Flexible Cognitive Strategies during Motor Learning
When learning a new motor skill , verbal instruction often proves useful to hasten the learning process ., For example , a new driver is instructed on the sequence of steps required to change gears when using a standard transmission ., As the skill becomes consolidated , the driver no longer requires explicit reference to these instructions ., Operating a vehicle with a stiffer or looser clutch does not generally require further instruction , but rather entails a subtle recalibration , or adaptation of the previously learned skill ., Indeed , the use of an explicit strategy may even lead to degradation in the experts performance ., Consideration of these contradictory issues brings into question the role of instructions or explicit strategies in sensorimotor learning ., The type of motor task and nature of the instruction can have varying effects on motor execution and learning 1–3 ., In the serial reaction time task ( SRT ) , participants produce a sequence of cued button presses ., If the participant is informed of the underlying sequence , learning occurs much more rapidly compared to when sequential learning arises from repeated performance 4 ., However , learning in the SRT task entails the linkage of a series of discrete actions ., Explicit instructions of the sequence structure may be viewed as a way to create a working memory representation of the series ., Many skills lack such a clear elemental partition and , as such , participants cannot easily verbalize what a successful movement entails ., For example , the pattern of forces required to move the hand in a straight line in a novel force field 5–7 would be hard to verbalize ., Various studies have examined the role of explicit strategies in tasks involving sensorimotor adaptation 8–11 ., The benefits of an explicit strategy may be illusory with adaptive processes arising from automatic and incremental updating of a motor system that is impenetrable to conscious intervention 12–16 ., However , performance measures indicate that adaptation may differ between conditions in which participants are either aware or unaware of the changes in the environment 17 ., For example , a large visuomotor rotation can be introduced abruptly , in which case , awareness is likely , or introduced incrementally such that participants are unaware of the rotation ., The abrupt onset of large unexpected errors may promote the use of cognitive strategies 18–20 ., Participants who gain explicit knowledge of an imposed visuomotor rotation show better performance during learning than participants who report little or no awareness of the rotation 10 ., Moreover , the rate of learning , at least in the early phase of adaptation , correlates positively with spatial working memory span 21 , suggesting that strategic compensation may be dependent on working memory capacity ., Studies of sensorimotor adaptation during aging also indicate that the rate of learning is slower in older adults compared to young adults , despite similar aftereffects 22–24 ., This cost is absent in older adults who report awareness of the rotation 25 ., In many of the studies cited above , the assumption has been that the development of awareness can lead to the utilization of compensatory strategies ., However , few studies have directly sought to manipulate strategic control during sensorimotor adaptation ., One striking exception is a study by Mazzoni and Krakauer 9 ., Participants viewed a display of eight small circles , or visual landmarks , that were evenly spaced by 45° to form a large , implicit ring ., The target location was specified by presenting a bullseye within one of the eight circles ., After an initial training phase in which the visuomotor mapping was unaltered , a 45° rotation in the counterclockwise direction ( CCW ) was introduced ., In the standard condition in which no instructions were provided , participants gradually reduced endpoint error by altering their movement heading in the clockwise direction ( CW ) ., In the strategy condition , participants were given explicit instructions to move to the circle located 45° clockwise to the target ., This strategy enabled these participants to immediately eliminate all error ., However , as training continued , the participants progressively increased their movement heading in the clockwise direction ., As such , the endpoint location of the feedback cursor drifted further from the actual target location and , thus , performance showed an increase in error over training , a rather counterintuitive result 26 ., Mazzoni and Krakauer 9 proposed that this drift arises from the implicit adaptation of an internal forward model ., Importantly , the error signal for this learning process is not based on difference between the observed visual feedback and target location ., Rather , it is based on the difference between the observed visual feedback and strategic aiming location ., Even though participants aim to a clockwise location of the target ( as