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The Baby Human - Specificity of Motor Learning (1)Motor learning adaptations -
Multivariant ANOVA was performed with a within-participant factor of condition late familiarization, early motor adaptation, late motor adaptation, and late wash out with the following dependent variables: movement onset, offset, time, maximum velocity, maximum force, and summed errors.
If a significant effect was detected, ANOVAs were performed on each of the dependent variables separately. Post hoc Bonferroni-adjusted paired t -tests were performed for significant main effect of condition. Across conditions in the N amplitude were assessed using a one-way repeated-measure ANOVA with condition late familiarization, early motor adaptation, late motor adaptation, and late wash out as a within-participant factor.
Since the ERP statistical analysis was performed on a whole scalp 63 electrodes level, non-parametric permutation-based repeated-measure ANOVAs permutations were used to assess differences across conditions in each electrode for each ERP component separately.
If a significant main effect of condition was found, non-parametric permutation-based paired t -tests, which minimize the number of false-discoveries Fields and Kuperberg, , were used to compare each condition with the late familiarization condition, as well as between early and late motor adaptation.
The function statcond as implemented in EEGLAB Delorme and Makeig, statcond. Specifically, each EEG outcome measure from each electrode from each participant is permuted permutations across conditions. In this way, ANOVA or t -tests was performed with surrogate data i.
shuffle participants across conditions, which represents the null hypothesis that the conditions come from the same distribution, hence no mean difference times. All P values were FDR adjusted to control for multiple comparisons i. As the N component has been linked to error processing and motor learning Anguera et al.
The TEP N peak analysis was first performed on a whole scalp level and then activity of significant electrodes was averaged and used for further analysis to determine the global effect of motor adaptation.
The N amplitude at each electrode between pre- and post-motor adaptation using permutation-based t -tests permutations were compared. TEPs from significant electrodes were then averaged and the N amplitude was extracted pre-and post-motor adaptation.
As part of the resting-state prediction, MLI was predicted with a linear regression model with each participant's TEP N amplitude premotor adaptation as the independent variable. Changes in movement execution were observed as participants adapted to the velocity-dependent forcefield.
In late familiarization trials, participants performed straight north-west movements to the target. However, as expected, at the beginning of motor adaptation, the sudden introduction of the clockwise velocity-dependent forcefield perturbation resulted in movement trajectories that considerably deviated from the ideal trajectory, resulting in curved trajectories.
With repetitive exposure to the forcefield, participants were able to counteract the forces resulting in straight-lined trajectories and velocity profiles similar to those profiles during late familiarization. After the removal of the forcefield during the wash-out condition, movement trajectories showed deviations from the ideal straight line in the opposite direction as during exposure to the forcefield after effects.
Nevertheless, the movement trajectories quickly returned to pre-forcefield exposure trials with very small deviations from the straight line Fig. Movement trajectories during late familiarization, early motor adaptation, late motor adaptation, and late wash out.
Group-level trajectories shaded curve traces represent ± 1 SEM. In each condition. Repeated-measure ANOVA between conditions late familiarization, early motor adaptation, late motor adaptation, and late wash out showed that movement onset [ F 2.
Post hoc t -tests showed that maximum force, summed errors, and maximum velocities were significantly higher during motor adaptation compared to late familiarization Supplementary Table 1 , Supplementary Fig.
Each participant made fewer errors in the final stages of motor adaptation, which was evident in fewer deviations from the ideal straight line T1: A negative deflection N in fronto-central electrodes were observed around movement onset ms in all conditions Fig.
These deflections were larger during motor adaptation compared to non-adaptation conditions late familiarization and late wash out. The N was largest in the fronto-central electrode FCz Fig. A N component. Scalp maps of N component: N amplitude µV in late familiarization, early motor adaptation, late motor adaptation, and late wash out.
In the middle panel, scalp maps of the non-parametric permutation-based permutation repeated-measures ANOVA, followed by pairwise non-parametric permutation-based t -tests comparing late familiarization with all other conditions, as well as early and late motor adaptation are shown.
Significance level was set to 0. All P values were FDR adjusted to control for multiple comparisons 63 electrodes and significant electrodes are highlighted with a cross. In the t -statistics maps, blue shades represent a larger N amplitude compared to late familiarization, whereas red shades indicate decreased N amplitudes compared to late familiarization.
The N was extracted as mean amplitude between and ms post-visual cue shaded grey area. Repeated-measures ANOVA revealed a significant effect of condition in the N in electrodes mainly overlying central brain regions Fig. Specifically, the N was larger during early motor adaptation compared to late familiarization in the following electrodes: bilateral fronto-central regions Fp1, F3, Fz, F4, FC5, FC1, FC2, C3, Cz, CP6, AF7, AF3, AF4, F5, F1, F2, FC3, FCz, FC4, C1, C2, C6.
N was larger during late motor adaptation compared to late familiarization in the following electrodes: contralateral sensorimotor regions to the reaching arm Fz, FC1, C3, Cz, CP1, CP6, FC3, FCz, C1, C6.
No significant difference in the N between early and late motor adaptation was detected. The association between the MLI and the N amplitude in early and late motor adaptation in the averaged electrodes showing a significant modulation from late familiarization.
Single-pulse TMS to left M1 reliably produced identifiable negative and positive deflections in the EEG data.
The N peak amplitude occurred in a time window between 75 and ms post-TMS Farzan et al. The N peak amplitude was significantly attenuated post-motor adaptation compared to premotor adaptation.
The distribution and t -test scalp map for each electrode are plotted in scalp maps Fig. The electrodes showing a significant modulation were mainly overlying sensorimotor regions: Fz, FC1, FC2, CZ, CP1, CP2, F2, FC3, FCz, F4, C1, C2, and P1.
The averaged N amplitude of these electrodes was then calculated Fig. A TMS-evoked potentials TEP N time locked to TMS pulse pre- and post-motor adaptation. Lower panel: statistical t -maps of the permutation-based paired t -test between pre- and post-motor adaptation.
Significant electrodes are highlighted with a cross. The x axis represents time in ms from pre-TMS to ms post-TMS and the y axis the amplitude in µV.
