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

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

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.

Authors

  • Tjasa Kunavar
    Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
  • Marko Jamšek
    Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
  • Edwin Johnatan Avila-Mireles
    Laboratory for Neromechanics and Biorobotics, Department of Automatics and Biocybernetics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
  • Elmar Rueckert
    Institute for Robotics and Cognitive Systems, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Intelligent Autonomous Systems, Technische Universität Darmstadt, Hochschulstr. 10, 64289 Darmstadt, Germany. Electronic address: rueckert@rob.uni-luebeck.de.
  • Luka Peternel
    Dept. of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia.
  • Jan Babič
    Dept. of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia.