Lack of generalization between explicit and implicit visuomotor learning

Visuomotor adaptation has been thought to occur implicitly, although recent findings suggest that it involves both explicit and implicit processes. Here, we investigated generalization between an explicit condition, in which subjects reached toward imaginary targets under a veridical visuomotor condition, and an implicit condition, in which subjects reached toward visual targets under a 30-degree counterclockwise rotation condition. In experiment 1, two groups of healthy young adults first experienced either the explicit or the implicit condition, then the other condition. The third group experienced the explicit, then the implicit condition with an instruction that the same cognitive strategy could be used in both conditions. Results showed that initial explicit learning did not facilitate subsequent implicit learning, or vice versa, in the first two groups. Subjects in the third group performed better at the beginning of the implicit condition, but still had to adapt to the rotation gradually. In experiment 2, three additional subject groups were tested. One group experienced the explicit, then an implicit condition in which the rotation direction was opposite (30-degree clockwise rotation). Generalization between the conditions was still minimal in that group. Two other groups experienced either the explicit or implicit condition, then performed reaching movements without visual feedback. Those who experienced the explicit condition did not demonstrate aftereffects, while those who experienced the implicit condition did. Collectively, these findings suggest that visuomotor adaptation primarily involves implicit processes, and that explicit processes can add up in a complementary fashion as individuals become increasingly aware of the perturbation

Bridging event-related potentials with behavioral studies in motor learning

Behavioral approaches and electrophysiology in understanding human sensorimotor systems have both yielded substantial advancements in past decades. In fact, behavioral neuroscientists have found that motor learning involves the two distinct processes of the implicit and the explicit. Separately, they have also distinguished two kinds of errors that drive motor learning: sensory prediction error and task error. Scientists in electrophysiology, in addition, have discovered two motor-related, event-related potentials (ERPs): error-related negativity (ERN), and feedback-related negativity (FRN). However, there has been a lack of interchange between the two lines of research. This article, therefore, will survey through the literature in both directions, attempting to establish a bridge between these two fruitful lines of research

Effects of Cognitive Awareness via Explicit Instruction and a Large Perturbation on Hand Localization Following Motor Adaption

Explicit awareness of a task is often beneficial in improving performance. However, the effects of awareness of perturbations on the resulting sensory and motor changes are not well understood. Here, we manipulate awareness of a visuomotor perturbation during a reaching task and test resulting changes in perceived and predicted sensory consequences, and implicit motor changes. We split participants into 4 groups which differ in both magnitude of the rotation, and whether they receive a strategy to counter the rotation. We find equal amounts of implicit learning across all groups. Likewise, we find that changes in estimates of felt hand position, reflecting updates in proprioception and efference based estimates, are not modulated by either instruction or perturbation size. Our results indicate that not all processes of motor learning benefit from explicit awareness of the task. Particularly, proprioceptive recalibration and the updating of predicted sensory consequences are largely implicit processes

A revised computational neuroanatomy for motor control

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and basal ganglia. These subcortical areas are thus engaged in domain appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modelled? We suggest that one fundamental division is between modelling of task and body while another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices

Sensory prediction errors, not performance errors, update memories in visuomotor adaptation

Sensory prediction errors are thought to update memories in motor adaptation, but the role of performance errors is largely unknown. To dissociate these errors, we manipulated visual feedback during fast shooting movements under visuomotor rotation. Participants were instructed to strategically correct for performance errors by shooting to a neighboring target in one of four conditions: following the movement onset, the main target, the neighboring target, both targets, or none of the targets disappeared. Participants in all conditions experienced a drift away from the main target following the strategy. In conditions where the main target was shown, participants often tried to minimize performance errors caused by the drift by generating corrective movements. However, despite differences in performance during adaptation between conditions, memory decay in a delayed washout block was indistinguishable between conditions. Our results thus suggest that, in visuomotor adaptation, sensory predictions errors, but not performance errors, update the slow, temporally stable, component of motor memory

Characterizing the Sensorimotor Properties of a Rapid Visuomotor Reach Movement on Human Upper Limb Muscles

Humans and other primates rely heavily on vision as a primary sensory input to drive our upcoming volitional motor actions. Our motor system makes so many of these visual-to-motor transformations that they become ubiquitous in our daily lives. However, a central question in systems neuroscience is how does the brain perform these transformations? Reaching movements have been an ideal model for studying volitional motor control in primates. Broadly, these visually-guided reach movements contain three inherent sensorimotor components: an action selection component, a motor execution component, and a motor learning component. A core assumption is that as reach movements become more complex, our motor system requires more cortical processing, which prolongs the time between stimulus onset and reach initiation. Typically, visually-guided reach movements occur within 200-300 ms after the onset of a visual stimulus. Previous human behavioural studies have shown that prior to these volitional reach movements, a directionally-tuned neuromuscular response can also be detected on human upper limb muscles within 100 ms after the onset of a novel visual stimulus. In this thesis, I characterized the sensorimotor properties of this visual stimulus-locked response (SLR), under the same framework that has been used to describe volitional motor control. In Chapter 2, I showed that the SLR is an ‘automatic’ motor command generated towards the visual stimulus location regardless of the current task demands. In Chapter 3, by changing the initial starting hand position and the pre-planned reach trajectory, I showed that like volitional control, the pathway mediating the SLR can rapidly transform the eye-centric visual stimuli into a proper hand-centric motor command. In Chapter 4, I showed that the directional tuning of the SLR is influenced by motor learning. However unlike volitional control, the SLR is only influenced by the implicit, but not explicit, component of motor learning. Thus, the results from this thesis suggest that despite the reflexive nature of the SLR, the SLR shares some sensorimotor properties that have been classically reserved for volitional motor control

The effect of reward on motor adaptation and motor control

The prospect for rewarding outcomes has long been known for its impact on human behaviour, and motor control is no exception to this. Recent years were marked by widespread interest in how reward alters motor learning and motor control in humans, and subsequent efforts produced a wealth of descriptive reports underlining which behaviours are shaped by it. More recently, the focus is shifting toward asking which underlying mechanisms drive these alterations and this work adheres to this effort. This thesis is divided into two main parts. First, investigating what underlying mechanisms drive enhancement of motor learning with reward, we see that explicit control is tightly coupled with reward processing in motor adaptation. Extending these findings, we explore which individual characteristics predict sensitivity to reward during motor learning, and observe that working memory, rather than genetic profile, shapes this variability. In the second part, we turn to motor control, and see that enhanced control during reaching is driven by regulation of arm stiffness, in addition to other proposed mechanisms such as feedback control. Finally, in an attempt to manipulate reward-based effects using transcranial magnetic stimulation of the ventromedial prefrontal cortex and supplementary motor area, no alteration of behavioural enhancements was observed