The Binding of Learning to Action in Motor Adaptation
[['When learning to swim , the proper stroke motion is usually taught on the pool deck .', 'Although a student might seem to have mastered this motion on dry land , upon entering the water she will have difficulty in accurately reproducing it underwater .', 'However , after many laps , the student eventually learns to produce the pattern of motor output that leads to the proper stroke motion while swimming .', 'This learning occurs via the formation of internal models of the physical dynamics experienced which allow the programming of movement to contend with the dynamics of the environment [1]–[4] .', 'These internal models have been shown to predict the dynamics of the environment as a function of motion rather than as a function of time [5]–[8] – a strategy that makes sense in light of the viscoelastic and inertial physics of our own limbs and the objects we interact with .', 'Consequently , the neural plasticity which underlies this learning must establish associations between motion state ( i . e . , position and velocity vectors ) and motor output which can counteract environmental forces .', 'Although the existence of these associations has been well established , the mechanism by which they form is not yet understood .', 'How does this state-dependent learning arise during the course of motor adaptation ?', 'One possibility is that on individual trials , an internal model of the environment is updated based on a combination of the errors experienced and the motion plans that led to those errors .', 'Another possibility is that internal models are updated based on errors experienced in combination with the actual motion states associated with those errors .', 'It is remarkable that previous work on motor learning in neural systems has widely assumed the former [4] , [9]–[16] , despite the fact that direct evidence for this hypothesis is scant .', 'The idea that learning is associated with the motion that was planned ( plan-referenced learning ) is especially pervasive in the learning rules of the algorithms that have been proposed to model the process of adaptation in the neuromotor learning literature [4] , [9] , [11]–[12] , [15] , however it is difficult to find work that addresses the validity of this assumption , explores its implications or provides a clear rationale for its use .', 'The machine learning community has developed , in parallel , a series of algorithms for updating internal models in robotic systems .', 'Interestingly , these algorithms almost uniformly involve learning rules in which internal models are updated based on a combination of the errors experienced and the actual motion associated with those errors ( motion-referenced learning ) rather than the motions that were planned [17]–[21] .', 'The choice of these learning rules is grounded in the idea that adaptive changes should be provably stable in the sense that , under a set of reasonable assumptions , updated internal models should never result in worse performance [17]–[21] .', 'Here we ask the question: Do the associations between motor output and motion state formed during human motor learning arise from adaptation based on planned or actual motions ?', 'The answer to this question is important not only for theories of motor control , and issues of stability during learning , but also because knowledge of how associations are formed during motor learning can be leveraged to improve the efficiency of training procedures .', 'Motor adaptation can be described as the process of tuning motor output to reduce the errors between plan and action .', 'Thus the associations between motion state and motor output formed during this process result from the way that responsibility for these errors is assigned .', 'This is known as a credit assignment problem .', 'This problem can be posited as the task of assigning blame after an error is experienced to the set of actions that would be most likely to give rise to similar errors in the future .', 'This set of actions could then be modified in order to improve performance in subsequent trials .', 'Viewed in this way , the distinction between plan-referenced learning ( PRL ) and motion-referenced learning ( MRL ) corresponds to whether the blame for motor errors should be assigned to the planned versus actual motion .', 'Consequently , the amount of adaptation on a given trial will be determined by the magnitude of the error , however the location of the adaptation ( which future motions will benefit from the adaptation ) will be determined by the credit assignment mechanism .', 'Here we studied the generalization of motor adaptation to untrained conditions in order to elucidate the credit assignment mechanism used by the CNS , and then used our understanding of this mechanism to design a training paradigm that takes advantage of it to improve the efficiency of motor adaptation .'], ['The adaptations that would occur at different stages of training for reaching arm movements in a velocity-dependent