instructed ) , the motor system experiences a mismatch between the predicted state and the visual feedback ., This mismatch defines an error signal that is used to recalibrate the internal model ., Reducing the mismatch results in an adjustment of the internal model such that the next movement will be even further in the clockwise direction ., Thus , the operation of an implicit learning process that is impervious to the strategy produces the paradoxical deterioration in performance over time ., In the present paper , we start by asking how this hypothesis could be formalized in a computational model of motor learning ., State space modeling techniques have successfully described adaptation and generalization during motor learning 27–29 ., These models focus on how learning mechanisms minimize error from trial to trial ., Variants of these models postulate multiple learning mechanisms that operate at different time scales 28 ., Within this framework , strategic factors might be associated with fast learning processes that rapidly reduce error ., However , such models are unable to account for the drift that arises following the deployment of a strategy ., To address these issues , we developed a series of setpoint state-space models of adaptation to quantitatively explore how strategic control and implicit adaptation interact ., Assuming a fixed strategy , adaptation should continue to occur until the error signal , the difference between the feedback location and the aiming location is zero; that is , the visual feedback matches the intended aim of the reach ., As such , drift arising from implicit adaptation should continue to rise until it offsets the adopted strategy ., To test this prediction , we increased the length of the adaptation phase ., Moreover , we manipulated the salience of the visual landmarks used to support the strategy ., We hypothesized that these landmarks served as a proxy for the aiming location ., If this assumption is correct , then elimination of the visual landmarks should weaken the error signal , given uncertainty concerning the aiming location , and drift should be attenuated ., We test this prediction by comparing performance with and without visual landmarks ., When informed of an appropriate strategy that will compensate for the rotation , participants immediately counteract the rotation and show on-target accuracy ., The standard model as formulated above does not provide a mechanism to implement an explicit strategy ., To allow immediate implementation of the strategy , we postulate that there is direct feedthrough of the strategy ( s ) to the target error equation ( equation 1 ) : ( 3 ) Direct feedthrough allows the strategy to contribute to the target error equation without directly influencing the updating of the internal model ., If the strategy operated through the internal model , then the impact of the strategy would take time to evolve , assuming there is substantial memory of the internal models estimation of the rotation ( i . e . , A has a high value in Eq . 2 ) ., With direct feedthrough , the implementation of an appropriate strategy can immediately compensate for the rotation ., In the current arrangement , the appropriate strategy is fixed at 45° in the CW direction from the cued target ., Once the strategy is implemented , performance should remain stable since the error term is small ., Indeed , a model based on Eq ., 3 immediately compensates for the rotation ., The target error , the difference between the feedback location and target location , is essentially zero on the first trial with the strategy , and remains so throughout the rotation block ( Figure 1B – green line ) ., However , this model fails to match the empirical results observed by Mazzoni and Krakauer 9: performance drifts over time with an increase in errors in the direction of the strategy ., This phenomenon led the authors to suggest that the prediction error signal to the internal model is not based on target error ., Instead , the error signal should be defined by the difference between the feedback location and aiming location ( see Figure 2E ) : ( 4 ) The formulation of the prediction error term in Eq ., 4 is akin to a setpoint or reference signal from engineering control theory 30 ., In typical motor learning studies , the setpoint is to reach to the target ., When there is no strategy ( s\u200a=\u200a0 ) , the target error in Eq ., 1 is the same as the error term in Eq ., 4 ., However , when a strategy with direct feedthrough is used ( s≠0 ) , the strategy terms may cancel out if the actual implemented strategy is similar to the desired strategy ., The input error to update the internal models estimate of the rotation becomes: ( 5 ) This model shows immediate compensation for the visuomotor rotation , and more importantly , produces a gradual deterioration in performance over the course of continued training with the reaching error drifting in the direction of the strategy ( Figure 1A – red line ) , consistent with the results reported by