Visuomotor Adaptation Experiment Research Articles

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.

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation

Humans predict the sensory consequences of motor commands by learning internal models of the body and of environment perturbations. When facing a sensory prediction error, should we attribute this error to a change in our body, and update the body internal model, or to a change in the environment? In the latter case, should we update an existing perturbation model or create a new model? Here, we propose that a decision-making process compares the models' prediction errors, weighted by their precisions, to select and update either the body model or an existing perturbation model. When no model can predict a perturbation, a new perturbation model is created and selected. When a model is selected, both the prediction's mean estimate and uncertainty are updated to minimize future prediction errors and to increase the precision of the predictions. Results from computer simulations, which we verified in an arm visuomotor adaptation experiment with subjects of both sexes, account for short aftereffects and large savings after adaptation to large, but not small, perturbations. Results also clarify previous data in the absence of errors (error-clamp): motor memories show an initial lack of decay after a large perturbation, but gradual decay after a small perturbation. Finally, qualitative individual differences in adaptation were explained by subjects selecting and updating either the body model or a perturbation model. Our results suggest that motor adaptation belongs to a general class of learning according to which memories are created when no existing memories can predict sensory data accurately and precisely.SIGNIFICANCE STATEMENT When movements are followed by unexpected outcomes, such as following the introduction of a visuomotor or a force field perturbation, or the sudden removal of such perturbations, it is unclear whether the CNS updates existing memories or creates new memories. Here, we propose a novel model of adaptation, and investigate, via computer simulations and behavioral experiments, how the amplitude and schedule of the perturbation, as well as the characteristics of the learner, lead to the selection and update of existing memories or the creation of new memories. Our results provide insights into a number of puzzling and contradictory motor adaptation data, as well as into qualitative individual differences in adaptation.

Improving the repeatability of two-rate model parameter estimations by using autoencoder networks.

The adaptive changes elicited in visuomotor adaptation experiments are usually well explained at group level by two-rate models (Smith et al., 2006), but parameters fitted to individuals show considerable variance. Data cleaning can mitigate this problem, but the assumption of smoothness can be problematic due to fast adaptive changes with discontinuous derivatives. In this paper, we collected time-series data from an experimental paradigm involving repeated training and investigated the effect of various cleaning methods, including an autoencoder network (AE), on the parameter estimation. We compared changes in the fitted parameters across different methods and across training repetitions. The results suggest that AE performed best overall, without introducing an underestimation bias on bf like moving average or piecewise polynomials, and that it reduced the within-subject variance overall and especially that of the fast retention rate af by >50%.

Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related.

Individual variations in motor adaptation rate were recently shown to correlate with movement variability or “motor noise” in a forcefield adaptation task. However, this finding could not be replicated in a meta-analysis of adaptation experiments. Possibly, this inconsistency stems from noise being composed of distinct components that relate to adaptation rate in different ways. Indeed, previous modeling and electrophysiological studies have suggested that motor noise can be factored into planning noise, originating from the brain, and execution noise, stemming from the periphery. Were the motor system optimally tuned to these noise sources, planning noise would correlate positively with adaptation rate, and execution noise would correlate negatively with adaptation rate, a phenomenon familiar in Kalman filters. To test this prediction, we performed a visuomotor adaptation experiment in 69 subjects. Using a novel Bayesian fitting procedure, we succeeded in applying the well-established state-space model of adaptation to individual data. We found that adaptation rate correlates positively with planning noise (β = 0.44; 95% HDI = [0.27 0.59]) and negatively with execution noise (β = –0.39; 95% HDI = [–0.50 –0.30]). In addition, the steady-state Kalman gain calculated from planning and execution noise correlated positively with adaptation rate (r = 0.54; 95% HDI = [0.38 0.66]). These results suggest that motor adaptation is tuned to approximate optimal learning, consistent with the “optimal control” framework that has been used to explain motor control. Since motor adaptation is thought to be a largely cerebellar process, the results further suggest the sensitivity of the cerebellum to both planning noise and execution noise.