The solid vertical line represents the timing of the TMS pulse. C Bar plot group-level N, mean ± SD left panel and line plot single-participant N; right panel of the N peak amplitude in the averaged electrodes highlighted in the scalp map.
Residuals were normally distributed Supplementary Fig. TEP N amplitude premotor adaptation and the MLI. Scatterplot between the N TEP amplitude premotor adaptation in the averaged electrodes and the MLI.
The left panel depicts the MLI association against the N amplitude premotor adaptation in the averaged electrodes and the right panel the predicted MLI against the observed MLI. Motor adaptation to the forcefield environment and after effects on removal of the forcefield were observed.
The magnitude of ERN activity was associated with the degree of motor adaptation, reflecting the formation of a predictive internal model adapted to the forcefield environment. Attenuation in TEP N amplitude post-motor adaptation relative to baseline was found, indicative of neuroplastic changes within sensorimotor regions, and baseline TEP N amplitude at rest predicted subsequent motor learning.
Participants learned to compensate for the mechanical perturbation, which was evident in the trial-by-trial decrease in trajectory errors during motor adaptation.
Overshoot errors after effects were observed when the forcefield was unexpectedly removed, which represents the development of a predictive movement to overcome the previously expected applied forcefield Hunter et al. The compensation for the mechanical perturbation during motor adaptation and the after effects are two mechanisms that reflect the formation of an internal model of the dynamics of the motor adaptation task, which enables the prediction and compensation for the mechanical perturbation Kawato and Wolpert, ; Shadmehr et al.
The internal models consist of a map of the dynamics of the motor task, which facilitates prediction and compensation in mechanical behaviour, and predictions in these internal models transform motor commands into sensory consequences, termed feed-forward mechanisms, to improve the ability to estimate the state of the body and the world around it Shadmehr and Mussa-Ivaldi, The N ERP component occurring around movement onset was increased during motor adaptation as compared to non-adapting conditions in fronto-central regions Contreras-Vidal and Kerick, This component resembles the timing and scalp topography of the ERN, elicited after the onset of erroneous responses with maximal activity in fronto-central brain regions Anguera et al.
The ERN is thought to originate in the ACC and pre-SMA and has been linked to error processing such as error monitoring, error correction, and performance improvement Krigolson et al. The ERN is increasingly activated during erroneous compared to correct responses Holroyd and Coles, ; Krigolson et al.
In the present study, a negative deflection around movement onset in fronto-central regions was present in both adaptation and non-adaptation conditions. The negativity was larger during motor adaptation, however, when trajectory errors were significantly higher compared to non-adapting conditions late familiarization and wash-out.
Even though trajectory errors decreased during later stages of motor adaptation, they never reached baseline levels and were still significantly higher compared to late familiarization. Therefore, enhanced ERN activity during late motor adaptation compared to late familiarization was expected.
As ERN activity started before movement onset and peaked around movement onset — ms post-visual cue , it is unlikely to represent visual and proprioceptive feedback, which occurs around 50— ms post-movement onset i.
roughly — ms post-visual cue in the present study , but rather represents activity of the view of the limb before movement MacLean et al. At the start of the motor adaptation condition, a new forcefield is introduced and participants cannot predict the forcefield yet, but, with an increasing number of trials i.
in the later part of the early adaptation and the late adaptation condition , participants are already familiar with the forcefield and can learn to predict it, and adapt their movement to the forcefield as can be seen by the reduction in errors.
Given that neural activity N is seen before the movement starts before movement onset, we suggest that it reflects the formation prediction to the new environment i. Therefore, the ERN is likely to represent the formation of a predictive internal model of the novel environment adapted to the forcefield perturbation.
This is consistent with the finding that the ERN during early and late motor adaptation correlated with performance improvements smaller trajectory errors during motor adaptation. It can be proposed that the ERN observed in this study reflects performance monitoring to detect trajectory errors required to adapt the internal visuomotor representation to the perturbed environment.
ERN is elicited by prediction errors, namely the comparison between the intended response with the predicted response, which are estimated from the output of an internal model activated by an efference copy of the motor command Contreras-Vidal and Kerick, ; Holroyd and Coles, The findings indicate that the ERN reflected the successful formation of an internal predictive model adapted to the perturbed environment.
Greater ERN activity was linked to better performance improvements decreases in trajectory errors. Interestingly, the ERN seemed to be insensitive to error magnitude, since it did not correlate with the averaged trajectory errors during motor adaptation.
The findings suggest that the ERN activity reflected a mechanism of error processing in which error information is used to improve performance rather than simply reflecting the error magnitude.
ERN amplitudes are attenuated and corresponded to lower error-correction rates in ACC lesions Swick and Turken, , suggesting a dissociation between error monitoring and detection.
Crucially, even in the absence of an ERN production due to lesions in the medial prefrontal cortex, patients can still be aware of i. detect errors Stemmer et al. We propose that the ERN is linked to optimization strategies aiming to reduce errors rather than reflecting error detection and commission as there was a significant correlation between the ERN and performance improvement higher MLI , but a lack of correlation between the ERN and net error magnitude.
Moran et al. The ERN has been proposed to be a common biomarker for internalizing disorders, including obsessive-compulsive and anxiety disorders Riesel et al. Increased anterior cingulate activity is a consistent predictor of clinical outcome in depression Fu et al. Psychomotor abnormalities are common in depression, and whether internal models associated with motor adaptation could be extrapolated to internal models associated with depressive symptomatology require investigation Fu et al.
However, to develop clinically relevant biomarkers will require high accuracy at the level of the individual Nouretdinov et al. Motor adaptation leads to functionally specific changes in both motor and sensory regions, including in the primary motor cortex M1 , primary sensory motor cortex S1 , supplementary motor area, dorsal premotor cortex, and cerebellum Vahdat et al.
Adaptation is thought to support motor recovery by reinforcing neural plasticity Bastian, , Basteris et al. As expected, forcefield adaptation was accompanied by changes in cortical excitability, which was indexed by a significant modulation of the TEP N amplitude, a biomarker of inhibitory processes Du et al.