force-field ( FF ) for the PRL and MRL credit assignment hypotheses are shown in Figure 1 .', 'The green shaded region around the planned motion – which is essentially straight toward the target for short ( 10 cm ) movements [22] – represents the space of future motions which would benefit from the adaptation to the greatest degree under PRL ( Figure 1A ) .', 'Alternatively , each red shaded region represents the space of future motions which would benefit maximally under MRL .', 'A more direct visualization of the adaptive changes predicted by each credit assignment hypothesis can be made by representing motion and the resulting adaptation in velocity-space rather than position-space , since the adaptation to the velocity-dependent dynamics studied in the current series of experiments is believed to be mediated by an internal model largely composed of velocity-dependent motor primitives [8] , [10] , [12]–[13] , [23] .', 'These primitives are the learning elements which contribute to the compensatory motor output ( i . e . , compensatory force ) in a velocity-dependent manner .', 'Figure 1B shows how individual motor primitives would adapt based on PRL versus MRL credit assignment early on in training .', 'Here each circle represents a single motor primitive ( centered at its preferred velocity ) with a color intensity denoting the amount of adaptation that would arise from the illustrated trial .', 'The left and right panels of Figure 1B show the adaptations predicted by PRL ( green ) and MRL ( red ) , respectively .', 'As in Figure 1A , adaptation is centered on the planned motion for PRL and centered on the actual motion for MRL .', 'As training proceeds over the course of several trials , the activation levels of the adapted primitives would continue to increase .', 'This continued increase in activation ( not illustrated ) leads to increased compensatory force , resulting in greater compensation of the external dynamics and thus straighter trajectories .', 'Note that the adapted primitives would be noticeably different for the two credit assignment hypotheses early in training , but would overlap late in training as force compensation increases and the planned and actual motions converge as illustrated in Figure 1A .', 'Given the different implications that the PRL and MRL credit assignment mechanisms have for motor adaptation , we can assess which one is favored by the CNS by asking a simple question: After training , which motions gain the most benefit from the induced adaptation ?', 'The motions that were planned or the motions that were experienced ?', 'Since the mechanism for credit assignment determines which motions will benefit from adaptation on a particular trial , we studied how motor adaptation to a single target direction generalizes to neighboring motion directions .', 'If a particular motion is trained , the pattern of generalization can be viewed as a record of the history of credit-assignment for the errors experienced during a training period .', 'Specifically , the amount of generalization in the directions neighboring the trained movement constitutes the set of actions that the motor system believes should be adapted based on the history of errors experienced .', 'Therefore , PRL and MRL should give rise to different patterns of generalization .', 'In order to cleanly distinguish between these hypotheses , we designed an experiment in which the planned motion and the actual motion were maintained to be distinct from one another during the entire dataset so that the patterns of generalization predicted by PRL vs . MRL would be very different from one another .', 'This is a challenge because , training a motor adaptation generally results in improved performance such that the actual motion converges onto the planned motion , and such a scenario could hamper the ability to clearly distinguish between the PRL and MRL hypotheses .', 'Thus , we designed an experiment in which actual motion would not converge onto planned motion during the course of training , resulting in enduring differences between the predictions of these two hypotheses .', 'To accomplish this , subjects were exposed to a training period consisting of short , successive blocks of movements towards a single target location with a force-field ( FF ) that alternated between clockwise ( CW ) and counterclockwise ( CCW ) directions from block to block ( see Figure 2A ) .', 'The magnitudes of the CW and CCW FFs were , respectively , 9 and −9 N/ ( m/s ) .', 'In these FFs , the peak force perturbations were 2 . 7 and −2 . 7 N , respectively , for an average movement with a peak speed of 0 . 