning to associate motor commands with novel sensory consequences, called forward model, or is the cerebellum important for learning to associate sensory goals with novel motor commands, called inverse model? Here, we compared performance of cerebellar patients and healthy controls in a reaching task with a gradual perturbation schedule. This schedule allowed both groups to adapt their motor commands. Following training, we measured two kinds of behavior: in one case people were presented with reach targets near the direction in which they had trained. The resulting generalization patterns of patients and controls were similar, suggesting comparable inverse models. In another case, they reached without a target and reported the location of their hand. In controls the pattern of change in reported hand location was consistent with simulation results of a forward model that had learned to associate motor commands with new sensory consequences. In patients, this change was significantly smaller. Therefore, in our sample of patients we observed that while adaptation of motor commands can take place despite cerebellar damage, cerebellar integrity appears critical for learning to predict visual sensory consequences of motor commands.
PMCID: PMC3326584  PMID: 22442085
8.  Contributions of the Motor Cortex to Adaptive Control of Reaching Depend on the Perturbation Schedule 
Cerebral Cortex (New York, NY)  2010;21(7):1475-1484.
During adaptation, motor commands tend to repeat as performance plateaus. It has been hypothesized that this repetition produces plasticity in the motor cortex (M1). Here, we considered a force field reaching paradigm, varied the perturbation schedule to potentially alter the amount of repetition, and quantified the interaction between disruption of M1 using transcranial magnetic stimulation (TMS) and the schedule of perturbations. In the abrupt condition (introduction of the perturbation on a single trial followed by constant perturbation), motor output adapted rapidly and was then followed by significant repetition as performance plateaued. TMS of M1 had no effect on the rapid adaptation phase but reduced adaptation at the plateau. In the intermediate condition (introduction of the perturbation over 45 trials), disruption of M1 had no effect on the phase in which motor output changed but again impaired adaptation when performance had plateaued. Finally, when the perturbation was imposed gradually (over 240 trials), the motor commands continuously changed during adaptation and never repeated, and disruption of M1 had no effect on performance. Therefore, TMS of M1 appeared to reduce adaptation of motor commands during a specific phase of learning: when motor commands tended to repeat.
PMCID: PMC3116732  PMID: 21131448
force-field adaptation; motor control; primary motor cortex; repetition-dependent plasticity; transcranial magnetic stimulation
9.  A shared resource between declarative memory and motor memory 
The Journal of Neuroscience  2010;30(44):14817-14823.
The neural systems that support motor adaptation in humans are thought to be distinct from those that support the declarative system. Yet, during motor adaptation changes in motor commands are supported by a fast adaptive process that has important properties (rapid learning, fast decay) that are usually associated with the declarative system. The fast process can be contrasted to a slow adaptive process that also supports motor memory, but learns gradually and shows resistance to forgetting. Here we show that after people stop performing a motor task, the fast motor memory can be disrupted by a task that engages declarative memory, but the slow motor memory is immune from this interference. Furthermore, we find that the fast/declarative component plays a major role in the consolidation of the slow motor memory. Because of the competitive nature of declarative and non-declarative memory during consolidation, impairment of the fast/declarative component leads to improvements in the slow/non-declarative component. Therefore, the fast process that supports formation of motor memory is not only neurally distinct from the slow process, but it shares critical resources with the declarative memory system.
PMCID: PMC3379001  PMID: 21048140
adaptation; learning; memory; motor control; motor learning; movement
10.  Stimulation of the Human Motor Cortex Alters Generalization Patterns of Motor Learning 
The Journal of Neuroscience  2011;31(19):7102-7110.
It has been hypothesized that the generalization patterns that accompany learning carry the signatures of the neural systems that are engaged in that learning. Reach adaptation in force fields has generalization patterns that suggest primary engagement of a neural system that encodes movements in the intrinsic coordinates of joints and muscles, and lesser engagement of a neural system that encodes movements in the extrinsic coordinates of the task. Among the cortical motor areas, the intrinsic coordinate system is most prominently represented in the primary sensorimotor cortices. Here, we used transcranial direct current stimulation (tDCS) to alter mechanisms of synaptic plasticity and found that when it was applied to the motor cortex, it increased generalization in intrinsic coordinates but not extrinsic coordinates. However, when tDCS was applied to the posterior parietal cortex, it had no effects on learning or generalization in the force field task. The results suggest that during force field adaptation, the component of learning that produces generalization in intrinsic coordinates depends on the plasticity in the sensorimotor cortex.
