Implicit Adaptation Is Modulated by the Relevance of Feedback.

Given that informative and relevant feedback in the real world is often intertwined with distracting and irrelevant feedback, we asked how the relevancy of visual feedback impacts implicit sensorimotor adaptation. To tackle this question, we presented multiple cursors as visual feedback in a center-out reaching task and varied the task relevance of these cursors. In other words, participants were instructed to hit a target with a specific task-relevant cursor, while ignoring the other cursors. In Experiment 1, we found that reach aftereffects were attenuated by the mere presence of distracting cursors, compared with reach aftereffects in response to a single task-relevant cursor. The degree of attenuation did not depend on the position of the distracting cursors. In Experiment 2, we examined the interaction between task relevance and attention. Participants were asked to adapt to a task-relevant cursor/target pair, while ignoring the task-irrelevant cursor/target pair. Critically, we jittered the location of the relevant and irrelevant target in an uncorrelated manner, allowing us to index attention via how well participants tracked the position of target. We found that participants who were better at tracking the task-relevant target/cursor pair showed greater aftereffects, and interestingly, the same correlation applied to the task-irrelevant target/cursor pair. Together, these results highlight a novel role of task relevancy on modulating implicit adaptation, perhaps by giving greater attention to informative sources of feedback, increasing the saliency of the sensory prediction error.

Distinct Neural Signatures of Outcome Monitoring After Selection and Execution Errors.

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as the result of either errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the feedback-related negativity [FRN]) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards, and amplitude of this signal correlated with behavioral adjustment after these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action.

Dissociable use-dependent processes for volitional goal-directed reaching.

Repetition of specific movement biases subsequent actions towards the practiced movement, a phenomenon known as use-dependent learning (UDL). Recent experiments that impose strict constraints on planning time have revealed two sources of use-dependent biases, one arising from dynamic changes occurring during motor planning and another reflecting a stable shift in motor execution. Here, we used a distributional analysis to examine the contribution of these biases in reaching. To create the conditions for UDL, the target appeared at a designated 'frequent' location on most trials, and at one of six 'rare' locations on other trials. Strikingly, the heading angles were bimodally distributed, with peaks at both frequent and rare target locations. Despite having no constraints on planning time, participants exhibited a robust bias towards the frequent target when movements were self-initiated quickly, the signature of a planning bias; notably, the peak near the rare target was shifted in the frequently practiced direction, the signature of an execution bias. Furthermore, these execution biases were not only replicated in a delayed-response task but were also insensitive to reward. Taken together, these results extend our understanding of how volitional movements are influenced by recent experience.

Individual differences in proprioception predict the extent of implicit sensorimotor adaptation.

Recent studies have revealed an upper bound in motor adaptation, beyond which other learning systems may be recruited. The factors determining this upper bound are poorly understood. The multisensory integration hypothesis states that this limit arises from opposing responses to visual and proprioceptive feedback. As individuals adapt to a visual perturbation, they experience an increasing proprioceptive error in the opposite direction, and the upper bound is the point where these two error signals reach an equilibrium. Assuming that visual and proprioceptive feedback are weighted according to their variability, there should be a correlation between proprioceptive variability and the limits of adaptation. Alternatively, the proprioceptive realignment hypothesis states that the upper bound arises when the (visually biased) sensed hand position realigns with the expected sensed position (target). When a visuo-proprioceptive discrepancy is introduced, the sensed hand position is biased toward the visual cursor, and the adaptive system counteracts this discrepancy by driving the hand away from the target. This hypothesis predicts a correlation between the size of the proprioceptive shift and the upper bound of adaptation. We tested these two hypotheses by considering natural variation in proprioception and motor adaptation across individuals. We observed a modest, yet reliable correlation between the upper bound of adaptation with both proprioceptive measures (variability and shift). Although the results do not clearly favor one hypothesis over the other, they underscore the critical role of proprioception in sensorimotor adaptation.NEW & NOTEWORTHY Although the sensorimotor system uses sensory feedback to remain calibrated, this learning process is constrained, limited by the maximum degree of plasticity. The factors determining this limit remain elusive. Guided by two hypotheses, we show that individual differences in the upper bound of adaptation in response to a visual perturbation can be predicted by the bias and variability in proprioception. These results underscore the critical, but often neglected role of proprioception in human motor learning.

