Greater neural responses to trajectory errors are associated with superior force field adaptation in older adults

Dorsomedial frontal cortex damage impairs error-based, but not reinforcement-based motor learning in humans

We adapt our movements to new and changing environments through multiple processes. Sensory error-based learning counteracts environmental perturbations that affect the sensory consequences of movements. Sensory errors also cause the upregulation of reflexes and muscle co-contraction. Reinforcement-based learning enhances the selection of movements that produce rewarding outcomes. Although some findings have identified dissociable neural substrates of sensory error- and reinforcement-based learning, correlative methods have implicated dorsomedial frontal cortex in both. Here, we tested the causal contributions of dorsomedial frontal to adaptive motor control, studying people with chronic damage to this region. Seven human participants with focal brain lesions affecting the dorsomedial frontal and 20 controls performed a battery of arm movement tasks. Three experiments tested: (i) the upregulation of visuomotor reflexes and muscle co-contraction in response to unpredictable mechanical perturbations, (ii) sensory error-based learning in which participants learned to compensate predictively for mechanical force-field perturbations, and (iii) reinforcement-based motor learning based on binary feedback in the absence of sensory error feedback. Participants with dorsomedial frontal damage were impaired in the early stages of force field adaptation, but performed similarly to controls in all other measures. These results provide evidence for a specific and selective causal role for the dorsomedial frontal in sensory error-based learning.

Feedback processing in cognitive and motor tasks: A meta-analysis on the feedback-related negativity

For motor learning, the processing of behavioral outcomes is of high significance. The feedback-related negativity (FRN) is an event-related potential, which is often described as a correlate of the reward prediction error in reinforcement learning. The number of studies examining the FRN in motor tasks is increasing. This meta-analysis summarizes the component in the motor domain and compares it to the cognitive domain. Therefore, a data set of a previous meta-analysis in the cognitive domain that comprised 47 studies  was reanalyzed and compared to additional 25 studies of the motor domain. Further, a moderator analysis for the studies in the motor domain was conducted. The FRN amplitude was higher in the motor domain than in the cognitive domain. This might be related to a higher task complexity and a higher feedback ambiguity of motor tasks. The FRN latency was shorter in the motor domain than in the cognitive domain. Given that sensory information can be used as an external feedback predictor prior to the presentation of the final feedback, reward processing in the motor domain may have been faster and reduced the FRN latency. The moderator variable analysis revealed that the feedback modality influenced the FRN latency, with shorter FRN latencies after bimodal than after visual feedback. Processing of outcome feedback seems to share basic principles in both domains; however, differences exist and should be considered in FRN studies. Future research is motivated to scrutinize the effects of bimodal feedback and other moderators within the motor domain.

Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking

Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13 Hz) and beta band (13-30 Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7 Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.

Learning vs. minding: How subjective costs can mask motor learning

When learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects reach to targets in a force field. Concluding that those with greater error have learned less has a critical assumption: everyone uses the same error-canceling strategy. Alternatively, it could be that those with greater error may be choosing to sacrifice error reduction in favor of a lower effort movement. Here, we test this hypothesis in a dataset that includes both younger and older adults, where older adults exhibited greater kinematic errors. Utilizing the framework of optimal control theory, we infer subjective costs (i.e., strategies) and internal model accuracy (i.e., proportion of the novel dynamics learned) by fitting a model to each population's trajectory data. Our results demonstrate trajectories are defined by a combination of the amount learned and strategic differences represented by relative cost weights. Based on the model fits, younger adults could have learned between 65-90% of the novel dynamics. Critically, older adults could have learned between 60-85%. Each model fit produces trajectories that match the experimentally observed data, where a lower proportion learned in the model is compensated for by increasing costs on kinematic errors relative to effort. This suggests older and younger adults could be learning to the same extent, but older adults have a higher relative cost on effort compared to younger adults. These results call into question the proposition that older adults learn less than younger adults and provide a potential explanation for the equivocal findings in the literature. Importantly, our findings suggest that the metrics commonly used to probe motor learning paint an incomplete picture, and that to accurately quantify the learning process the subjective costs of movements should be considered.

Using EEG to study sensorimotor adaptation

Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.

Individual differences in error-related frontal midline theta activity during visuomotor adaptation

Post-feedback frontal midline EEG activity has been found to correlate with error magnitude during motor adaptation. However, the role of this neuronal activity remains to be elucidated. It has been hypothesized that post-feedback frontal midline activity may represent a prediction error, which in turn may be directly related to the adaptation process or to an unspecific orienting response. To address these hypotheses, we replicated a previous visuomotor adaptation experiment with very small perturbations, likely to invoke implicit adaptation, in a new group of 60 participants and combined it with EEG recordings. We found error-related peaks in the frontal midline electrodes in the time domain. However, these were best understood as modulations of frontal midline theta activity (FMT, 4-8 Hz). Trial-level differences in FMT correlated with error magnitude. This correlation was robust even for very small errors as well as in the absence of imposed perturbations, indicating that FMT does not depend on explicit or strategic re-aiming. Within participants, trial-level differences in FMT were not related to between-trial error corrections. Between participants, individual differences in FMT-error-sensitivity did not predict differences in adaptation rate. Taken together, these results imply that FMT does not drive implicit motor adaptation. Finally, individual differences in FMT-error-sensitivity negatively correlate to motor execution noise. This suggests that FMT reflects saliency: larger execution noise means a larger standard deviation of errors so that a fixed error magnitude is less salient. In conclusion, this study suggests that frontal midline theta activity represents a saliency signal and does not directly drive motor adaptation.