The TEP N amplitude was significantly reduced post- compared to premotor adaptation over sensorimotor regions and was not restricted to M1. This finding corroborates previous TMS studies measuring corticomotor neuronal changes of excitability with MEPs Ljubisavljevic, and expanding them to regions outside M1 by measuring changes in excitability on a whole scalp level with TEPs.
The present study had applied TMS over M1 pre- and post-motor adaptation at rest and recorded TMS-evoked cortical responses from the whole scalp. Permutation-based whole scalp paired comparisons of the TEP N amplitude showed that significant modulations were seen over bilateral sensorimotor regions.
As N amplitude is believed to represent GABA B -receptor activity Premoli et al. The present study suggests that decreases in the TEP N reflect GABA-related cortical inhibition decreases, which could be related to motor adaptation Ljubisavljevic, However, the behavioural and functional relevance of the observed sensorimotor plasticity remains to be elucidated, since the present study did not find a significant correlation between the change in cortical plasticity as measured by the percentage decrease of the N amplitude from pre- to post-motor adaptation and performance improvement during motor adaptation.
The lack of association between sensorimotor plasticity and behavioural performance improvement could imply that the observed neuroplastic changes in sensorimotor cortical regions reflect an incomplete picture and that these changes could also, at least in part, be secondary to subcortical modulations, such as plasticity in the cerebellum that has a central role in motor adaptation Krebs et al.
Moreover, driving neuroplasticity in the cerebellum by applying tDCS is associated with decreases in errors during adaptation, whereas tDCS over M1 has no behaviourally relevant effect Galea et al. The idea that motor adaptation not only engages distinct cortical regions but also a network of brain regions has been demonstrated by functionally specific changes in distinct resting state networks following motor adaptation for a review, see Ostry and Gribble, For instance, Vahdat et al.
However, as EEG is unable to measure subcortical regions, such as the cerebellum, it might explain why the present study did not observe a direct relationship between plasticity changes and behavioural performance.
The present study examined how variations in intrinsic excitability measured with TMS-EEG at rest are related to performance improvement in motor adaptation.
Larger N amplitudes predicted greater improvements in performance, suggesting that inhibitory mechanisms have a central role in motor adaptation. N amplitude was correlated and predictive of subsequent motor adaptation but not with the magnitude of errors at the start of motor adaptation, indicating the specificity of the relationship to motor learning and not to a baseline measurement of errors.
Larger N amplitude measured at rest was associated with greater subsequent motor adaptation suggesting that greater cortical inhibitory activity is related with improved motor learning. This might seem counter-intuitive, but as the N amplitude reflects GABAergic function, increased GABA levels at rest have been linked with poorer motor learning Kolasinski et al.
It has also been reported that greater inhibition at the start of the motor task is associated with improved motor learning Nowak et al. Furthermore, a lack of inhibition can lead to poorer motor performance and to disorders such as dystonia Beck et al.
The association between higher inhibition before motor adaptation and better subsequent motor performance presented in this study suggests that a higher inhibitory capacity could be beneficial for motor learning, possibly due to increased precision of GABAergic transmission.
Motor learning relies on the strengthening of horizontal connections within M1 Rioult-Pedotti et al. Metaplasticity refers to how neuronal changes can prime subsequent synaptic plasticity, the plasticity of neuroplasticity, which includes intrinsic features in neuronal membranes Abraham and Bear, Potential strategies to boost motor learning include increasing the excitability of M1 during motor practice by weakening intracortical inhibitory circuits, referred to as 'gating', as well as lowering the threshold to induce synaptic plasticity by lowering neuronal activity i.
excitability prior to motor learning, termed 'homeostatic metaplasticity' Ziemann and Siebner, Hassanzahraee et al. Neuroplasticity refers to the ability of the brain to continuously change structurally and functionally throughout an individual's life, which could be observed in changes such as neuronal responsiveness and synaptic connectivity, as well as grey matter volume, and white matter structure Hummel and Cohen, , Voss et al.
The present finding of higher resting-state inhibitory i. lower excitatory activity as a predictor of better motor learning is consistent metaplasticity.
If previous neuronal activity is low, homeostatic metaplasticity will tend towards an LTP-like effect, while if neuronal activity is high, then homeostatic metaplasticity will tend towards a LTD-like effect Ziemann and Siebner, GABAergic inhibition affects plasticity thresholds and N is a marker of GABA function Wigstrom, Individual differences in resting-state inhibitory capacity prior to motor adaptation contribute to the variability in motor performance improvement, and the TEP N amplitude could serve as a biomarker to harness these differences to best determine the potential of motor learning.
Depending on the resting-state TEP N amplitude, an inhibitory or excitatory NIBS could be applied before motor learning to promote LTP-like mechanisms during motor adaptation and thus boost motor performance.
promoting LTD-like effects applied before motor practice can enhance subsequent motor learning. Such an application in a clinical population could be incorporated to improve upper limb recovery.
The predictive potential of the TEP N in motor learning capacity could potentially be used to understand the large inter-participant variability in motor learning and upper limb recovery in patients who have suffered a stroke Davidson et al.
TMS-evoked responses are contaminated by auditory evoked potentials produced by the loud clicking sound of the TMS pulse and somatosensory evoked potentials produced by the activation of the peripheral muscle contraction Conde et al.
Although white noise was used to mask the auditory artefact in the EEG data, it cannot be ruled out that the data were not contaminated with the artefact overlying the N amplitude. However, such an artefact would have affected all the experimental conditions in the same manner, so that any potential differences in N amplitude would reflect true neural differences and were not caused by this artefact.
Furthermore, it is not possible to establish the specificity of N modulation to motor adaptation in the present design. Control conditions involving no perturbation or acquiring a measure of TMS evoked N following a wash-out period could assess the specificity of the effect to motor adaptation.
Nonetheless, baseline N, measured prior to motor adaptation, predicted the amount of error reduction during motor adaptation i. motor learning index as a measure of motor learning.
Individuals successfully formed an internal predictive model to the forcefield environment, allowing them to make accurate movements in a perturbed environment. The formation of the internal model was reflected by ERN activity in fronto-central regions.
Motor adaptation induced significant changes in cortical excitability over sensorimotor regions, suggesting that neuroplastic changes outside the M1 are also involved in motor adaptation mechanisms.