PMCID: PMC3330486  PMID: 21562272
11.  Protection and expression of human motor memories 
When we adapt our movements to a perturbation, and then adapt to another perturbation, is the initial memory destroyed, or is it protected? Despite decades of experiments, this question remains unresolved. The confusion, in our view, is due to the fact that in every instance the approach has been to assay contents of motor memory by re-testing with the same perturbations. When performance in re-testing is the same as naïve, this is usually interpreted as the memory being destroyed. However, it is also possible that the initial memory is simply masked by the competing memory. We trained humans in a reaching task in field B, and then in field A (or washout) over an equal number of trials. To assay contents of motor memory, we used a new tool: after completion of training in A, we withheld reinforcement (i.e., reward) for a brief block of trials and then clamped movement errors to zero over a long block of trials. We found that this led to spontaneous recovery of B. That is, withholding reinforcement for the current motor output resulted in the expression of the competing memory. Therefore, adaptation followed by washout or reverse-adaptation produced competing motor memories. The protection from unlearning was unrelated to sudden changes in performance errors that might signal a contextual change, as competing memories formed even when the perturbations were introduced gradually. Rather, reinforcement appears to be a critical signal that affords protection to motor memories, and lack of reinforcement encourages retrieval of a competing memory.
PMCID: PMC3208234  PMID: 21957245
motor learning; adaptation; extinction; context; spontaneous recovery
12.  TMS perturbs saccade trajectories and unmasks an internal feedback controller for saccades 
When we applied a single pulse of transcranial magnetic stimulation (TMS) to any part of the human head during a saccadic eye movement, the ongoing eye velocity was reduced starting as early as 45ms after the TMS, and lasted around 32ms. The perturbation to the saccade trajectory was not due to a mechanical effect of the lid on the eye (e.g., from blinks). When the saccade involved coordinated movements of both the eyes and the lids, e.g., in vertical saccades, TMS produced a synchronized inhibition of the motor commands to both eye and lid muscles. The TMS induced perturbation of the eye trajectory did not show habituation with repetition, and was present in both pro- and anti-saccades. Despite the perturbation, the eye trajectory was corrected within the same saccade with compensatory motor commands that guided the eyes to the target. This within-saccade correction did not rely on visual input, suggesting that the brain monitored the oculomotor commands as the saccade unfolded, maintained a real time estimate of the position of the eyes, and corrected for the perturbation. TMS disrupted saccades regardless of the location of the coil on the head, suggesting that the coil discharge engages a non-habituating startle-like reflex system. This system affects ongoing motor commands upstream of the oculomotor neurons, possibly at the level of the superior colliculus or omnipause neurons. Therefore, a TMS pulse centrally perturbs saccadic motor commands, which are monitored possibly via efference copy, and are corrected via internal feedback.
PMCID: PMC3167087  PMID: 21832184
saccade accuracy; pause; TMS; startle; omnipause neuron; forward model
13.  Learning from Sensory and Reward Prediction Errors during Motor Adaptation 
PLoS Computational Biology  2011;7(3):e1002012.
Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.
Author Summary
It is thought that motor adaptation relies on sensory prediction errors to form an estimate of the perturbation. Here, we present evidence that motor adaptation can be driven by both sensory and reward prediction errors. We found that learning from sensory prediction error altered the predicted consequences of motor commands, leaving behind a sensory remapping, whereas learning from reward prediction error produced comparable change in motor commands, but did not produce a sensory remapping. It is possible that the neural basis of learning from sensory and reward prediction errors are distinct because they produce different generalization patterns.
PMCID: PMC3053313  PMID: 21423711
14.  Temporal discounting of reward and the cost of time in motor control 
Why do movements take a characteristic amount of time, and why do diseases that affect the reward system alter control of movements? Suppose that purpose of any movement is to position our body in a more rewarding state. People and other animals discount future reward as a hyperbolic function of time. Here, we show that across populations of people and monkeys there is a correlation between discounting of reward and control of movements. We consider saccadic eye movements and hypothesize that duration of a movement is equivalent to a delay of reward. The hyperbolic cost of this delay not only accounts for kinematics of saccades in adults, it also accounts for the faster saccades of children, who temporally discount reward more steeply. Our theory explains why saccade velocities increase when reward is elevated, and why disorders in the encoding of reward, for example in Parkinson’s disease and schizophrenia, produce changes in saccade. We show that delay of reward elevates the cost of saccades, reducing velocities. Finally, we consider coordinated movements that include motion of eyes and head and find that their kinematics are also consistent with a hyperbolic, reward dependent cost of time. Therefore, each voluntary movement carries a cost because its duration delays acquisition of reward. The cost depends on the value that the brain assigns to stimuli, and the rate at which it discounts this value in time. The motor commands that move our eyes reflect this cost of time.
