Abstract

We routinely make fine motor adjustments to maintain optimal motor performance. These adaptations have been attributed to both implicit, error-based mechanisms, and explicit, strategy-based mechanisms. However, little is known about the neural basis of implicit vs. explicit learning. Here, we aimed to use anodal transcranial direct current stimulation (tDCS) to probe the relationship between different brain regions and learning mechanisms during a visuomotor adaptation task in humans. We hypothesized that anodal tDCS over the cerebellum (CB) should increase implicit learning while anodal tDCS over the dorsolateral prefrontal cortex (dlPFC), a region associated with higher-level cognition, should facilitate explicit learning. Using a horizontal visuomotor adaptation task that measures explicit/implicit contributions to learning (Taylor et al., 2014), we found that dlPFC stimulation significantly improved performance compared to the other groups, and weakly increased explicit learning. However, CB stimulation had no effects on either target error or implicit learning. Previous work showed variable CB stimulation effects only on a vertical visuomotor adaptation task (Jalali et al., 2017), so in Experiment 2, we conducted the same study using a vertical context to see if we could find effects of CB stimulation. We found only weak effects of CB stimulation on target error and implicit learning, and now the dlPFC effect did not replicate. To resolve this discrepancy, in Experiment 3, we examined the effect of context (vertical vs. horizontal) on implicit and explicit contributions and found that individuals performed significantly worse and used greater implicit learning in the vertical screen condition compared to the horizontal screen condition. Across all experiments, however, there was high inter-individual variability, with strong influences of a few individuals, suggesting that these effects are not consistent across individuals. Overall, this work provides preliminary support for the idea that different neural regions can be engaged to improve visuomotor adaptation, but shows that each region's effects are highly context-dependent and not clearly dissociable from one another. This holds implications especially in neurorehabilitation, where an intact neural region could be engaged to potentially compensate if another region is impaired. Future work should examine factors influencing interindividual variability during these processes.

Highlights

  • Motor actions are rapidly and continuously adjusted to accommodate changes in our bodies or our environment (Körding and Wolpert, 2006; Shadmehr et al, 2010)

  • DlPFC Stimulation Reduces Target Error Compared to Sham We first compared the effects of dorsolateral prefrontal cortex (dlPFC), CB, and SHAM stimulation on target error during the visuomotor adaptation task

  • In line with our hypotheses, there was a significant difference in target error between groups during the rotation block [one-way ANOVA: F(2,43) = 3.40, p = 0.042, ηp2 = 0.14, f = 0.40, Bayes Factor (BF) = 1.65 ± 0.02%; CB = −1.00 ± 4.11◦, dlPFC = 1.41 ± 2.51◦, SHAM = −3.94 ± 8.53◦; Figure 2; Table 1]

Read more

Summary

Introduction

Motor actions are rapidly and continuously adjusted to accommodate changes in our bodies or our environment (Körding and Wolpert, 2006; Shadmehr et al, 2010). Individuals are asked to make reaching movements to a target in the presence of a perturbation (e.g., 45◦ clockwise rotation), and learn to counteract this perturbation by reaching in opposite direction (e.g., 45◦ in counterclockwise direction) This type of learning is thought to rely on the formation of an internal model from sensorimotor prediction errors, based on the difference between the intended movement and visual feedback (Wolpert and Miall, 1996; Körding and Wolpert, 2004). By incorporating a self-reported aiming direction into a classic visuomotor adaptation paradigm, Taylor et al (2014) have shown the role of both explicit strategy and implicit error-based components as distinct mechanisms underlying learning adaptation tasks They show different conditions can influence the engagement of implicit and explicit mechanisms. It is unclear whether and how explicit and implicit components interact during learning, and what the neural bases of these components might be

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.