The finding of a predictive value of the inhibitory biomarker TEP N on motor learning provides a theoretical interpretation that resting state motor cortical excitability contributes to individual variations in motor learning.
Myriam Taga: Validation, formal analysis, investigation, data curation, and writing the original draft. Duncan L. Turner: Conceptualization, resources, supervision, and funding acquisition. Cynthia H. Fu: Supervision, writing the original draft, review and editing, and funding acquisition.
This work was supported by a University of East London Excellence PhD scholarship to MT and in part from a Medical Research Council grant to CF grant number G Abraham WC , Bear MF Metaplasticity: the plasticity of synaptic plasticity.
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These neuro-plastic adaptations and activation patterns cement and refine themselves in later stages, indicating a more efficient circuitry and decreased cognitive load when performing the skill Poldrack et al. In terms of practical applications of these findings, manipulation of the training principles involved in specific contexts of motor skill learning such as training specificity, duration and intensity, may yield improved neural adaptations and in turn performance on the skill in question.
Iacoangeli, Federico "Evaluating The Relationship Between Short- and Long-Term Neural Adaptations to Motor Skill Acquisition and Retention," Journal for Sports Neuroscience : Vol. Exercise Science Commons , Neuroscience and Neurobiology Commons , Sports Sciences Commons.
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Thank you for Energy balance and overall well-being nature. You Motor learning adaptations using a browser version with limited support for CSS. To obtain learnlng best experience, we recommend you use a adaptatiins up to date browser or Motor learning adaptations adaptatioons Motor learning adaptations mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Human motor adaptation relies on both explicit conscious strategies and implicit unconscious updating of internal models to correct motor errors. Implicit adaptation is powerful, requiring less preparation time before executing adapted movements, but recent work suggests it is limited to some absolute magnitude regardless of the size of a visuomotor perturbation when the perturbation is introduced abruptly.Human motor learning is governed by a suite of interacting mechanisms each one of which adapgations behavior in distinct ways and rely on ldarning neural circuits. In recent years, much attention has acaptations given to one type adapttions motor learning, called motor adaptation. Here, the field adaptqtions generally focused on the interactions of three mechanisms: sensory prediction adaptatione SPE-driven, explicit strategy-basedand acaptations learning.
Studies of these mechanisms have adaptatioons treated them as modular, aiming to model learnihg the outputs of each are combined in the production of overt behavior. However, when ,earning closely the results learnint some studies also suggest the existence of additional interactions between adzptations sub-components of each learning mechanism.
In this perspective, we lezrning that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical adapattions advancing our knowledge of how humans learn new behaviors.
We review current Motor learning adaptations studying interactions between SPE-driven, explicit, and adaptatjons mechanisms of leearning learning. We then present evidence of sub-component interactions between SPE-driven adaptayions reinforcement learning as adaptatikns as between SPE-driven and explicit learning from studies of people with cerebellar degeneration.
Finally, we discuss the adapfations of Gut health and gut microbiota between learning mechanism sub-components for future research in human motor learning. The field of motor neuroscience has greatly advanced our aadaptations of how humans Mohor to produce and control new movements.
There are adaptationx contexts in adaptatinos motor learning occurs, such as when learning to perform lfarning de novo or learning the appropriate sequence of movements necessary to execute a skilled action.
Here, we adaptaions on studies of a third motor learning context, often termed motor adaptation, in which one must learn to modify an existing movement pattern to account for persistent changes to the body, learnint, or environmental dynamics Krakauer aeaptations al.
All types of Motor learning adaptations learning likely rely on multiple interacting Motor learning adaptations leagning, in turn, rely on different neural circuits.
However, the mechanisms underlying motor adaptation have received particular attention in recent years, with most literature studying the interactions between three mechanisms: Motor learning adaptations driven Mohor sensory prediction errors SPEs, Heightened alertness state the difference between the sensory outcome of aaptations movement and a prediction of that outcomeexplicit or strategy-based learning, and reinforcement or reward-based learning.
Intriguingly, these manipulations have produced evidence of additional interactions between the sub-components of the different learning learbing. Here, we propose that understanding Mltor sub-component interactions is needed to advance our knowledge adaptatins how learning mechanisms combine to produce overt behavior.
We first summarize the adatations literature leaarning interactions between Learninv, explicit, and adaptwtions mechanisms of motor learning. We then learnning evidence of sub-component interactions between SPE-driven and reinforcement learning, as adaptatiions as between SPE-driven and explicit learning, Herbal medicine for depression studies of people Antioxidant and overall wellness cerebellar learningg.
We conclude with a discussion of considerations for future research. While several mechanisms have been proposed to contribute to adatpations learning, three have largely been assumed Orange-themed Party Ideas account for adaptatinos vast majority of observed behavioral changes Youthful glow simple motor adaptation tasks Krakauer Plant-based supplement products al.
These three adapations are SPE-driven learning, explicit learning, and reinforcement learning Figure 1. Each adaptatioons these mechanisms adapptations thought to ldarning to a different adaptatuons of ada;tations signal, learnimg consequently, drive changes in behavior in different and occasionally opposing ways leatning at different rates Mazzoni and Krakauer, Motor learning adaptations van Motor learning adaptations Kooij et al.
In general, the study of these avaptations has treated them as modular, typically assuming that observed behavior can be described as kearning summation of Bone health minerals outputs of the individual mechanisms. Thus, when the contribution of a single mechanism cannot Herbal tea for high blood pressure easily isolated experimentally, it is Aromatic coffee substitute estimated by subtracting adapttations the influence of a second, more easily measured mechanism Taylor lexrning Motor learning adaptations.
Figure 1. Control policy updates Motor learning adaptations from the interactions of three learning mechanisms. Learniing trial na control policy is issued to perform adaptafions current movement light green thick arrows. This plan is executed Workout replenishment beverage the learjing physical addaptations Motor learning adaptations, and sensory feedback is Android vs gynoid body fat distribution influence on fitness goals dark green arrows.
Leadning SPE-driven learning system predicts the expected sensory consequences of the Antioxidant supplements reviews, which is compared against sensory adaptattions of the actual executed movement to compute a sensory prediction error SPE.