PMCID: PMC2926660  PMID: 20685993
saccade; human; monkey; gaze; development; children; Parkinson’s disease; schizophrenia; reward; temporal discount; optimal control; drug abuse
15.  Cerebellar contributions to adaptive control of saccades in humans 
The cerebellum may monitor motor commands and through internal feedback corrects for anticipated errors. Saccades provide a test of this idea because these movements are completed too quickly for sensory feedback to be useful. Earlier we reported that motor commands that accelerate the eyes toward a constant amplitude target showed variability. Here, we demonstrate that this variability is not random noise, but is due to the cognitive state of the subject. Healthy people showed within saccade compensation for this variability with commands that arrived later in the same saccade. However, in people with cerebellar damage, the same variability resulted in dysmetria. This ability to correct for variability in the motor commands that initiated a saccade was a predictor of each subject’s ability to learn from endpoint errors. In a paradigm in which a target on the horizontal meridian jumped vertically during the saccade (resulting in an endpoint error), the adaptive response exhibited two timescales: a fast timescale that learned quickly from endpoint error but had poor retention, and a slow timescale that learned slowly but had strong retention. With cortical cerebellar damage, the fast timescale of adaptation was effectively absent, but the slow timescale was less impaired. Therefore the cerebellum corrects for variability in the motor commands that initiate saccades within the same movement via an adaptive response that not only exhibits strong sensitivity to previous endpoint errors, but also rapid forgetting.
PMCID: PMC2994243  PMID: 19828807
saccade adaptation; forward model; SCA-6; fatigue; saccade repetition; saccade kinematics; repetition attenuation
16.  Representation of internal models of action in the autistic brain 
Nature neuroscience  2009;12(8):970-972.
Children with autism spectrum disorder (ASD) exhibit deficits in motor control, imitation, and social function. Does a dysfunction in the neural basis of representing internal models of action contribute to these problems? We measured patterns of generalization as children learned to control a novel tool and found that the autistic brain built a stronger than normal association between self generated motor commands and proprioceptive feedback; furthermore, the greater the reliance on proprioception, the greater the child’s impairments in social function and imitation.
PMCID: PMC2740616  PMID: 19578379
17.  The intrinsic value of visual information affects saccade velocities 
Let us assume that the purpose of any movement is to position our body in a more advantageous or rewarding state. For example, we might make a saccade to foveate an image because our brain assigns an intrinsic value to the information that it expects to acquire at the endpoint of that saccade. Different images might have different intrinsic values. Optimal control theory predicts that the intrinsic value that the brain assigns to targets of saccades should be reflected in the trajectory of the saccade. That is, in anticipation of foveating a highly valued image, our brain should produce a saccade with a higher velocity and shorter duration. Here, we considered four types of images: faces, objects, inverted faces, and meaningless visual noise. Indeed, we found that reflexive saccades that were made to a laser light in anticipation of viewing an image of a face had the highest velocities and shortest durations. The intrinsic value of visual information appears to have a small but significant influence on the motor commands that guide saccades.
PMCID: PMC2771693  PMID: 19526358
Optimal control; motor control; computational neuroscience; eye movements; saccades; kinematics; image value
18.  Acquisition of internal models of motor tasks in children with autism 
Brain  2008;131(11):2894-2903.
Children with autism exhibit a host of motor disorders including poor coordination, poor tool use and delayed learning of complex motor skills like riding a tricycle. Theory suggests that one of the crucial steps in motor learning is the ability to form internal models: to predict the sensory consequences of motor commands and learn from errors to improve performance on the next attempt. The cerebellum appears to be an important site for acquisition of internal models, and indeed the development of the cerebellum is abnormal in autism. Here, we examined autistic children on a range of tasks that required a change in the motor output in response to a change in the environment. We first considered a prism adaptation task in which the visual map of the environment was shifted. The children were asked to throw balls to visual targets with and without the prism goggles. We next considered a reaching task that required moving the handle of a novel tool (a robotic arm). The tool either imposed forces on the hand or displaced the cursor associated with the handle position. In all tasks, the children with autism adapted their motor output by forming a predictive internal model, as exhibited through after-effects. Surprisingly, the rates of acquisition and washout were indistinguishable from normally developing children. Therefore, the mechanisms of acquisition and adaptation of internal models in self-generated movements appeared normal in autism. Sparing of adaptation suggests that alternative mechanisms contribute to impaired motor skill development in autism. Furthermore, the findings may have therapeutic implications, highlighting a reliable mechanism by which children with autism can most effectively alter their behaviour.