3 m/s .', 'The FF blocks were short enough ( 7±2 trials ) that neither the CW nor the CCW FF could be learned very well before unlearning with the opposite FF occurred .', 'After subjects were exposed to a number of these interfering FF cycles , we measured the generalization of adaptation to untrained movement directions with error-clamp ( EC ) trials ( see Materials and Methods for details ) .', 'The predictions of PRL and MRL are strikingly different for this experiment .', 'For the PRL hypothesis , since the adaptation is associated with motor primitives centered at the same target direction for both FFs ( Figure 2B top panel , blue and orange traces ) , the balanced exposure to these opposite FFs would lead to cancellation of the CW and CCW FF learning resulting in near zero adaptation at the trained target direction and the adjacent directions ( Figure 2B , dashed green trace ) .', 'Note that although target locations are identical between CW and CCW FF trials , the actual movement directions differ .', 'The CW FF perturbs motion towards smaller movement angles whereas the CCW FF does the opposite .', 'Therefore , MRL predicts that smaller movement angles would be preferentially associated with adaptation appropriate for the CW FF ( blue trace in the bottom panel of Figure 2B ) , whereas higher movement angles would be preferentially associated with adaptation appropriate for the CCW FF ( orange trace in the bottom panel of Figure 2B ) .', 'This would lead to the bimodal pattern of generalization illustrated in Figure 2B ( red dashed trace ) .', 'We trained one group of subjects in this FF interference paradigm at a target location of 270° .', 'We found that target directions smaller than the training direction consistently display generalization appropriate for the CW FF ( negative ) whereas target directions greater than the training direction display generalization appropriate for the CCW FF ( positive ) .', 'This is consistent with the bimodal generalization pattern predicted by MRL ( compare the blue and red traces in Figure 2C: r\u200a=\u200a0 . 92 , F ( 1 , 7 ) =\u200a36 . 87 , p<0 . 001 ) and quite different from the essentially flat pattern predicted by PRL ( green trace ) .', 'Correspondingly , we found the adaptation levels at the target directions corresponding to the peaks of the predicted generalization pattern ( −30° and +30° , see Figure 2C ) to be significantly different from one another ( t11\u200a=\u200a7 . 26 , p<9×10−6 ) and from zero ( t11\u200a=\u200a5 . 95 , p<5×10−5 for −30° , and t11\u200a=\u200a3 . 89 , p<0 . 002 for +30° ) .', 'These results provide direct evidence for MRL by matching the complex pattern of generalization predicted by it .', 'In our experiment we balanced the direction of the FF that was presented before testing generalization , nevertheless , we noticed a small bias in the generalization function at the training direction consistent with a bias in adaptation level that we observed during the training period ( see Figure S1 and Text S1 ) .', 'This bias is compatible with other results showing somewhat faster learning for a CW FF [8] .', 'In order to eliminate the possibility that this bias or the target location we chose for training ( 270° ) might have somehow contributed to the generalization pattern we observed in the data , we trained a second group of subjects in a version of this experiment that was designed to eliminate the bias and provide training at another target location ( 60° ) .', 'We eliminated the bias by unbalancing the number of CW versus CCW FF trials in each cycle in this second group of subjects ( see Text S1 ) .', 'We found that the close match between the pattern of generalization that these subjects displayed ( Figure 2C , grey trace ) and the pattern predicted by MRL persisted under these conditions ( r\u200a=\u200a0 . 93 , F ( 1 , 7 ) =\u200a42 . 61 , p<0 . 001 ) .', 'Correspondingly , the adaptation levels at −30° and +30° were significantly different from each other ( t9\u200a=\u200a5 . 37 , p<3×10−4 ) , and significantly different from zero ( t9\u200a=\u200a3 . 72 , p<0 . 003 for −30° , and t9\u200a=\u200a4 . 38 , p<9×10−4 for +30° ) .', 'Together , these results provide compelling evidence for MRL as the mechanism for credit assignment in motor adaptation .', 'We note that Equations 3 and 4 used for our simulations incorporate local motor primitives that are functions of the initial movement direction ( θ ) rather than of the full time series of the velocity vectors encountered during each trial .', 'This might seem an inappropriate choice since , as we discussed above , velocity-dependent motor primitives are thought to underlie the learning of velocity-dependent dynamics [8] , [10] , [12]–[13] , [23] .', 'However this approximation is a good one when movements are approximately straight , which is essentially the case for the first 400 ms of the movements considered in our study .', 'This approximation , of course , breaks down at the end of the movement when the initial movement direction no longer describes the velocities experienced .', 'However , the amplitudes of the velocity vectors during the end-movement correction are quite low and so the unmodeled spread of learning to the actual motion experienced in this correction phase should have relatively little effect since at low velocities , viscous dynamics have small consequences .', 'This effect can be visualized