Does proprioceptive acuity influence the extent of implicit sensorimotor adaptation in young and older adults?

The ability to adjust movements to changes in the environment declines with aging. This age-related decline is caused by the decline of explicit adjustments. However, implicit adaptation remains intact and might even be increased with aging. Since proprioceptive information has been linked to implicit adaptation, it might well be that an age-related decline in proprioceptive acuity might be linked to the performance of older adults in implicit adaptation tasks. Indeed, age-related proprioceptive deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced proprioceptive acuity results in an increased reliance on predicted sensory consequences of the movement. Both these explanations led to our preregistered hypothesis: we expected a relation between the decline of proprioception and the amount of implicit adaptation across ages. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and proprioceptive signals during motor adaptation.

Valence-dependent Neural Correlates of Augmented Feedback Processing in Extensive Motor Sequence Learning - Part I: Practice-related Changes of Feedback Processing

Several event-related potentials (ERPs) are associated with the processing of valence-dependent augmented feedback during the practice of motor tasks. In this study, 38 students learned a sequential arm-movement-task with 192 trials in each of five practice sessions (960 practice trials in total), to examine practice-related changes in neural feedback processing. Electroencephalogram (EEG) was recorded in the first and last practice session. An adaptive bandwidth for movement accuracy led to equal amounts of positive and negative feedback. A frontal located negative deflection in the time window of the feedback-related negativity (FRN) was more negative for negative feedback and might reflect reward prediction errors in reinforcement learning. This negativity increased after extensive practice, which might indicate that smaller errors are harder to identify in the later phase. The late fronto-central positivity (LFCP) was more positive for negative feedback and is assumed to be associated with supervised learning and behavioral adaptations based on feedback with higher complexity. No practice-related changes of the LFCP were observed, which suggests that complex feedback is processed independent from the practice phase. The P300 displayed a more positive activation for positive feedback, which might be interpreted as the higher significance of positive feedback for the updating of internal models in this setting. A valence-independent increase of the P300 amplitude after practice might reflect an improved ability to update the internal representation based on feedback information. These results demonstrate that valence-dependent neural feedback processing changes with extensive practice of a novel motor task. Dissociating changes in latencies of different components support the assumption that they are related to distinct mechanisms of feedback-dependent learning.

Task Feedback Processing Differs Between Young and Older Adults in Visuomotor Rotation Learning Despite Similar Initial Adaptation and Savings

Ageing has been suggested to affect sensorimotor adaptation by impairing explicit strategy use. Here we recorded electrophysiological (EEG) responses during visuomotor rotation in both young (n = 24) and older adults (n = 25), to investigate the neural processes that underpin putative age-related effects on adaptation. We measured the feedback related negativity (FRN) and the P3 in response to task-feedback, as electrophysiological markers of task error processing and outcome evaluation. The two age groups adapted similarly and showed comparable after effects and savings when re-exposed to the same perturbation several days after the initial session. Older adults, however, had less distinct EEG responses (i.e., reduced FRN amplitudes) to negative and positive task feedback. The P3 did not differ between age groups. Both young and older adults also showed a sustained late positivity following task feedback. Measured at the frontal electrode Fz, this sustained activity was negatively associated with both the amount of voluntary disengagement of explicit strategy and savings. In conclusion, despite preserved task performance, we find clear differences in neural responses to errors in older people, which suggests that there is a fundamental decline in this aspect of sensorimotor brain function with age.

Adaptation of reach action to a novel force-field is not predicted by acuity of dynamic proprioception in either older or younger adults

Healthy ageing involves degeneration of the neuromuscular system which impacts movement control and proprioception. Yet the relationship between these sensory and motor deficits in upper limb reaching has not been examined in detail. Recently, we reported that age-related proprioceptive deficits were unrelated to accuracy in rapid arm movements, but whether this applied in motor tasks more heavily dependent on proprioceptive feedback was not clear. To address this, we have tested groups of younger and older adults on a force-field adaptation task under either full or limited visual feedback conditions and examined how performance was related to dynamic proprioceptive acuity. Adaptive performance was similar between the age groups, regardless of visual feedback condition, although older adults showed increased after-effects. Physically inactive individuals made larger systematic (but not variable) proprioceptive errors, irrespective of age. However, dynamic proprioceptive acuity was unrelated to adaptation and there was no consistent evidence of proprioceptive recalibration with adaptation to the force-field for any group. Finally, in spite of clear age-dependent loss of spatial working memory capacity, we found no relationship between memory capacity and adaptive performance or proprioceptive acuity. Thus, non-clinical levels of deficit in dynamic proprioception, due to age or physical inactivity, do not affect force-field adaptation, even under conditions of limited visual feedback that might require greater proprioceptive control.