Cerebellar transcranial direct current stimulation interacts with BDNF Val66Met in motor learning

BackgroundCerebellar transcranial direct current stimulation has been reported to enhance motor associative learning and motor adaptation, holding promise for clinical application in patients with movement disorders. However, behavioral benefits from cerebellar tDCS have been inconsistent. ObjectiveIdentifying determinants of treatment success is necessary. BDNF Val66Met is a candidate determinant, because the polymorphism is associated with motor skill learning and BDNF is thought to mediate tDCS effects. MethodsWe undertook two cerebellar tDCS studies in subjects genotyped for BDNF Val66Met. Subjects performed an eyeblink conditioning task and received sham, anodal or cathodal tDCS (N = 117, between-subjects design) or a vestibulo-ocular reflex adaptation task and received sham and anodal tDCS (N = 51 subjects, within-subjects design). Performance was quantified as a learning parameter from 0 to 100%. We investigated (1) the distribution of the learning parameter with mixture modeling presented as the mean (M), standard deviation (S) and proportion (P) of the groups, and (2) the role of BDNF Val66Met and cerebellar tDCS using linear regression presented as the regression coefficients (B) and odds ratios (OR) with equally-tailed intervals (ETIs). ResultsFor the eyeblink conditioning task, we found distinct groups of learners (MLearner = 67.2%; SLearner = 14.7%; PLearner = 61.6%) and non-learners (MNon-learner = 14.2%; SNon-learner = 8.0%; PNon-learner = 38.4%). Carriers of the BDNF Val66Met polymorphism were more likely to be learners (OR = 2.7 [1.2 6.2]). Within the group of learners, anodal tDCS supported eyeblink conditioning in BDNF Val66Met non-carriers (B = 11.9% 95%ETI = [0.8 23.0]%), but not in carriers (B = 1.0% 95%ETI = [-10.2 12.1]%). For the vestibulo-ocular reflex adaptation task, we found no effect of BDNF Val66Met (B = −2.0% 95%ETI = [-8.7 4.7]%) or anodal tDCS in either carriers (B = 3.4% 95%ETI = [-3.2 9.5]%) or non-carriers (B = 0.6% 95%ETI = [-3.4 4.8]%). Finally, we performed additional saccade and visuomotor adaptation experiments (N = 72) to investigate the general role of BDNF Val66Met in cerebellum-dependent learning and found no difference between carriers and non-carriers for both saccade (B = 1.0% 95%ETI = [-8.6 10.6]%) and visuomotor adaptation (B = 2.7% 95%ETI = [-2.5 7.9]%). ConclusionsThe specific role for BDNF Val66Met in eyeblink conditioning, but not vestibulo-ocular reflex adaptation, saccade adaptation or visuomotor adaptation could be related to dominance of the role of simple spike suppression of cerebellar Purkinje cells with a high baseline firing frequency in eyeblink conditioning. Susceptibility of non-carriers to anodal tDCS in eyeblink conditioning might be explained by a relatively larger effect of tDCS-induced subthreshold depolarization in this group, which might increase the spontaneous firing frequency up to the level of that of the carriers.

Generalization properties of a “saccadic-like” hand-reaching adaptation along a single degree of freedom

Visuomotor-adaptation experiments devoted to the study of plasticity are also used to indirectly test hypotheses about how the brain encodes the spatio-temporal characteristics of arm movement directed at a visual target. A current major theory, the vectorial coding hypothesis, postulates that arm movements are processed differentially for direction and amplitude. This approach, at first developed in an extrinsic Cartesian frame of references, has been also adopted in an intrinsic joint space. In the present paper, we report an experiment that corroborates this last point of view. Subjects performed pointing movements in a one degree of freedom condition, while systematic self-attributed endpoint errors were introduced. Through an observation of motor behavior in a battery of pre- and post-tests, we suggested that adaptation consisted in an increase in the motor gain in the adapted direction, with a perfect transfer to all starting points in the experimental reaching space. We explained the results by the absence of intersensory conflict and of correlative sensory adaptive component. As this paradigm was adapted from the saccadic adaptation paradigm, we eventually compared the two paradigms and highlighted that both induced mostly motor effects.