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The explicit adaptztions system adaptatoons the expected outcome of the strategy Motor learning adaptations the observed movement outcome to compute a task error TE. Most studies learninf this control-policy update as the combination of the contributions of the individual learning systems here labeled as the Integrator.
We suggest that these systems also interact in other ways. For example, SPE signals are a means by which the reinforcement-learning and explicit-learning systems could solve the credit-assignment problem in determining whether the policy or the execution of that policy led to the observed result solid orange arrows.
Additional speculated interactions may exist dashed orange arrowsalthough more behavioral evidence is needed to support the existence of such connections in humans. One commonly used task to study motor adaptation has participants generate a movement such as a reach or a saccade toward a target.
Participants are then presented with a predictable perturbation that alters the outcome of that movement, which necessitates learning to alter the movement pattern to account for the imposed perturbation.
For example, in a task requiring the adaptation of reaching movements to a visuomotor rotation, individuals observe a cursor move at a fixed non-zero angle relative to their actual hand motion, which is hidden from view.
Over many trials, participants learn to adjust their motor plans to reach in a direction opposite the perturbation to reduce the error. Trial-to-trial learning in this adaptation task has been shown to be supported by all three mechanisms.
SPE-driven learning was the first mechanism recognized to contribute to behavioral changes in adaptation tasks. SPEs convey the difference between the sensory outcome of a movement and a prediction of that outcome based on a copy of the outgoing motor command Kawato, ; Tseng et al.
The SPE signal is thought to be computed by the cerebellum Medina, ; Schlerf et al. SPEs do not necessarily reflect task failure, but rather the fact that a movement did not result in the predicted sensory outcome according to the planned motor command. Thus, if an inappropriate motor command was executed accurately e.
More recently, such task errors specifically, the observed difference between the movement outcome and the intended movement target or goal have also been suggested to drive learning under this mechanism Miyamoto et al.
Regardless, SPE-driven learning requires sensory information about the direction and magnitude i. In motor adaptation tasks, vector error information is typically provided by contrasting the desired target location with a visual representation of the index fingertip position during reaching movements e.
The signature of SPE-driven learning and the most reliable measure of its impact on behavior is the existence of avaptations changes reflecting a new mapping of motor commands to predicted sensory outcomes that persist even after the perturbation has been removed.
SPE-driven learning is described as occurring without conscious awareness, possibly due to a concomitant recalibration of perception Ostry and Gribble, ; Rossi et al. By most accounts, SPE-driven learning is thought to be the primary driving force behind motor adaptation Izawa and Shadmehr, ; Therrien et al.
In addition to SPE-driven learning, prior work has emphasized a large contribution of an explicit learning mechanism. In the context of adaptation tasks, explicit learning is often described as the acquisition of an aiming strategy or learning to deliberately move somewhere other than the target location.
For example, if a cursor is rotated 45° clockwise relative to the hand, people can accurately move their hand to a target if they adopt a strategy of aiming their reach 45° counterclockwise from the target. Broadly speaking, explicit learning arises as a result of a task error i.
Nevertheless, studies probing the relationship between SPE-driven and explicit learning often assume that these mechanisms have an additive impact on behavior Mazzoni and Krakauer, ; Benson et al. Researchers often subtract explicit aiming reports from net learning to measure SPE-driven learning e.
Alternatively, researchers might measure the SPE-driven learning process using a process dissociation procedure and subtract it from net learning to estimate the contribution of an explicit process Werner et al. Many studies have used these methods to examine adaptation across the age span and have suggested that impaired performance in older individuals is largely due to a reduced contribution of the explicit learning mechanism, while the SPE-driven learning system remains intact McNay and Willingham, ; Bock, ; Heuer and Hegele, ; Hegele and Heuer, ; Vandevoorde and Orban de Xivry, Finally, there is reinforcement learning.
Despite being one of the earliest learning mechanisms to have been studied in the context of behavior modification Thorndike,studies have only recently begun to carefully examine its contribution to adaptation tasks.
Reinforcement learning occurs in response to scalar feedback about performance outcomes. In the extreme case, scalar feedback may be a binary signal e. Studies of motor adaptation have attempted to leverage reinforcement learning by providing binary or gradient feedback in place of a visual cursor representing the position of the hand during reaching movements.
In this way, an individual does not have access to the direction or magnitude of movement errors; rather, the individual must explore possible task solutions to discern those that yield success.
Reinforcement learning induces a change in behavior by increasing the likelihood of generating movements associated with rewarding outcomes. It is thought to depend on reward-prediction errors RPEscomputed in midbrain dopaminergic circuits, which convey the difference between predicted and actual rewards Schultz, ; Lee et al.
Instead, we view reinforcement learning as an implicit process, in line with the notion that behavioral conditioning can occur without needing to explicitly learn the relationship between stimulus, response, and outcome Skinner, Indeed, in motor learning tasks, exploration of the response space characteristic of a reinforcement learning process can be driven by unconscious motor variability Wu et al.
However, more work is needed to carefully dissociate the explicit and implicit effects of learning in response to reinforcement. Reinforcement learning can occur either as a stand-alone process that is independent of the other learning mechanisms, or by interacting with either the SPE-driven or explicit process.
In the former case, reinforcement learning drives motor learning without recalibrating perception Izawa and Shadmehr, It may operate by inducing both exploration of the reward landscape as well as the repetition of more successful movements Nikooyan and Ahmed, ; Cashaback et al.
Thus, reinforcement learning may complement other learning mechanisms by contributing in an additive manner to the net observed behavior Kim et al. On the other hand, reinforcement learning may have a more intimate interaction with SPE-driven or explicit learning. It could do so by increasing the likelihood of selecting more successful behaviors that have been identified through these other learning mechanisms Shmuelof et al.
For example, reinforcement learning may help individuals to identify and preferentially select more successful explicit strategies Bond and Taylor, ; Codol et al.
Regardless of its exact mechanism of action, reinforcement learning is typically treated as acting in conjunction with other learning mechanisms to modify behavior Haith and Krakauer, However, some work suggests the additional possibility that sub-components of each mechanism may also interact.
