Proprioceptive Re-alignment drives Implicit Sensorimotor Adaptation

Multiple learning processes contribute to successful goal-directed actions in the face of changing physiological states, biomechanical constraints, and environmental contexts. Amongst these processes, implicit sensorimotor adaptation is of primary importance, ensuring that movements remain well-calibrated and accurate. A large body of work on reaching movements has emphasized how adaptation centers on an iterative process designed to minimize visual errors. The role of proprioception has been largely neglected, thought to play a passive role in which proprioception is affected by the visual error but does not directly contribute to adaptation. Here we present an alternative to this visuo-centric framework, arguing that that implicit adaptation can be understood as minimizing a proprioceptive error, the distance between the perceived hand position and its intended goal. We use this proprioceptive re-alignment model (PReMo) to re-examine many phenomena that have previously been interpreted in terms of learning from visual errors, as well as offer novel accounts for unexplained phenomena. We discuss potential challenges for this new perspective on implicit adaptation and outline a set of predictions for future experimentation.

Visual error correction of continuous aerobics action images based on graph difference function

In order to solve the problem of the easy appearance of blurring images (easy to appear blur) or jagged effect after correction by traditional method, and as the visual error correction effect is not good, we propose a new visual error correction method of continuous calisthenics action image and a new algorithm for visual error correction. The continuous calisthenics action image is encoded and decoded, the error between the original image and the error image is obtained and with that the difference function is processed. Then the error compensation results are obtained and the visual error correction of the continuous calisthenics action image is realised. At the same time, a new algorithm for checking and correcting visual parallax matching errors is proposed in this paper. This algorithm can not only identify all matching errors in the parallax data, but also detect and correct them according to the continuity of shape representation, and effectively calculate the various performances of such matching algorithms automatically and quantitatively. The results show that the image processed by the proposed method has no significant visual error, and the peak signal-to-noise ratio and structural similarity are very high. The experimental results of the new algorithm also prove that the algorithm is very useful for the study of visual matching. It can be seen that the proposed method is effective and can be effectively used.

Adaptive control of movement deceleration during saccades

As you read this text, your eyes make saccades that guide your fovea from one word to the next. Accuracy of these movements require the brain to monitor and learn from visual errors. A current model suggests that learning is supported by two different adaptive processes, one fast (high error sensitivity, low retention), and the other slow (low error sensitivity, high retention). Here, we searched for signatures of these hypothesized processes and found that following experience of a visual error, there was an adaptive change in the motor commands of the subsequent saccade. Surprisingly, this adaptation was not uniformly expressed throughout the movement. Rather, after experience of a single error, the adaptive response in the subsequent trial was limited to the deceleration period. After repeated exposure to the same error, the acceleration period commands also adapted, and exhibited resistance to forgetting during set-breaks. In contrast, the deceleration period commands adapted more rapidly, but suffered from poor retention during these same breaks. State-space models suggested that acceleration and deceleration periods were supported by a shared adaptive state which re-aimed the saccade, as well as two separate processes which resembled a two-state model: one that learned slowly and contributed primarily via acceleration period commands, and another that learned rapidly but contributed primarily via deceleration period commands.

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

Dopamine signaling is thought to mediate reward-based learning. We tested for a role of dopamine in motor adaptation by administering the dopamine precursor levodopa to healthy participants in two experiments involving reaching movements. Levodopa has been shown to impair reward-based learning in cognitive tasks. Thus, we hypothesized that levodopa would selectively impair aspects of motor adaptation that depend on reinforcement of rewarding actions.In the first experiment, participants performed two separate tasks in which adaptation was driven either by visual error-based feedback of the hand position or binary reward feedback. We used EEG to measure event-related potentials evoked by task feedback. We hypothesized that levodopa would specifically diminish adaptation and the neural responses to feedback in the reward learning task. However, levodopa did not affect motor adaptation in either task nor did it diminish event-related potentials elicited by reward outcomes. In the second experiment, participants learned to compensate for mechanical force field perturbations applied to the hand during reaching. Previous exposure to a particular force field can result in savings during subsequent adaptation to the same force field or interference during adaptation to an opposite force field. We hypothesized that levodopa would diminish savings and anterograde interference, as previous work suggests that these phenomena result from a reinforcement learning process. However, we found no reliable effects of levodopa.These results suggest that reward-based motor adaptation, savings, and interference may not depend on the same dopaminergic mechanisms that have been shown to be disrupted by levodopa during various cognitive tasks.