Mazzoni and Krakauer 9 ., It is important to emphasize that the error signal for sensorimotor recalibration in Eq ., 4 is not based on the difference between the feedback location and target location ( target error ) ., Rather , the error signal is defined by the difference between the feedback location and aiming location , or what we will refer to as aiming error ., When a fixed strategy is adopted throughout training ( Figure 1B – blue line ) , the aiming error is ( initially ) quite large given that the predicted hand location is far from the location of visual feedback , even though the feedback cursor may be close to the actual target ., In its simplest form , the setpoint model predicts that , as the internal model minimizes this error ( Figure 1B – red line ) , drift will continue until the observed feedback of the hand matches the aiming location ., That is , the magnitude of the drift should equal the size of the strategic adjustment ., In the Mazzoni and Krakauer set-up 9 , the drift would eventually reach 45° in the CW direction ( Figure 1A – red line ) ., A second prediction can be derived by considering that the error signal in Eq ., 4 relies on an accurate estimate of the strategic aiming location ., We assume that a visual landmark in the display can be used as a reference point for strategy implementation ( e . g . , the blue circle adjacent to the target ) ., This landmark can serve as a proxy for the aiming location ., The salience of this landmark provides an accurate estimate of the aiming location and , from Eq ., 4 , drift should be pronounced ., However , if these landmarks are not available , then the estimate of the aiming location will be less certain ., Previous studies have shown that adaptation is attenuated when sensory feedback is noisy 31 , 32 ., One approach for modeling the effect of changing the availability or certainty of the ( strategy defined ) aiming location would be to vary the adaptation rate ( B ) ., For example , B could be smaller if there is a decrease in certainty of the aiming location , and correspondingly , a decrease in the certainty of the aiming error ., This model predicts that the rate of drift is directly related to B: if B is lower due to decreased certainty of the aiming location , then the rate of drift will be attenuated ( Figure 1A – cyan line ) ., To evaluate the predictions of this setpoint model , participants were tested in an extended visuomotor rotation task in which we varied the visual displays used to define the target and strategic landmarks ( see methods ) ., The target was defined as a green circle , appearing at one of eight possible locations , separated by 45° ( Figure 2 , only three shown here ) ., By encouraging the participants to make movements that “sliced” through the target , and only providing feedback at the point of intersection with the virtual target ring , we were able to train the participants to move quickly with relatively low trial-to-trial variability ., We assume that participants mostly relied on feedforward control given the ballistic nature of the movements and absence of continuous online feedback ., Participants were assigned to one of three experimental groups ( n\u200a=\u200a10 per group ) , with the groups defined by our manipulation of the blue landmarks in the visual displays ., For the aiming-target group ( AT ) , the blue circles were always visible , similar to the method used by Mazzoni and Krakauer ., For the disappearing aiming-target group ( AT ) , the blue circles were visible at the start of the trial and disappeared when the movement was initiated ., For the no aiming-target group ( NoAT ) , the blue landmarks were not included in the display ., The participants were initially required to reach to the green target ( Figure 2A ) ., Movement duration , measured when the hand crossed the target ring , averaged 275±50 . 8 ms with no significant difference between groups ( F2 , 27\u200a=\u200a1 . 02 , p\u200a=\u200a0 . 37 ) ., Following the initial familiarization block , participants were trained to use a strategy of moving 45° in the CW direction from the green target location , ( Figure 2B ) ., This location corresponded to the position of the neighboring blue circle ., Feedback was veridical in this phase ( e . g . , corresponded to hand position ) ., To help participants in the NoAT group learn to move at 45° , the blue circles were also presented on half of the trials for this group ( in this phase only ) ., The mean angular shifts , relative to the green target , were 43 . 4±1 . 6° and 42 . 9±1 . 2° for the AT and DAT groups , respectively ( Figure 3 - orange ) ., For the NoAT group , the mean angular shift was 43 . 5±0 . 9° when the aiming target was present and 40 . 1±7 . 1° when the aiming target was absent ., While the variance was considerably larger for trials without the aiming target , the means were not significantly different ( t18\u200a=\u200a0 . 95 , p\u200a=\u200a0 . 