Age-related differences in correction behavior for unintended acceleration

Although unintended acceleration caused by pedal misapplication is a cause of traffic accidents, fatal accidents may be avoided if drivers realize their error immediately and quickly correct how they are stepping on the pedal. This correction behavior may decline with age because the rate of fatal accidents is fairly higher for older adults than for younger adults. To investigate this possibility, the present study recruited older adults (n = 40, age range = 67-81 years) as well as younger adults (n = 40, age range = 18-32 years). In this study, they performed a pedal stepping task during which they were required to stop the simulated vehicle as quickly as possible when a red signal was presented on a monitor. During most trials, the vehicle decelerated/stopped when the brake pedal was applied in a normal manner. In a few trials, however, stepping on the brake pedal resulted in sudden acceleration of the vehicle (i.e., the occurrence of the unintended acceleration); when this occurred, the participants had to release the pedal and re-step on another pedal to decelerate/stop the vehicle as quickly as possible. We focused on the age-related differences of the reaction latencies during three time periods: from the appearance of the red signal on the screen until stepping on the pedal (Period 1), from stepping on the pedal until the release of the pedal (Period 2), and from the release of the pedal until re-stepping of another pedal (Period 3). The results showed that there was no age-related difference in the latency of Period 1, p = .771, whereas those of Periods 2 and 3 were longer for the older adults (ps < .001). The results suggest that there are age-related differences in error detection and correction abilities under unintended situations with foot pedal manipulation.

Neural signatures of reward and sensory error feedback processing in motor learning

At least two distinct processes have been identified by which motor commands are adapted according to movement-related feedback: reward-based learning and sensory error-based learning. In sensory error-based learning, mappings between sensory targets and motor commands are recalibrated according to sensory error feedback. In reward-based learning, motor commands are associated with subjective value, such that successful actions are reinforced. We designed two tasks to isolate reward- and sensory error-based motor adaptation, and we used electroencephalography in humans to identify and dissociate the neural correlates of reward and sensory error feedback processing. We designed a visuomotor rotation task to isolate sensory error-based learning that was induced by altered visual feedback of hand position. In a reward learning task, we isolated reward-based learning induced by binary reward feedback that was decoupled from the visual target. A fronto-central event-related potential called the feedback-related negativity (FRN) was elicited specifically by binary reward feedback but not sensory error feedback. A more posterior component called the P300 was evoked by feedback in both tasks. In the visuomotor rotation task, P300 amplitude was increased by sensory error induced by perturbed visual feedback and was correlated with learning rate. In the reward learning task, P300 amplitude was increased by reward relative to nonreward and by surprise regardless of feedback valence. We propose that during motor adaptation the FRN specifically reflects a reward-based learning signal whereas the P300 reflects feedback processing that is related to adaptation more generally. NEW & NOTEWORTHY We studied the event-related potentials evoked by feedback stimuli during motor adaptation tasks that isolate reward- and sensory error-based learning mechanisms. We found that the feedback-related negativity was specifically elicited by binary reward feedback, whereas the P300 was observed in both tasks. These results reveal neural processes associated with different learning mechanisms and elucidate which classes of errors, from a computational standpoint, elicit the feedback-related negativity and P300.

Proprioceptive deficits in inactive older adults are not reflected in fast targeted reaching movements

During normal healthy ageing there is a decline in the ability to control simple movements, characterised by increased reaction times, movement durations and variability. There is also growing evidence of age-related proprioceptive loss which may contribute to these impairments. However, this relationship has not been studied in detail for the upper limb. We recruited 20 younger adults (YAs) and 31 older adults (OAs) who each performed 2 tasks on a 2D robotic manipulandum. The first assessed dynamic proprioceptive acuity using active, multi-joint movements constrained by the robot to a pre-defined path. Participants made perceptual judgements of the lateral position of the unseen arm. The second task required fast, accurate and discrete movements to the same targets in the absence of visual feedback of the hand, and without robotic intervention. We predicted that the variable proprioceptive error (uncertainty range) assessed in Task 1 would be increased in physically inactive OAs and would predict increased movement variability in Task 2. Instead we found that physically inactive OAs had larger systematic proprioceptive errors (bias) than YAs (t[33] = 2.8, p = 0.009), and neither proprioceptive uncertainty nor bias was related to motor performance in either age group (all regression model R2 ≤ 0.06). We suggest that previously reported estimates of proprioceptive decline with ageing may be exaggerated by task demands and that the extent of these deficits is unrelated to control of discrete, rapid movement. The relationship between dynamic proprioceptive acuity and movement control in other tasks with greater emphasis on online feedback is still unclear and warrants further investigation.