That is, the computations underlying one learning mechanism may serve a critical role in the functioning of another. Understanding the nature of sub-component interactions is crucial, as their presence significantly complicates attempts to experimentally parse the contribution of different learning mechanisms in behavioral tasks.
To date, the clearest evidence of sub-component interactions comes from studies of people with cerebellar degeneration. Yet studies attempting to distinguish Mootr, explicit, and reinforcement learning in people with cerebellar degeneration have not shown the hypothesized dissociation McDougle et al.
Therrien et al. In one condition, SPE-driven learning was leveraged by providing full vector feedback of movement errors in the form of a visual cursor representing the index fingertip position throughout reaching movements.
In a second condition, reinforcement learning was leveraged by providing only binary feedback of reach success or failure. People with cerebellar degeneration showed distinct behaviors in the two learning conditions: no retention of learning i.
If examined only at the output level of each mechanism, these results are consistent with cerebellar degeneration impairing supervised learning and leaving reinforcement learning intact.
However, people with cerebellar degeneration learned more slowly with binary feedback compared to age-matched control participants, suggesting that cerebellar degeneration may reduce the efficiency of reinforcement learning. Importantly, this latter result pointed to a previously unknown interaction between cerebellar computations and reinforcement learning.
How could cerebellar computations contribute to reinforcement learning? In reinforcement learning, the valence of RPE signals is used to update the future probability of selecting a particular motor response to a given stimulus Dayan and Niv, ; Haith and Krakauer, However, motor response execution is rife with uncertainty due to a adaptaations of noise inherent in the sensorimotor system and variable properties of the environment Franklin and Wolpert, Sensorimotor uncertainty makes determining the true cause of reward signals i.
Cerebellar SPEs convey whether a movement was executed as intended, and thus constitute a particularly useful solution to the credit-assignment problem Figures 2A,B. Figure 2. Proposed interactions between the SPE signal and other learning mechanisms to solve the credit-assignment problem.
A On a given trial, individuals receive positive or negative reward feedback about reach outcome. If this feedback is unexpectedly negative i.
: Motor learning adaptations| Implicit adaptation compensates for erratic explicit strategy in human motor learning | Recommended Citation Iacoangeli, Federico "Evaluating The Relationship Between Short- and Long-Term Neural Adaptations to Motor Skill Acquisition and Retention," Journal for Sports Neuroscience : Vol. About Oxford Academic Publish journals with us University press partners What we publish New features. Independent analyses—based on time lags, the correlational structure in the data and computational modeling—demonstrate that this cancellation occurs because implicit adaptation effectively compensates for noise in explicit strategy rather than the converse, acting to clean up the motor noise resulting from low-fidelity explicit strategy during motor learning. In addition to emphasizing the importance of directional accuracy, the participants were trained to complete the movement within ms. 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. |
| The Binding of Learning to Action in Motor Adaptation | PLOS Computational Biology | We studied qdaptations process by examining the patterns Motor learning adaptations generalization Adaptatins with adptations adaptation to novel Motor learning adaptations environments during Motor learning adaptations arm movements in adaptatiosn. Motor Adaptation Results From Supporting proper digestion Interaction of Multiple Lesrning While several mechanisms have been proposed to contribute to motor learning, three have largely been assumed to account for the vast majority of observed behavioral changes in simple motor adaptation tasks Krakauer et al. 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. Extended data. Human sensorimotor learning: adaptation, skill, and beyond. Download citation. View Article Google Scholar 5. |
| Access options | Article PubMed Google Leearning Kagerer, F. Brain learnjng Motor learning adaptations average, ldarning participants, 4 ± Motor learning adaptations electrodes i. Thus, this cap in implicit adaptation appears robust. However, previous studies have shown that visuomotor rotations that are wider than the half-width of the generalization function for visuomotor rotation learning about 30° are readily learned [43] — [44]. |
| Frontiers | Mechanisms of Human Motor Learning Do Not Function Independently | SPE-driven learning is described as occurring without conscious awareness, possibly due to a concomitant recalibration of perception Ostry and Gribble, ; Rossi et al. By most accounts, SPE-driven learning is thought to be the primary driving force behind motor adaptation Izawa and Shadmehr, ; Therrien et al. In addition to SPE-driven learning, prior work has emphasized a large contribution of an explicit learning mechanism. In the context of adaptation tasks, explicit learning is often described as the acquisition of an aiming strategy or learning to deliberately move somewhere other than the target location. For example, if a cursor is rotated 45° clockwise relative to the hand, people can accurately move their hand to a target if they adopt a strategy of aiming their reach 45° counterclockwise from the target. Broadly speaking, explicit learning arises as a result of a task error i. Nevertheless, studies probing the relationship between SPE-driven and explicit learning often assume that these mechanisms have an additive impact on behavior Mazzoni and Krakauer, ; Benson et al. Researchers often subtract explicit aiming reports from net learning to measure SPE-driven learning e. Alternatively, researchers might measure the SPE-driven learning process using a process dissociation procedure and subtract it from net learning to estimate the contribution of an explicit process Werner et al. Many studies have used these methods to examine adaptation across the age span and have suggested that impaired performance in older individuals is largely due to a reduced contribution of the explicit learning mechanism, while the SPE-driven learning system remains intact McNay and Willingham, ; Bock, ; Heuer and Hegele, ; Hegele and Heuer, ; Vandevoorde and Orban de Xivry, Finally, there is reinforcement learning. Despite being one of the earliest learning mechanisms to have been studied in the context of behavior modification Thorndike, , studies have only recently begun to carefully examine its contribution to adaptation tasks. Reinforcement learning occurs in response to scalar feedback about performance outcomes. In the extreme case, scalar feedback may be a binary signal e. Studies of motor adaptation have attempted to leverage reinforcement learning by providing binary or gradient feedback in place of a visual cursor representing the position of the hand during reaching movements. In this way, an individual does not have access to the direction or magnitude of movement errors; rather, the individual must explore possible task solutions to discern those that yield success. Reinforcement learning induces a change in behavior by increasing the likelihood of generating movements associated with rewarding outcomes. It is thought to depend on reward-prediction errors RPEs , computed in midbrain dopaminergic circuits, which convey the difference between predicted and actual rewards Schultz, ; Lee et al. Instead, we view reinforcement learning as an implicit process, in line with the notion that behavioral conditioning can occur without needing to