The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task.

During the learning of a new sensorimotor task, individuals are usually provided with instructional stimuli and relevant information about the target task. The inclusion of haptic devices in the study of this kind of learning has greatly helped in the understanding of how an individual can improve or acquire new skills. However, the way in which the information and stimuli are delivered has not been extensively explored. We have designed a challenging task with nonintuitive visuomotor perturbation that allows us to apply and compare different motor strategies to study the teaching process and to avoid the interference of previous knowledge present in the naïve subjects. Three subject groups participated in our experiment, where the learning by repetition without assistance, learning by repetition with assistance, and task Segmentation Learning techniques were performed with a haptic robot. Our results show that all the groups were able to successfully complete the task and that the subjects' performance during training and evaluation was not affected by modifying the teaching strategy. Nevertheless, our results indicate that the presented task design is useful for the study of sensorimotor teaching and that the presented metrics are suitable for exploring the evolution of the accuracy and precision during learning.

Explicit learning based on reward prediction error facilitates agile motor adaptations.

Error based motor learning can be driven by both sensory prediction error and reward prediction error. Learning based on sensory prediction error is termed sensorimotor adaptation, while learning based on reward prediction error is termed reward learning. To investigate the characteristics and differences between sensorimotor adaptation and reward learning, we adapted a visuomotor paradigm where subjects performed arm movements while presented with either the sensory prediction error, signed end-point error, or binary reward. Before each trial, perturbation indicators in the form of visual cues were presented to inform the subjects of the presence and direction of the perturbation. To analyse the interconnection between sensorimotor adaptation and reward learning, we designed a computational model that distinguishes between the two prediction errors. Our results indicate that subjects adapted to novel perturbations irrespective of the type of prediction error they received during learning, and they converged towards the same movement patterns. Sensorimotor adaptations led to a pronounced aftereffect, while adaptation based on reward consequences produced smaller aftereffects suggesting that reward learning does not alter the internal model to the same degree as sensorimotor adaptation. Even though all subjects had learned to counteract two different perturbations separately, only those who relied on explicit learning using reward prediction error could timely adapt to the randomly changing perturbation. The results from the computational model suggest that sensorimotor and reward learning operate through distinct adaptation processes and that only sensorimotor adaptation changes the internal model, whereas reward learning employs explicit strategies that do not result in aftereffects. Additionally, we demonstrate that when humans learn motor tasks, they utilize both learning processes to successfully adapt to the new environments.

Effects of Simulated Microgravity and Hypergravity Conditions on Arm Movements in Normogravity.

The human sensorimotor control has evolved in the Earth's environment where all movement is influenced by the gravitational force. Changes in this environmental force can severely impact the performance of arm movements which can be detrimental in completing certain tasks such as piloting or controlling complex vehicles. For this reason, subjects that are required to perform such tasks undergo extensive training procedures in order to minimize the chances of failure. We investigated whether local gravity simulation of altered gravitational conditions on the arm would lead to changes in kinematic parameters comparable to the full-body experience of microgravity and hypergravity onboard a parabolic flight. To see if this would be a feasible approach for on-ground training of arm reaching movements in altered gravity conditions we developed a robotic device that was able to apply forces at the wrist in order to simulate micro- or hypergravity conditions for the arm while subjects performed pointing movements on a touch screen. We analyzed and compared the results of several kinematic parameters along with muscle activity using this system with data of the same subjects being fully exposed to microgravity and hypergravity conditions on a parabolic flight. Both in our simulation and in-flight, we observed a significant increase in movement durations in microgravity conditions and increased velocities in hypergravity for upward movements. Additionally, we noted a reduced accuracy of pointing both in-flight and in our simulation. These promising results suggest, that locally simulated altered gravity can elicit similar changes in some movement characteristics for arm reaching movements. This could potentially be exploited as a means of developing devices such as exoskeletons to aid in training individuals prior to undertaking tasks in changed gravitational conditions.

Effects of Local Gravity Compensation on Motor Control During Altered Environmental Gravity.

Our sensorimotor control is well adapted to normogravity environment encountered on Earth and any change in gravity significantly disturbs our movement. In order to produce appropriate motor commands for aimed arm movements such as pointing or reaching, environmental changes have to be taken into account. This adaptation is crucial when performing successful movements during microgravity and hypergravity conditions. To mitigate the effects of changing gravitational levels, such as the changed movement duration and decreased accuracy, we explored the possible beneficial effects of gravity compensation on movement. Local gravity compensation was achieved using a motorized robotic device capable of applying precise forces to the subject's wrist that generated a normogravity equivalent torque at the shoulder joint during periods of microgravity and hypergravity. The efficiency of the local gravity compensation was assessed with an experiment in which participants performed a series of pointing movements toward the target on a screen during a parabolic flight. We compared movement duration, accuracy, movement trajectory, and muscle activations of movements during periods of microgravity and hypergravity with conditions when local gravity compensation was provided. The use of local gravity compensation at the arm mitigated the changes in movement duration, accuracy, and muscle activity. Our results suggest that the use of such an assistive device helps with movements during unfamiliar environmental gravity.