38 ) ., Practicing the 45° CW strategy did not produce interference on a subsequent baseline block in which participants were again instructed to reach to the cued , green target ( Figure 3 – black ) ., Over the last 10 movements of the familiarization block participants , across all groups , had an average target error of −1 . 5±0 . 7° ., Over the first 10 movements of the baseline block , this value was −0 . 5±0 . 6° , confirming that the strategy-only block did not produce a substantial bias ., Without warning , the CCW rotation was introduced ( Figure 2C ) ., As expected , the introduction of the CCW rotation induced a large target error ., Averaged over the two , rotation probe trials , the mean values were −41 . 6±3 . 3° , −43 . 8±1 . 1° , −43 . 5±3 . 2° for AT , DAT , and NoAT groups , respectively ( Figure 3 – “x” ) ., After the participants were instructed to use the clockwise strategy ( Figure 2D ) , the target error was reduced immediately to 3 . 5±4 . 4° , 1 . 0±4 . 3° , and −2 . 5±6 . 6° , values that were not significantly different from each other ( F2 , 27\u200a=\u200a1 . 96 , p\u200a=\u200a0 . 16 ) ., The participants were then instructed to use the strategy and required to produce a total of 320 reaching movements under the CCW rotation ., This extended phase allowed us to, a ) verify that error increased over time , drifting in the direction of the strategy , and, b ) determine if the magnitude of the drift would approximate the magnitude of the rotation , a prediction of the simplest form of the setpoint model ., Consistent with the results of Mazzoni and Krakauer 9 , error increased in the direction of the strategy over the initial phase of the rotation block ., However , the extent of the drift fell far short of the magnitude of the rotation ., To quantify the peak drift , each participants time series of endpoint errors was averaged over 10 movements and we identified the bin with the largest error ., Based on this estimate of peak drift , a significant difference was observed between groups ( F2 , 27\u200a=\u200a21 . 9 , p<0 . 001; Figure 4A ) ., This is consistent with the prediction of the model based that the salience of the aiming targets would influence the estimation of the aiming location ., Drift was largest when the aiming targets were always visible , and progressively less for the DAT and NoAT groups ., Drift was not isolated to particular target locations ( Figure 4B ) ., Our rotation plus strategy block lasted 320 trials , nearly four times the number of trials used by Mazzoni and Krakauer 9 ., This larger window provides an interesting probe on learning given that the participants become progressively worse in performance with respect to the target over the drift phase ., While the AT group had the largest drift , they eventually showed a change in performance such that the heading angle at the end of the rotation block was close to 45° CW from the green target ( Figure 3A ) ., By the end of training , their target error was only 0 . 3±3 . 9° , which was not significantly different from zero ( t9\u200a=\u200a0 . 17 , p\u200a=\u200a0 . 85 ) ., We did not observe a consistent pattern in how these participants counteracted the drift ( Figure 5 ) ., Two participants showed clear evidence of an abrupt change in their performance , suggesting a discrete change in their aiming strategy ., For the other eight AT participants , the changes in performance were more gradual ., The drift persisted over the 320 trials of the rotation block for participants in the DAT group ( Figure 3C ) ., The average drift was 5 . 9±4 . 8° at the end of training , a value that was significantly greater than zero ( t9\u200a=\u200a2 . 40 , p\u200a=\u200a0 . 04 ) ., Given that the NoAT group showed minimal drift , we did not observe any consistent changes in performance over the block ., At the end of training , the mean target error was only 1 . 0±1 . 9° , a value which is not significantly different from zero ( t9\u200a=\u200a1 . 01 , p\u200a=\u200a0 . 33 ) ., The availability or certainty in the estimate of the aiming location was manipulated by altering the presence of the aiming target across the groups ., As predicted by the setpoint model , the degree of drift was attenuated as the availability of the aiming targets decreased ., In the current implementation of our model , this decrease in drift rate is captured by a decrease in the adaptation rate ( B ) : with greater uncertainty , the weight given to the error term for updating the internal model is reduced ., However , one prediction of this model is at odds with the empirical results ., Variation in the adaptation rate not only predicts a change in drift rate , but also predicts a change in the washout period ., Specifically , decreasing the adaptation rate should produce a slower washout , or extended aftereffect ( Figure 1B – cyan ) ., This prediction was not supported ., The washout rates are similar across the three groups ( bootstrap , p>0 . 