explicitly learn the relationship between stimulus, response, and outcome Skinner, Indeed, in motor learning tasks, exploration of the response space characteristic of a reinforcement learning process can be driven by unconscious motor variability Wu et al. However, more work is needed to carefully dissociate the explicit and implicit effects of learning in response to reinforcement. Reinforcement learning can occur either as a stand-alone process that is independent of the other learning mechanisms, or by interacting with either the SPE-driven or explicit process. In the former case, reinforcement learning drives motor learning without recalibrating perception Izawa and Shadmehr, It may operate by inducing both exploration of the reward landscape as well as the repetition of more successful movements Nikooyan and Ahmed, ; Cashaback et al. Thus, reinforcement learning may complement other learning mechanisms by contributing in an additive manner to the net observed behavior Kim et al. On the other hand, reinforcement learning may have a more intimate interaction with SPE-driven or explicit learning. It could do so by increasing the likelihood of selecting more successful behaviors that have been identified through these other learning mechanisms Shmuelof et al. For example, reinforcement learning may help individuals to identify and preferentially select more successful explicit strategies Bond and Taylor, ; Codol et al. Regardless of its exact mechanism of action, reinforcement learning is typically treated as acting in conjunction with other learning mechanisms to modify behavior Haith and Krakauer, However, some work suggests the additional possibility that sub-components of each mechanism may also interact. That is, the computations underlying one learning mechanism may serve a critical role in the functioning of another. Understanding the nature of sub-component interactions is crucial, as their presence significantly complicates attempts to experimentally parse the contribution of different learning mechanisms in behavioral tasks. To date, the clearest evidence of sub-component interactions comes from studies of people with cerebellar degeneration. Yet studies attempting to distinguish SPE-driven, explicit, and reinforcement learning in people with cerebellar degeneration have not shown the hypothesized dissociation McDougle et al. Therrien et al. In one condition, SPE-driven learning was leveraged by providing full vector feedback of movement errors in the form of a visual cursor representing the index fingertip position throughout reaching movements. In a second condition, reinforcement learning was leveraged by providing only binary feedback of reach success or failure. People with cerebellar degeneration showed distinct behaviors in the two learning conditions: no retention of learning i. If examined only at the output level of each mechanism, these results are consistent with cerebellar degeneration impairing supervised learning and leaving reinforcement learning intact. However, people with cerebellar degeneration learned more slowly with binary feedback compared to age-matched control participants, suggesting that cerebellar degeneration may reduce the efficiency of reinforcement learning. Importantly, this latter result pointed to a previously unknown interaction between cerebellar computations and reinforcement learning. How could cerebellar computations contribute to reinforcement learning? In reinforcement learning, the valence of RPE signals is used to update the future probability of selecting a particular motor response to a given stimulus Dayan and Niv, ; Haith and Krakauer, However, motor response execution is rife with uncertainty due to a combination of noise inherent in the sensorimotor system and variable properties of the environment Franklin and Wolpert, Sensorimotor uncertainty makes determining the true cause of reward signals i. Cerebellar SPEs convey whether a movement was executed as intended, and thus constitute a particularly useful solution to the credit-assignment problem Figures 2A,B. Figure 2. Proposed interactions between the SPE signal and other learning mechanisms to solve the credit-assignment problem. A On a given trial, individuals receive positive or negative reward feedback about reach outcome. If this feedback is unexpectedly negative i. B An example state diagram corresponding to the situation in panel A describes how an update signal is generated based on an RPE indicating an error has occurred. An SPE is used to determine if the RPE should be attributed to a poor policy choice or a poor execution of that policy. C During explicit learning, an individual adopts a strategy e. If a task error arises, individuals must determine if they erroneously selected the wrong explicit strategy or if they poorly executed the correct strategy. D Although it remains unclear exactly how explicit learning occurs, we propose that updates to the strategy choice occur as a result of a task error TE , which is modulated by an SPE informing about the accuracy of executing the chosen strategy. Reinforcement learning behavior is known to account for higher-order statistical properties of sensorimotor uncertainty, such as the distribution standard deviation Trommershäuser et al. However, behavioral variability reflects variance in both motor planning i. Their conjecture was that, after positive reinforcement, response selection is updated in a manner that accounts for exploration, but not motor noise. In their studies, people with cerebellar degeneration displayed reinforcement learning behavior consistent with excessive variability being allotted to motor noise—a pattern indicative of impaired estimation of action execution. People with cerebellar degeneration also showed reduced exploration after negative reinforcement Therrien et al. The cumulative result is a reduced updating of action selection in response to reinforcement signaling that slows learning in this population. McDougle et al. Participants were required to select between two visual targets, each associated with a different magnitude of reward, by reaching to hit one or the other. On some trials they were given false feedback about the accuracy of their reach, which generated RPEs—the actual reward received differed from the expected outcome. In contrast to neurologically healthy participants, people with cerebellar degeneration were unable to determine if RPEs should be attributed to themselves or the experimental manipulation i. Reinforcement learning is not the only situation in which a credit-assignment problem must be resolved. Although it is less clear exactly how explicit learning operates, sensorimotor uncertainty likely contributes to a credit-assignment problem similar to that identified above. For example, one must determine if an error arose because of a poor choice of strategy, or because of poor execution of the chosen strategy. Here again, the involvement of an SPE signal would be beneficial to formulate and modify explicit strategies by informing how well the intended strategy was executed Figures 2C,D. Evidence supporting the involvement of an SPE-like signal in explicit learning arises from a series of studies investigating the ability of people with cerebellar degeneration to develop de novo strategies for learning. As noted above, cerebellar degeneration disrupts the signal supporting SPE-driven learning, which impairs performance during a visuomotor rotation paradigm. Previous work had demonstrated that in such tasks, people with cerebellar degeneration could follow a provided strategy to aim in a direction other than the target i. Such an observation led to a puzzling question—if their ability to employ strategies was so successful, why did not people with cerebellar degeneration use strategies all the time to compensate for their movement deficits instead of continuing to rely on an impaired SPE-driven learning system? Butcher et al. That is, some people with cerebellar degeneration