PMCID: PMC2577807  PMID: 18819989
reach adaptation; prism adaptation; motor control; autism
19.  Motor adaptation as a process of reoptimization 
Adaptation is sometimes viewed as a process where the nervous system learns to predict and cancel effects of a novel environment, returning movements to near baseline (unperturbed) conditions. An alternate view is that cancellation is not the goal of adaptation. Rather, the goal is to maximize performance in that environment. If performance criteria are well defined, theory allows one to predict the re-optimized trajectory. For example, if velocity dependent forces perturb the hand perpendicular to the direction of a reaching movement, the best reach plan is not a straight line but a curved path that appears to over-compensate for the forces. If this environment is stochastic (changing from trial to trial), the re-optimized plan should take into account this uncertainty, removing the over-compensation. If the stochastic environment is zero-mean, peak velocities should increase to allow for more time to approach the target. Finally, if one is reaching through a via-point, the optimum plan in a zero-mean deterministic environment is a smooth movement, but in a zero-mean stochastic environment is a segmented movement. We observed all of these tendencies in how people adapt to novel environments. Therefore, motor control in a novel environment is not a process of perturbation cancellation. Rather, the process resembles re-optimization: through practice in the novel environment, we learn internal models that predict sensory consequences of motor commands. Through reward based optimization, we use the internal model to search for a better movement plan to minimize implicit motor costs and maximize rewards.
PMCID: PMC2752329  PMID: 18337419
motor learning; motor adaptation; cerebellar damage; ataxia; optimal control; internal model
20.  Spontaneous recovery of motor memory during saccade adaptation 
Journal of neurophysiology  2008;99(5):2577-2583.
It is possible that motor adaptation in the timescales of minutes is supported by two distinct processes: one process that learns slowly from error but has strong retention, and another that learns rapidly from error but has poor retention. This two-state model makes the prediction that if a period of adaptation is followed by a period of reverse-adaptation, then in the subsequent period in which errors are clamped to zero (error-clamp trials) there will be a spontaneous recovery, i.e., a rebound of behavior toward the initial level of adaptation. Here we tested and confirmed this prediction during double-step, on-axis, saccade adaptation. When people adapted their saccadic gain to a magnitude other than one (adaptation) and then the gain was rapidly reversed back to one (reverse-adaptation), in the subsequent error-clamp trials (visual target placed on the fovea after the saccade) the gain reverted toward the initially adapted value and then gradually reverted toward normal. We estimated that the fast system was about 20 times more sensitive to error than the slow system, but had a time constant of 28 seconds while the slow system had a time constant of nearly 8 minutes. Therefore, short-term adaptive mechanisms that maintain accuracy of saccades rely on a memory system that has characteristics of a multi-state process with a logarithmic distribution of timescales.
PMCID: PMC2733835  PMID: 18353917
Saccade adaptation; motor memory; computational neuroscience; extinction
21.  Adaptive control of saccades via internal feedback 
Ballistic movements like saccades require the brain to generate motor commands without the benefit of sensory feedback. Despite this, saccades are remarkably accurate. Theory suggests that this accuracy arises because the brain relies on an internal forward model that monitors the motor commands, predicts their sensory consequences, and corrects eye trajectory midflight. If control of saccades relies on a forward model, then the forward model should adapt whenever its predictions fail to match sensory feedback at the end of the movement. Using optimal feedback control theory, we predicted how this adaptation should alter saccade trajectories. We trained subjects on a paradigm where the horizontal target jumped vertically during the saccade. With training, the final position of the saccade moved toward the second target. However, saccades became increasingly curved, i.e., suboptimal, as oculomotor commands were corrected online to steer the eye toward the second target. The adaptive response had two components: 1) the motor commands that initiated the saccades changed slowly, aiming the saccade closer to the jumped target. The adaptation of these earliest motor commands displayed little forgetting during the rest periods. 2) Late in saccade trajectory, another adaptive response steered it still closer to the jumped target, producing curvature. Adaptation of these late motor commands showed near complete forgetting during the rest periods. The two components adapted at different timescales, with the late-acting component displaying much faster rates. It appears that in controlling saccades, the brain relies on an internal feedback that has the characteristics of a fast adapting forward model.