in the left panel of Figure 1B which shows that the end-movement correction which has a velocity vector that points to the second quadrant would only excite velocity-dependent primitives near the origin under MRL .', 'Note that the separation of the peaks in the bimodal generalization pattern predicted by MRL ( red dashed line in Figure 2C ) results from the size of the errors experienced during training .', 'Consequently , larger force-field perturbations which induce larger errors would result in greater separation between the peaks .', 'However , the separation between the peaks ( about 60° ) is predicted to be greater than the separation between the average errors experienced in the two force-fields ( about 25° ) .', 'There are two reasons for this .', 'The first is that more adaptation occurs on trials with larger errors than those with smaller errors , skewing the center of adaptation for each force-field outwardly from the mean experienced error .', 'The second reason is illustrated in the lower panel of Figure 2B: When the patterns of generalization for the positive and negative force-fields are summed , resulting in a bimodal generalization pattern for MRL , the peaks of this bimodal generalization pattern ( red ) are separated by an even greater distance than the peaks of the positive ( orange ) and negative ( blue ) components because the amount of cancellation between these components is greater at movement directions corresponding to smaller rather than larger errors resulting in further outward skew .', 'Previous work has attempted to measure the generalization functions ( GFs ) associated with learning a single FF .', 'MRL predicts that these GFs will be shifted toward the motion directions experienced during training .', 'Many of these studies have estimated GFs from complex datasets using a system identification framework [10] , [12]–[13] .', 'However the implementation of this framework assumed PRL in these studies , thus preventing a straightforward interpretation of their results .', 'In one study [24] a simpler generalization experiment was conducted , in which subjects were trained with a single FF to a single target location , after which the resulting GF was measured .', 'Because the actual motions approached the planned motions late in training , the shifts predicted by MRL would be subtle .', 'Furthermore , the ability to detect shifts in the generalization function was hampered by a coarse sampling of the generalization function ( 45° ) .', 'Nevertheless , careful inspection of these GFs consistently reveals subtle shifts towards the motions experienced during training as predicted by MRL .', 'However , it is difficult to be certain whether if the shifts observed in this study result from MRL rather than innate biases in generalization functions because only a single FF direction was studied .', 'Innate biases might stem from biomechanical asymmetries or direction-related biases in adaptation .', 'We therefore performed a pair of single-target , single-FF experiments in order to compare the shifts in generalization associated with opposite FFs .', 'The results of these experiments confirm the existence of subtle but significant shifts in generalization [25] .', 'The magnitudes and the directions of these shifts are consistent with the MRL hypothesis [25] .', 'Insights into the mechanisms for learning in the CNS can provide a platform for creating training procedures that leverage these insights to improve the rate of learning – an important goal for both motor skill training and neurologic rehabilitation .', 'With our new understanding of how the CNS solves the credit assignment problem , we looked into the possibility of designing a novel training paradigm to take advantage of this knowledge .', "A key consequence of plan-referenced learning is that this mechanism for credit assignment would result in a match between what is learned and what is commanded on the next trial if the same motion plan is repeated from one trial to the next during training – like when aiming a dart at the bull's eye repeatedly .", 'In contrast , motion-referenced learning would result in a mismatch .', 'Motion-referenced learning , therefore , predicts that the process of training an accurate movement to a given target location in a novel dynamic environment would be inefficient if that target were repeatedly presented at the same location during training ( single-target training , STT ) as illustrated in Figure 3 .', 'This inefficiency arises because the motion experienced during training does not coincide with the motion that is to be learned , resulting in limited overlap between the motion-referenced learning that occurs and the learning that is desired .', 'The aforementioned inefficiency can be ameliorated by a paradigm which continually changes the locations of the targets presented during the training period as shown in Figure 3 , second column .', 'In this training paradigm , target directions would be shifted from one trial to the next so that the actual