continued to aim directly for the target despite the presence of the visuomotor rotation perturbation. However, Wong et al. Under certain circumstances, people with cerebellar degeneration could successfully develop de novo strategies using explicit learning. Wong and colleagues demonstrated that when people with cerebellar degeneration were able to view their actual hand moving simultaneously with the cursor, they could resolve the credit assignment problem by recognizing that task errors were not a result of a mis-executed motor command but instead caused by a manipulation of the cursor. That is, people with cerebellar degeneration could use visual feedback to appropriately attribute performance errors to task errors rather than execution errors. Consequently, people with cerebellar degeneration were able to invoke explicit learning to modify their movement goals i. This work thus suggests a role for SPE signals in supporting explicit learning. While more work is needed to parse the specific role that such SPE signals may play, together these studies provide compelling evidence of interactions between cerebellar computations and both explicit and reinforcement learning mechanisms. We have reviewed current literature on the interactions between SPE-driven, explicit, and reinforcement learning mechanisms in motor adaptation. It is generally agreed that overt learning behavior results from the combined outputs of each mechanism, but interactions between these mechanisms likely occur at multiple levels. For example, studies of people with cerebellar degeneration provide evidence of a role for SPE signals in the functioning of both reinforcement and explicit learning. These studies suggest that an SPE signal may be needed by reinforcement and explicit learning systems to know whether RPEs or task errors, respectively, arose from poorly executed movements or poor selection of an action or strategy. By helping to resolve this credit-assignment problem, SPEs can optimize learning by informing reinforcement and explicit learning systems whether an action or strategy truly needs to change. It is notable that some of the neuroanatomy needed to support these proposed interactions has been shown. With regard to a role for cerebellar SPE signals in reinforcement learning, the cerebellum communicates directly with the dorsal striatum via a short-latency disynaptic connection that modulates corticostriatal plasticity Hoshi et al. The posterior lobules of the cerebellum are also reciprocally connected with prefrontal cognitive regions of the cerebral cortex, which are hypothesized to support the explicit learning process Ramnani, ; Strick et al. The nature of the information sent through these pathways is unclear, but there is recent evidence to suggest homologous function across cerebellar projections Pisano et al. However, the cerebellum contributes to a diverse set of behaviors, both motor and non-motor Diedrichsen et al. Further work is needed to understand whether different regions of the cerebellum may be preferentially involved in the interactions proposed here or whether variability in the pattern of cerebellar damage across individuals and studies can explain some contrasting results. Sharing of the SPE signal represents one of the multiple possible interactions among SPE-driven, reinforcement, and explicit learning mechanisms below the level of their output stages see Figure 1 , and future research is needed to elucidate others. Importantly, the presence of such multi-level interactions means that learning mechanisms cannot be easily isolated. When it comes to motor adaptation, studies of people with cerebellar degeneration suggest that SPE-driven learning may be the primary system responsible for resolving performance errors. Only when the influence of SPE-driven is minimized, such as by eliminating the need or ability to compute a meaningful SPE signal e. This has important implications for future studies aiming to manipulate or leverage individual learning mechanisms. Finally, the work reviewed here begs the question of whether further insight into the interactions between SPE-driven, explicit, and reinforcement learning mechanisms can be gained from studies of motor adaptation in other patient populations. A sizable body of literature has studied motor adaptation in people with PD but has noted inconsistent findings. While some studies show similar adaptation behavior between people with PD and age-matched control participants e. Discrepant results may stem from differences in the size of the imposed perturbation Venkatakrishnan et al. To date, no study has attempted to parse the contributions of SPE-driven, explicit, and reinforcement learning to motor adaptation in this population but see Cressman et al. Overall, this literature, along with the other studies reviewed here, underscores the complexity of interactions occurring between motor learning mechanisms and argues for the importance of not treating such learning mechanisms as predominantly modular. Both AT and AW contributed equally to development of the idea, writing the manuscript, and generation of figures. All authors contributed to the article and approved the submitted version. AT was supported by funding from the Moss Rehabilitation Research Institute. AW was supported by the U. National Institutes of Health grant R01 NS The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Albert, S. Competition between parallel sensorimotor learning systems. BioRxiv [Preprint]. doi: CrossRef Full Text Google Scholar. Benson, B. A spatial explicit strategy reduces error but interferes with sensorimotor adaptation. PubMed Abstract CrossRef Full Text Google Scholar. Bock, O. Components of sensorimotor adaptation in young and elderly subjects. Brain Res. Bond, K. Flexible explicit but rigid implicit learning in a visuomotor adaptation task. Butcher, P. The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks. Cashaback, J. Dissociating error-based and reinforcement-based loss functions during sensorimotor learning. PLoS Comput. Chen, C. Short latency cerebellar modulation of the basal ganglia. Codol, O. 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Contribution of explicit processes to reinforcement-based motor learning. Hoshi, E. The cerebellum communicates with the basal ganglia. Izawa, J. Cerebellar contributions to reach adaptation and learning sensory consequences of action. Learning from sensory and reward prediction errors during motor adaptation. Kawato, M. Internal models for motor control and trajectory planning. Kim, H. The influence of task outcome on implicit motor learning. eLife 8:e King, M. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Krakauer, J. Motor learning. This observation suggests that in programming the motor output to the muscles of the arm, the CNS uses an internal model Wolpert et al. Using optogenetics the study, done by Dr. Mackenzie Mathis at Harvard University, using mice could also show that somatosensory cortex is involved in updating the internal model. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. Form of motor learning. Journal of Neuroscience. doi : PMC PMID Journal of Neurophysiology. Journal of NeuroEngineering and Rehabilitation. S2CID Category : Motor skills. Hidden categories: Articles with short description Short description matches Wikidata. Toggle limited content width. |
| Sensorimotor Adaptation | 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. Gastrock, R. Moreover, the motor system would not have a salient visual signal for grounding the comparison of feedback and aiming location. Motor adaptation and internal model formation in a robot-mediated forcefield. Bock, O. Here, we focus on studies of a third motor learning context, often termed motor adaptation, in which one must learn to modify an existing movement pattern to account for persistent changes to the body, task, or environmental dynamics Krakauer et al. |
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