The effect of visual uncertainty on implicit motor adaptation.

Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their joint effect on adaptation, using a feedback method that avoids inherent limitations with standard visuomotor tasks. Uncertainty attenuated adaptation, but only when the error was small. This striking interaction highlights a novel constraint for models of sensorimotor adaptation. Sensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, motor adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to the motor system discounting noisy feedback and thus reducing the learning rate. In its simplest form, optimal integration predicts that uncertainty would result in reduced learning for all error sizes. However, these predictions remain untested since manipulations of error size in standard visuomotor tasks introduce confounds in the degree to which performance is influenced by other learning processes such as strategy use. Here, we used a novel visuomotor task that isolates the contribution of implicit adaptation, independent of error size. In two experiments, we varied feedback uncertainty and error size in a factorial manner. At odds with the basic predictions derived from the optimal integration theory, the results show that uncertainty attenuated learning only when the error size was small but had no effect when the error size was large. We discuss possible mechanisms that may account for this interaction, considering how uncertainty may interact with the relevance assigned to the error signal or how the output of the adaptation system in terms of recalibrating the sensorimotor map may be modified by uncertainty.NEW & NOTEWORTHY Sensorimotor adaptation is influenced by both the size and variance of error information. In the present study, we varied visual uncertainty and error size in a factorial manner and evaluated their joint effect on adaptation, using a feedback method that avoids inherent limitations with standard visuomotor tasks. Uncertainty attenuated adaptation but only when the error was small. This striking interaction highlights a novel constraint for models of sensorimotor adaptation.

Continuous reports of sensed hand position during sensorimotor adaptation.

Sensorimotor learning is thought to entail multiple learning processes, some volitional and others automatic. A new method to isolate implicit learning involves the use of a "clamped" visual perturbation in which, during a reaching movement, visual feedback is limited to a cursor that follows an invariant trajectory offset from the target by a fixed angle. Despite full awareness that the cursor movement is not contingent on their behavior, as well as explicit instructions to ignore the cursor, systematic changes in motor behavior are observed, and these changes have the signatures of motor adaptation observed in studies using classic visuomotor perturbations. Although it is clear that the response to clamped feedback occurs automatically, it remains unknown whether participants are sensitive to the large deviations in hand position that occur during adaptation. To address this question, we used the clamp method and asked participants to report their hand position after each reach. As expected, we observed robust deviations in hand angle away from the target (average of ∼18°). The hand reports also showed systematic deviations over the course of adaptation, initially attracted toward the visual feedback and then in the opposite direction, paralleling the shift in hand position. However, the shift in perceived hand position was subtle, reaching only ∼2° at asymptote. These results confirm that participants have limited awareness of the behavioral changes that occur during sensorimotor adaptation while revealing the impact of feedforward and feedback signals on their subjective experience.NEW & NOTEWORTHY Sensorimotor adaptation operates in an obligatory manner. Qualitatively, subjective reports obtained after adaptation demonstrate that, in many conditions, participants are unaware of significant changes in behavior. In the present study, we quantified participants' sensitivity to these adaptive changes by obtaining reports of hand position on a trial-by-trial basis. The results confirm that participants are largely unaware of adaptation but also reveal the subtle influence of feedback on their subjective experience.

Credit Assignment in a Motor Decision Making Task Is Influenced by Agency and Not Sensory Prediction Errors.