PMCID: PMC2733833  PMID: 18337410
saccade adaptation; forward model; internal feedback; optimal control; curved saccades; fatigue
22.  Online processing of uncertain information in visuomotor control 
Our sensory observations represent a delayed, noisy estimate of the environment. Delay causes instability and noise causes uncertainty. To deal with these problems, theory suggests that the brain’s processing of sensory information should be probabilistic: to start a movement or to alter it mid-flight, our brain should make predictions about the near future of sensory states, and then continuously integrate the delayed sensory measures with predictions to form an estimate of the current state. To test the predictions of this theory, we asked participants to reach to the center of a blurry target. With increased uncertainty about the target, reach reaction times increased. Occasionally, we changed the position of the target or its blurriness during the reach. We found that the motor response to a given 2nd target was influenced by the uncertainty about the 1st target. The specific trajectories of motor responses were consistent with predictions of a “minimum variance” state estimator. That is, the motor output that the brain programmed to start a reaching movement or correct it mid-flight was a continuous combination of two streams of information: a stream that predicted the near future of the state of the environment, and a stream that provided a delayed measurement of that state.
PMCID: PMC2729125  PMID: 18971478
reaction time; uncertainty; auto-pilot; integration; computational model; motor control
23.  Generalization as a Behavioral Window to the Neural Mechanisms of Learning Internal Models 
Human movement science  2004;23(5):543-568.
In generating motor commands, the brain seems to rely on internal models that predict physical dynamics of the limb and the external world. How does the brain compute an internal model? Which neural structures are involved? We consider a task where a force field is applied to the hand, altering the physical dynamics of reaching. Behavioral measures suggest that as the brain adapts to the field, it maps desired sensory states of the arm into estimates of force. If this neural computation is performed via a population code, i.e., via a set of bases, then activity fields of the bases dictate a generalization function that uses errors experienced in a given state to influence performance in any other state. The patterns of generalization suggest that the bases have activity fields that are directionally tuned, but directional tuning may be bimodal. Limb positions as well as contextual cues multiplicatively modulate the gain of tuning. These properties are consistent with the activity fields of cells in the motor cortex and the cerebellum. We suggest that activity fields of cells in these motor regions dictate the way we learn to represent internal models of limb dynamics.
PMCID: PMC2722915  PMID: 15589621
reaching movements; motor control; adaptation; motor learning; motor cortex; cerebellum; system identification; trial-to-trial analysis
24.  Changes in control of saccades during gain adaptation 
In a typical short-term saccadic adaptation protocol, the target moves intra-saccadically either toward (gain-down) or away (gain-up) from initial fixation, causing the saccade to complete with an endpoint error. A central question is how the motor system adapts in response to this error: are the motor commands changed to bring the eyes to a different goal, akin to a remapping of the target, or is adaptation focused on the processes that monitor the ongoing motor commands and correct them midflight, akin to changes that act via internal feedback? Here, we found that in the gain-down paradigm, the brain learned to produce a smaller amplitude saccade by altering the saccade's trajectory. The adapted saccades had reduced peak velocities, reduced accelerations, shallower decelerations, and increased durations compared to a control saccade of equal amplitude. These changes were consistent with a change in an internal feedback that acted as a forward model. On the other hand, in the gain-up paradigm the brain learned to produce a larger amplitude saccade with trajectories that were identical to those of control saccades of equal amplitude. Therefore, whereas the gain-down paradigm appeared to induce adaptation via an internal feedback that controlled saccades midflight, gain-up induced adaptation primarily via target remapping. Our simulations explained that for each condition, the specific adaptation produced a saccade that brought the eyes to the target with the smallest motor costs.
PMCID: PMC2632981  PMID: 19091981
Saccade adaptation; saccade kinematics; forward models; optimal control; computational neuroscience; Sensorimotor
25.  Sequential neural changes during motor learning in schizophrenia 
Psychiatry research  2008;163(1):1-12.
Positron emission tomography (PET) was used to investigate differences in neural plasticity associated with learning a unique motor task in patients with schizophrenia and healthy volunteers. Working with a robotic manipulandum, subjects learned reaching movements in a force field. Visual cues were provided to guide the reaching movements. PET rCBF measures were acquired while participants learned the motor skill over successive runs. The groups did not differ in behavioral performance but did differ in their rCBF activity patterns. Healthy volunteers displayed blood flow increases in primary motor cortex and supplementary motor area with motor learning. The patients with schizophrenia displayed an increase in the primary visual cortex with motor learning. Changes in these regions were positively correlated with changes in each group’s motor accuracy, respectively. This is the first study to employ a unique arm-reaching motor learning test to assess neural plasticity during multiple phases of motor learning in patients with schizophrenia. The patients may have an inability to rapidly tune motor cortical neural populations to a preferred direction. The visual system, however, appears to be highly compensated in schizophrenia and the inability to rapidly modulate the motor cortex may be substantially corrected by the schizophrenic group’s visuomotor adaptations.
PMCID: PMC2562703  PMID: 18407471
PET; Visuomotor; Arm reaching; Neural plasticity; rCBF; Motor learning

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