11 between all groups ) ., One could hypothesize different adaptation rates during the rotation and washout phases , with the effect of target certainty only relevant for the former ., However , a post hoc hypothesis along these lines is hard to justify ., Alternatively , it is possible that the adaptation rate ( B ) is similar for the three groups and that the variation in drift rate arises from another process ., One possibility is that the manipulation of the availability of the aiming targets influences the certainty of the desired strategy term in Equation 4 , and correspondingly , modifies the aiming error term: ( 6 ) A value of K that is less than 1 will attenuate drift ( Figure 1A – magenta line; simulated with K\u200a=\u200a0 . 5 ) because the strategy output ( Eq . 3 ) and the desired strategy ( Eq . 6 ) do not completely cancel out ., Consequently , the error used to adjust the internal model will be smaller and produce attenuated drift ( Figure 1B – magenta line ) ., Moreover , because the strategy is no longer used during the washout phase , the K term is no longer relevant ., Thus , the washout rates should be identical across the three groups , assuming a constant value of B . In sum , while variation in B or K can capture the group differences in drift rate , only the latter accounts for the similar rates of washout observed across groups ., When the availability of the aiming targets is reduced , either by flashing them briefly or eliminating them entirely , the participants certainty of the aiming location is attenuated ., This hypothesis is consistent with the notion that the aiming locations serve as a proxy for the predicted aiming location ., As noted above , none of the participants showed drift approaching 45° ., Even those exhibiting the largest drift eventually reversed direction such that they became more accurate over time in terms of reducing endpoint error with respect to the target location ., To capture this feature of the results , we considered how participants might vary their strategy over time as performance deteriorates ., It is reasonable to assume that the participant may recognize that the adopted strategy should be modified to offset the rising error ., One salient signal that could be used to adjust the strategy is the target error , the difference between the target location and the visual feedback ., To capture this idea , we modified the setpoint model , setting the strategy as a function of target error ( Figure 2E ) : ( 7 ) where E defines the retention of the state of the strategy and the F defines the rate of strategic adjustment ., As target error grows ( i . e . , drift ) , the strategy will be adjusted to minimize this error ( Figure 2E ) ., In our initial implementation of the setpoint model , the strategy term was fixed at 45° ., Equation 7 allows the strategy term to vary , taking on any value between 0° and 360° ., The availability of the aiming targets , captured by K in Eq ., 6 , influences the magnitude of the drift ., Greater drift occurs when the aiming error , that between the feedback location and aiming location , is salient ( Figure 1B – red line; K\u200a=\u200a1 ) ., However , when the target error grows too large , adjustments to the strategy begin to gain momentum and performance becomes more accurate with respect to the target given the change in strategy ( Figure 1C – blue line; simulated with E\u200a=\u200a1 and F\u200a=\u200a0 . 01 ) ., More emphasis on target errors rather than the aiming error results in less drift ( Figure 1C – orange line; simulated with F\u200a=\u200a0 . 05 ) ., Thus , the relative values of K and F determine the degree of performance error that is tolerated before strategic adjustments compensate to offset the drift ( Figure 1C ) ., This setpoint model ( Eqs . 8–11 ) was fit by bootstrapping ( see methods ) each groups time series of target errors: ( 8 ) ( 9 ) ( 10 ) ( 11 ) The fits ( Figure 6A–C and Table 1 ) show that K is the greatest for the AT group and progressively less for the DAT group and the NoAT group ( AT vs DAT group: p\u200a=\u200a0 . 003; AT vs NoAT groups: p<0 . 001; DAT vs . NoAT groups: p<0 . 001 ) ., When the aiming targets remain visible , the aiming error signal is readily available , and the weight given to the strategic aiming location , K , is larger ., Conversely , the weight given to the target error , F , is significantly greater for the NoAT group compared to the AT and DAT groups ( NoAT vs AT group: p\u200a=\u200a0 . 005; NoAT vs DAT group p\u200a=\u200a0 . 001 ) ., These results are consistent with the hypothesis that participants in the NoAT group rely more on target errors because the absence of the aiming targets removes a reference point for generating a reliable aiming error ( Eq . 9 ) ., The dynamics of the recalibration process and strategy state ( Eqs . 