motion experienced repeatedly lines up with the motion to be learned .', 'For the CW FF depicted in Figure 3 , this corresponds to left-shifted training ( LST ) .', 'Initial target locations are placed with large leftward shifts with respect to the desired learning direction – in anticipation of the large rightward initial errors with respect to the target location .', 'These leftward target shifts are then gradually reduced as learning proceeds and errors become smaller , in order to maintain alignment between the actual motion experienced and the movement to be learned .', 'The MRL hypothesis predicts that the LST training paradigm should produce faster learning than the standard STT paradigm used in previous motor adaptation studies in which a single target direction was trained [24] , [26] .', 'We tested this idea by comparing the learning curves associated with these training paradigms for adaptation to a clockwise viscous curl force-field .', 'A different group of subjects was studied on each paradigm to avoid the effects of savings [27]–[29] .', 'As a control for a possible increase in attention associated with changing target locations in the LST paradigm , we tested a third group of subjects with a right-shifted training ( RST ) paradigm .', 'Here targets were shifted to the right , mirroring the target positions in the LST paradigm .', 'The MRL hypothesis would predict slower learning for RST than STT or LST because right-shifted targets in a rightward pushing force-field would result in reaching movements even farther away from the desired learning direction than those expected in STT ( see Figure 3 , third column ) .', 'In contrast the PRL hypothesis would predict fastest learning for the STT paradigm and identical learning rates for the LST and RST paradigms because the STT paradigm creates perfect alignment between the desired learning and the planned motion whereas the LST and RST paradigms create misalignments between the desired learning direction and planned motion that are opposite in direction but equal in magnitude .', 'We used a FF magnitude of 22 . 5 N/ ( m/s ) for these experiments – 2 . 5 times the magnitude used in Experiment 1 – in order to magnify the various misalignments discussed above .', 'In all three paradigms , we measured learning at the desired learning direction ( 90° ) by pseudo-randomly interspersing 90° error-clamp trials among the training trials with an average frequency of 20% .', 'We first collected data from a subset of subjects in the STT paradigm in order to estimate the evolution of directional errors across trials .', 'We used this pattern of directional errors to determine the target shifts that would produce good alignment between experienced motion and desired learning direction for the LST paradigm ( see Materials and Methods ) .', 'As shown in Figure 4A , we obtained a good match between motion direction and the desired learning direction ( 90° ) throughout the training period for the LST paradigm , so that misalignment between these directions was dramatically reduced compared to the STT paradigm .', 'Correspondingly , the misalignment between motion direction and the desired learning direction was about twice as great for RST than for STT .', 'The plots shown in Figure 4B illustrate how the adaptation patterns predicted by MRL and PRL would evolve as training proceeds for the training paradigms discussed above .', 'Note that adaptation spreads across a limited range of movement directions consistent with local generalization [24]–[26] , but the alignment between adaptation and the desired learning direction ( 90° ) varies from one paradigm to another ( STT vs . LST vs . RST ) , and from one credit assignment hypothesis to another ( PRL vs . MRL ) .', 'The darkened dots which highlight a slice through these plots at 90° illustrate the amount of adaptation associated with the desired learning direction .', 'These simulations show that the PRL hypothesis predicts that in the STT paradigm , credit assignment will be perfectly aligned with the desired learning direction ( 90° ) throughout training .', 'PRL also predicts an equal but opposite pattern of misalignments between credit assignment and desired learning for the LST and RST paradigms ( Figure 4B ) .', 'These misalignments are initially large but become attenuated during the course of the training because planned and actual motions converge .', 'This results in simulated learning rates that are highest for the STT paradigm and lower , but identical , for the LST and RST paradigms under PRL ( Figure 4B–C ) .', 'In contrast , the simulations for the MRL hypothesis show perfect alignment between the credit assignment and the desired learning direction for the LST paradigm .', 'For STT , the MRL-based simulations show a gross misalignment between the credit assignment and the training direction .', 'For RST , the misalignment is even greater ( Figure 4B ) .', 'This results in learning rates that are predicted to be greatest for the LST paradigm , followed by the STT and RST paradigms , respectively ( Figure 4B , D ) .', 'As with the PRL simulations , the misalignments become attenuated as training proceeds .', 'Our experimental data show a clear difference between the learning curves obtained for the three training paradigms in the early stages of training ( first three EC trials; one-way ANOVA , F ( 2 , 87 ) =\u200a14 . 