Failures to obtain reward can occur from errors in action selection or action execution. Recently, we observed marked differences in choice behavior when the failure to obtain a reward was attributed to errors in action execution compared with errors in action selection (McDougle et al., 2016). Specifically, participants appeared to solve this credit assignment problem by discounting outcomes in which the absence of reward was attributed to errors in action execution. Building on recent evidence indicating relatively direct communication between the cerebellum and basal ganglia, we hypothesized that cerebellar-dependent sensory prediction errors (SPEs), a signal indicating execution failure, could attenuate value updating within a basal ganglia-dependent reinforcement learning system. Here we compared the SPE hypothesis to an alternative, "top-down" hypothesis in which changes in choice behavior reflect participants' sense of agency. In two experiments with male and female human participants, we manipulated the strength of SPEs, along with the participants' sense of agency in the second experiment. The results showed that, whereas the strength of SPE had no effect on choice behavior, participants were much more likely to discount the absence of rewards under conditions in which they believed the reward outcome depended on their ability to produce accurate movements. These results provide strong evidence that SPEs do not directly influence reinforcement learning. Instead, a participant's sense of agency appears to play a significant role in modulating choice behavior when unexpected outcomes can arise from errors in action execution.SIGNIFICANCE STATEMENT When learning from the outcome of actions, the brain faces a credit assignment problem: Failures of reward can be attributed to poor choice selection or poor action execution. Here, we test a specific hypothesis that execution errors are implicitly signaled by cerebellar-based sensory prediction errors. We evaluate this hypothesis and compare it with a more "top-down" hypothesis in which the modulation of choice behavior from execution errors reflects participants' sense of agency. We find that sensory prediction errors have no significant effect on reinforcement learning. Instead, instructions influencing participants' belief of causal outcomes appear to be the main factor influencing their choice behavior.

Characteristics of Implicit Sensorimotor Adaptation Revealed by Task-irrelevant Clamped Feedback.

Sensorimotor adaptation occurs when there is a discrepancy between the expected and actual sensory consequences of a movement. This learning can be precisely measured, but its source has been hard to pin down because standard adaptation tasks introduce two potential learning signals: task performance errors and sensory prediction errors. Here we employed a new method that induces sensory prediction errors without task performance errors. This method combines the use of clamped visual feedback that is angularly offset from the target and independent of the direction of motion, along with instructions to ignore this feedback while reaching to targets. Despite these instructions, participants unknowingly showed robust adaptation of their movements. This adaptation was similar to that observed with standard methods, showing sign dependence, local generalization, and cerebellar dependency. Surprisingly, adaptation rate and magnitude were invariant across a large range of offsets. Collectively, our results challenge current models of adaptation and demonstrate that behavior observed in many studies of adaptation reflect the composite effects of task performance and sensory prediction errors.

The influence of task outcome on implicit motor learning.

Recent studies have demonstrated that task success signals can modulate learning during sensorimotor adaptation tasks, primarily through engaging explicit processes. Here, we examine the influence of task outcome on implicit adaptation, using a reaching task in which adaptation is induced by feedback that is not contingent on actual performance. We imposed an invariant perturbation (rotation) on the feedback cursor while varying the target size. In this way, the cursor either hit or missed the target, with the former producing a marked attenuation of implicit motor learning. We explored different computational architectures that might account for how task outcome information interacts with implicit adaptation. The results fail to support an architecture in which adaptation operates in parallel with a model-free operant reinforcement process. Rather, task outcome may serve as a gain on implicit adaptation or provide a distinct error signal for a second, independent implicit learning process. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Neural Signatures of Prediction Errors in a Decision-Making Task Are Modulated by Action Execution Failures.

Decisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should be sensitive to not only whether the choice itself was suboptimal but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this, we used a modified version of a classic reinforcement learning task in which feedback indicated whether negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful, but reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction errors in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes.

Invariant errors reveal limitations in motor correction rather than constraints on error sensitivity.

Implicit sensorimotor adaptation is traditionally described as a process of error reduction, whereby a fraction of the error is corrected for with each movement. Here, in our study of healthy human participants, we characterize two constraints on this learning process: the size of adaptive corrections is only related to error size when errors are smaller than 6°, and learning functions converge to a similar level of asymptotic learning over a wide range of error sizes. These findings are problematic for current models of sensorimotor adaptation, and point to a new theoretical perspective in which learning is constrained by the size of the error correction, rather than sensitivity to error.