10 and 11 ) are plotted in Figure 6D ., These parameters , along with the other parameters that represent the memory of the internal model ( A ) , the adaptation rate ( B ) , and the memory of the strategy ( E ) are listed in Table 1 ., Following the rotation block , we instructed the participants that the rotation would be turned off and they should reach to the cued green target ., For the first eight trials , no endpoint feedback was presented ., This provided a measure of the degree of sensorimotor recalibration in the absence of learning ( Figure 4C – triangles ) ., Aftereffects were observed in all three groups ., The average error was significantly different from zero in the CW direction from the green target for all three groups ( one sample t-test for each group , p<0 . 001 ) ., In comparisons between the groups , the AT group showed the largest aftereffect of 19 . 2±3 . 7° ( t18\u200a=\u200a3 . 5 , p\u200a=\u200a0 . 003 and t18\u200a=\u200a5 . 61 , p<0 . 001 compared to the DAT and NoAT groups , respectively ) ., The mean aftereffects for the DAT and NoAT groups were 10 . 4±3 . 2° and 6 . 8±2 . 2° , values that were not significantly different ., When endpoint feedback was again provided , the size of the aftereffect diminished over the course of the washout block ( Figure 4C - squares ) ., In the setpoint model , the internal model will continue to adapt even in the face of strategic adjustments adopted to improve endpoint accuracy ., As such , the model predicts that the size of the aftereffect should be larger than the degree of drift ., To test this prediction , we compared the peak drift during the rotation block to the aftereffect ., In the preceding analysis , we had estimated peak drift for each participant by averaging over 10 movements and identifying the bin with the largest error ., However , a few errant movements could easily bias the estimate of drift within a 10-movement bin ., As an alternative procedure , we used a bootstrapping procedure to identify the bin with the largest angular error for each group ., This method should decrease the effect of noise because the estimate of peak drift is selected from an averaged sample of the participants data ., Moreover , any bias in the estimate of the magnitude of the peak should be uniform across the three groups of participants ., For consistency , we estimated the aftereffect ( the first 8 trials without feedback ) using the same bootstrap procedure ., For the AT group , the peak drift was 14 . 8±2 . 5° in the CW direction , occurring 64±30 movements into the rotation block ., For the DAT group , the peak drift was 10 . 0±1 . 8° , occurring at a later point in the rotation block ( 130±106 ) ., For the NoAT group , peak drift was only 3 . 2±2 . 7° and occurred after 145±131 movements ., As predicted by the model , the aftereffect was significantly larger than peak drift for the AT and NoAT groups ( Figure 4D; bootstrap: p\u200a=\u200a0 . 002 and p<0 . 001 , respectively ) ., The difference between the degree of peak drift and aftereffect in the DAT group was not reliable ., It is important to emphasize that estimates of the time of peak drift should be viewed cautiously , especially in terms of comparisons between the three groups ., These estimates have lower variance for the AT group because it was easier to detect the point of peak drift in this group compared to the DAT and NoAT groups ., Visuomotor rotation tasks are well-suited to explore how explicit cognitive strategies influence sensorimotor adaptation ., Following the approach introduced by Mazzoni and Krakauer 9 , we instructed participants to aim 45° CW in order to offset a −45° rotation ., Between groups , we manipulated the information available to support the strategy by either constantly providing an aiming target , blanking the aiming target at movement initiation , or never providing an aiming target ., In all groups , the strategy was initially effective , resulting in the rapid elimination of the rotation-induced endpoint error ., However , when the aiming target was present , participants showed a drift in the direction of the strategy , replicating the behavior observed in Mazzoni and Krakauer 9 ., This effect was markedly attenuated when the aiming target was not present suggesting that an accurate estimate of the strategic aiming location is responsible for causing the drift ., In addition , when the drift became quite large ( as in the AT group ) , participants begin to adjust their strategy to offset the implicit drift ., Mathematical models of sensorimotor adaptation have not explicitly addressed how a strategy influences learning and performance ., By formalizing the effect of strategy usage into the standard state-space model of motor learning , we can begin to evaluate qualitative hypotheses that have been offered to account for the influence of strategies on motor learning ., Mazzoni and Krakauer 9 suggested that drift reflects the interaction of the independent contribution of strategic and implicit learning processes in movement execution ., Current models of adaptation cannot be readily modified to account for this interaction ., Rather , we had to consider more substantive architectural changes ., Borrowing from engineering control theory , we used a setpoint model in which the internal model can be recalibrated around any given