57 , p<4×10−6 ) .', 'The LST group displays the highest adaptation levels and the RST group displays the lowest adaptation levels as shown in Figure 4E .', 'In particular , the LST group displayed an 86% increase in adaptation levels on the first EC trial and a 52% increase over the first three EC trials , whereas the RST group displayed a 59% decrease in adaptation levels compared to STT over the first three EC trials in the training period .', 'Post-hoc comparisons between groups over the first three EC trials indicate that the LST group showed significantly greater learning than the RST group ( t58\u200a=\u200a−5 . 05 , p<3×10−6 ) .', 'This result is in keeping with the MRL prediction , but defies the PRL prediction of equal learning rates for these groups .', 'Our data also shows that the LST group displays significantly greater learning than the STT group ( t58\u200a=\u200a−2 . 17 , p<0 . 02 ) , in keeping with the MRL prediction , but opposing the PRL prediction of a greater learning rate for STT .', 'We also find that the STT group displays significantly greater learning than the RST group ( t58\u200a=\u200a−3 . 90 , p<2×10−4 ) , corroborating the group order predicted by the MRL hypothesis .', 'These findings provide additional support for motion-referenced learning and demonstrate that a training paradigm that is designed to leverage knowledge about the mechanism for credit assignment can improve learning rates compared to standard training procedures .', 'Inspection of the learning curve for the RST group reveals that the adaptation for the first EC trial after exposure to the FF actually dips a bit below zero .', 'MRL predicts reduced learning for this group but would not predict opposite learning , consistent with the finding that the adaption level at this point , although nominally less than zero , is not significantly so ( t27\u200a=\u200a−2 . 01 , p>0 . 05 ) .', 'Additionally , we note that the third-to-last error-clamp trial in the baseline ( which is illustrated along with the full learning curve in Figure S2 ) displays an adaptation coefficient which dips below the average baseline and falls within the error bars of the first point in the RST learning curve , suggesting that the latter is not entirely outside the range of the data .', 'Despite the differences in learning rate predicted by MRL-based credit assignment , angular errors should decrease as the training period proceeds .', 'This results in reduced misalignment between prescribed and actual motion directions for the STT and RST groups , leading to a predicted convergence of the adaptation levels for all three groups ( see Figure S2 and Text S1 ) .', 'Our data bears out this prediction: despite significant differences between groups early in the training period , we find no significant difference between groups late in the training period ( last three EC trials; one-way ANOVA , F ( 2 , 87 ) =\u200a0 . 23 , p>0 . 05 ) .', 'In addition , although we have shown that the MRL-based training paradigm ( LST ) increases the rate of adaptation , our results do not provide any information on the long-term retention for this adaptation .', 'Further studies would be required to assess if the retention of the motor memories acquired using an MRL-based training paradigm is greater than that of memories acquired using single-target training paradigms .'], ['Despite the lack of direct evidence in support of it , plan-referenced learning has been widely assumed in the motor adaptation literature , particularly in modeling work in which a credit assignment scheme must be chosen , even if implicitly so , in order for a learning rule to be defined [4] , [9]–[16] .', 'Interestingly , Wolpert and Kawato ( 1998 ) assumed a hybrid credit assignment scheme: PRL for inverse-model learning and MRL for forward-model learning [4] .', 'In principle , PRL is attractive because adaptation referenced to the previously planned motion would have the greatest effect on the same movement if it were repeated .', 'In fact , Donchin et al . ( 2003 ) , which models motor adaptation with a PRL learning rule , contains what the authors maintain is a proof that PRL-based learning is optimal in their supplementary materials [10] .', 'However inspection of this proof reveals that its derivation is based on the assumption that motor adaptation acts to maximize the benefit that would be accrued if the same movement were repeated .', 'In other words , this proof investigated what the optimal credit assignment procedure should be for STT and found that PRL maximizes the benefit of motor adaptation for STT .', 'Since PRL is optimal for STT , MRL must be suboptimal for STT ( as