Competition between parallel sensorimotor learning systems

Sensorimotor adaptation benefits from learning in two parallel systems: one that has access to explicit knowledge, and another that relies on implicit, unconscious correction. However, it is unclear how these systems interact: does enhancing one system’s contributions, for example through instruction, impair the other, or do they learn independently? Here we illustrate that certain contexts can lead to competition between implicit and explicit learning. In some cases, each system is responsive to a task-related visual error. This shared error appears to create competition between these systems, such that when the explicit system increases its response, errors are siphoned away from the implicit system, thus reducing its learning. This model suggests that explicit strategy can mask changes in implicit error sensitivity related to savings and interference. Other contexts suggest that the implicit system can respond to multiple error sources. When these error sources conflict, a second type of competition occurs. Thus, the data show that during sensorimotor adaptation, behavior is shaped by competition between parallel learning systems.

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.

The Effect of Visual Uncertainty on Implicit Motor Adaptation

ABSTRACTSensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to a weakening of the error signal driving adaptation. Here we consider an alternative hypothesis, namely that uncertainty alters the perceived location of the feedback. We present two visuomotor adaptation experiments to compare these hypotheses, varying the size and uncertainty of a visual error signal. Uncertainty attenuated learning when the error size was small but had no effect when the error size was large. This pattern of results favors the hypothesis that uncertainty does not impact the strength of the error signal, but rather, leads to mis-localization of the error. We formalize these ideas to offer a novel perspective on the effect of visual uncertainty on implicit sensorimotor adaptation.SIGNIFICANCE STATEMENTCurrent models of sensorimotor adaptation assume that the rate of learning will be related to properties of the error signal (e.g., size, consistency, relevance). Recent evidence has challenged this view, pointing to a rigid, modular system, one that automatically recalibrates the sensorimotor map in response to movement errors, with minimal constraint. In light of these developments, this study revisits the influence of feedback uncertainty on sensorimotor adaptation. Adaptation was attenuated in response to a noisy feedback signal, but the effect was only manifest for small errors and not for large errors. This interaction suggests that uncertainty does not weaken the error signal. Rather, it may influence the perceived location of the feedback and thus the change in the sensorimotor map induced by that error. These ideas are formalized to show how the motor system remains exquisitely calibrated, even if adaptation is largely insensitive to the statistics of error signals.

Gaze Accuracy Differences During Single-Leg Balance Following Anterior Cruciate Ligament Reconstruction

Context: Individuals following anterior cruciate ligament reconstruction (ACLR) demonstrate altered postural stability and functional movement patterns. It is hypothesized that individuals following ACLR may compensate with sensory adaptations with greater reliance on visual mechanisms during activities. It is unknown if visual compensatory strategies are implemented to maintain postural stability during functional tasks. Objective: To examine visual gaze accuracy during a single-leg balance task in individuals following ACLR compared with healthy, active controls. Design: Case control. Setting: Controlled laboratory. Participants: A total of 20 individuals (10 ACLR and 10 healthy controls) participated in the study. Data Collection and Analysis: Visual gaze patterns were obtained during 20-second single-leg balance trials while participants were instructed to look at presented targets. During the Stationary Target Task, the visual target was presented in a central location for the duration of the trial. The Moving Target Task included a visual target that randomly moved to 1 of 9 target locations for a period of 2 seconds. Targets were stratified into superior, middle, and inferior levels for the Moving Target Task. Results: The Stationary Target Task demonstrated no differences in visual error between groups (P = .89). The Moving Target Task demonstrated a significant interaction between group and target level (F2,36 = 3.76, P = .033). Individuals following ACLR demonstrated greater visual error for the superior targets (ACLR = .70 [.44] m, healthy = .41 [.21] m, Cohen d = 0.83 [0.06 to 1.60]) and inferior targets (ACLR = .68 [.25] m, healthy = .33 [.16] m, Cohen d = 1.67 [0.81 to 2.52]). Conclusion: Individuals following ACLR demonstrate greater visual error during settings of high or low visual stimuli compared with healthy individuals to maintain single-limb postural stability. This population may rely on visual input to compensate for the somatosensory changes following injury.