reach location ., The idea of a setpoint is generally implicit in most models of learning , but this component does not come into play since the regression is around zero ., However , simply making the setpoint explicit is not sufficient to capture the drift phenomenon ., The strategy must have direct feedthrough to the output equation in order to implement the explicit strategy while allowing for an internal model to implicitly learn the visuomotor rotation ., This simple setpoint model was capable of completely eliminating error on the first trial and capture the deterioration of performance with increased training ., Drift arises because the error signal is driven by the difference between the internal models prediction of the aiming location and the actual , endpoint feedback ., The idea that an aiming error signal is the source of drift is consistent with the conjecture of Mazzoni and Krakauer 9 ., An important observation in the current study is that , given uncertainty in the prediction of the aiming location , participants use external cues as a proxy in generating this prediction ., This hypothesis accounts for the observation that drift was largest when the aiming target was always visible , intermediate when the aiming target was only visible at the start of the trial , and negligible when the aiming target was never visible ., The aiming target , when present , served as a proxy for predicted hand position , and helped define the error between the feedback cursor and aiming location in visual coordinates ., When the aiming target was not present , the aiming location was less well-defined in visual coordinates , and thus , the relationship between the aiming location and feedback cursor was less certain ., Under this condition , the participants certainty of the error was reduced and adaptation based of this signal was attenuated ., Quantitatively , progress
Visuomotor rotation tasks have proven to be a powerful tool to study adaptation of the motor system ., While adaptation in such tasks is seemingly automatic and incremental , participants may gain knowledge of the perturbation and invoke a compensatory strategy ., When provided with an explicit strategy to counteract a rotation , participants are initially very accurate , even without on-line feedback ., Surprisingly , with further testing , the angle of their reaching movements drifts in the direction of the strategy , producing an increase in endpoint errors ., This drift is attributed to the gradual adaptation of an internal model that operates independently from the strategy , even at the cost of task accuracy ., Here we identify constraints that influence this process , allowing us to explore models of the interaction between strategic and implicit changes during visuomotor adaptation ., When the adaptation phase was extended , participants eventually modified their strategy to offset the rise in endpoint errors ., Moreover , when we removed visual markers that provided external landmarks to support a strategy , the degree of drift was sharply attenuated ., These effects are accounted for by a setpoint state-space model in which a strategy is flexibly adjusted to offset performance errors arising from the implicit adaptation of an internal model ., More generally , these results suggest that strategic processes may operate in many studies of visuomotor adaptation , with participants arriving at a synergy between a strategic plan and the effects of sensorimotor adaptation .
Motor learning has been modeled as an implicit process in which an error , signaling the difference between the predicted and actual outcome is used to modify a model of the actor-environment interaction ., This process is assumed to operate automatically and implicitly ., However , people can employ cognitive strategies to improve performance ., It has recently been shown that when implicit and explicit processes are put in opposition , the operation of motor learning mechanisms will offset the advantages conferred by a strategy and eventually , performance deteriorates ., We present a computational model of the interplay of these processes ., A key insight of the model is that implicit and explicit learning mechanisms operate on different error signals ., Consistent with previous models of sensorimotor adaptation , implicit learning is driven by an error reflecting the difference between the predicted and actual feedback for that movement ., In contrast , explicit learning is driven by an error based on the difference between the feedback and target location of the movement , a signal that directly reflects task performance ., Empirically , we demonstrate constraints on these two error signals ., Taken together , the modeling and empirical results suggest that the benefits of a cognitive strategy may lie hidden in many motor learning tasks .
neuroscience/behavioral neuroscience, neuroscience/cognitive neuroscience, neuroscience/motor systems, computational biology/computational neuroscience, neuroscience/experimental psychology