our simulations predict; see Figure 4B–D ) .', 'This suggests that some training procedure other than STT would be optimal for MRL , and our data show that , for a clockwise FF , left-shifted training ( LST ) is indeed more effective than STT .', 'Effectively , Donchin et al . ( 2003 ) assumed that a credit assignment procedure optimized for performance on STT would be used by the nervous system .', 'Here we show that this is not the case .', 'Instead , the error-dependent learning that occurs on a particular trial is referenced to the actual motion experienced on that trial rather than the planned motion , and as a result , STT produces slower learning than another training procedure ( LST ) .', 'Thus the human motor system does not adapt with the mechanism that would have the greatest effect on the same movement if it were repeated .', 'Why would this be ?', 'The problem with PRL is that the dynamics experienced are generally functions of actual rather than planned motion .', 'For example , the dynamics experienced from moving a small dense mass would be proportional to the actual rather than the planned acceleration of that mass .', 'Note that the dynamics that subjects experienced in our experiments were also dependent on the actual motion state , i . e . , the force was based on the velocity of the actual rather than the planned motion .', 'The key consequence of this state dependence is that since the force pattern experienced during a particular motion does not reflect the planned motion ( because it reflects the actual motion ) , the force pattern that would have been experienced if the planned motion were achieved is unknown .', 'This means that , in principle , the error between the current motor output and the environmental dynamics acting on the planned motion adaptation is also unknown .', 'Because this error is unknown , no learning rule for adaptation referenced to the planned motion can be guaranteed to reduce it .', 'If , however , errors are small enough so that the dynamics experienced in actual and desired trajectories would be very similar to each other , plan-referenced learning schemes could converge because these schemes essentially assume equality between these dynamics .', 'On the other hand , if errors are sufficiently large , using such a credit assignment scheme might result in unstable learning which does not converge on the desired motor output .', 'Clearly , a credit assignment scheme 
['Introduction', 'Results', 'Discussion', 'Materials and Methods']
['In motor tasks , errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future .', 'It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned .', 'Here we show that this is not the case .', 'Instead , we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial .', 'The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it .', 'This reflects the idea that actual rather than planned motions are assigned ‘credit’ for motor errors because , in a computational sense , the maximal adaptive response would be associated with the condition credited with the error .', 'We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans .', 'We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion , with maximal generalization observed where actual motions were clustered .', 'We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action , can improve adaptation rates by greater than 50% .', 'Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation , and they suggest ways to optimize their use .']
['Einstein once said: “Insanity is doing the same thing over and over again and expecting different results” .', 'However , task repetition is generally the default procedure for training a motor skill .', 'This can work because motor learning ensures that repetition of the same motor task will lead to actions that are different , as errors are reduced and motor skill improves .', 'However , here we show that task repetition , although not “insane” , is inefficient .', 'The machine learning algorithms used to control motion in robotics adapt the movement that was actually made rather than the planned movement in order to assure stable learning .', 'In contrast , it had been widely assumed that neural motor systems adapt based on the planned rather than the actual movement .', 'If this were the case , task repetition would be an efficient training procedure .', 'Here we studied the mechanisms for motor adaptation in humans and found that , like in robotic learning , the adaptation that we experience is associated with the actual movement .', 'This finding led to the design of an improved training procedure that avoids task repetition .', 'Instead , this procedure continually adjusts the movement goal in order to drive participants to experience the correct movement , even if initially by accident , leading to an over 50% improvement in the motor adaptation rate .']
['motor systems', 'medicine', 'computational neuroscience', 'biology', 'neuroscience', 'learning and memory', 'physiotherapy and rehabilitation']