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Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

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Published on: April 16, 2014

Towards mastery of complex visuo-motor transformations.

Herbert Heuer1, Sandra Sülzenbrück

  • 1IfADo - Leibniz Research Centre for Working Environment and Human Factors Dortmund, Germany.

Frontiers in Human Neuroscience
|February 15, 2013
PubMed
Summary

Learning the sliding first-order lever involves acquiring an internal model of its complex transformation. Practice with impeded visual feedback enhances this model, which includes a rapid symmetry approximation and slow, local fine-tuning.

Keywords:
explicit learningimplicit learninginternal modelmotor learningtool usetransformation

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Area of Science:

  • Robotics and Human-Computer Interaction
  • Motor Learning and Control
  • Surgical Skill Acquisition

Background:

  • Sliding first-order levers are crucial tools in minimal access surgery.
  • Their kinematic transformation involves a fulcrum effect and gain anisotropy, altering movement paths.
  • Understanding the learning and internal model acquisition of these tools is vital for surgical training.

Purpose of the Study:

  • To review and integrate findings on learning the transformation of sliding first-order levers.
  • To elucidate the characteristics of the internal model acquired during practice.
  • To investigate factors influencing the accuracy and representation of this internal model.

Main Methods:

  • Review and synthesis of existing research findings on lever transformation learning.
  • Analysis of kinematic transformations including fulcrum effect and gain anisotropy.
  • Assessment of internal model accuracy using visual open-loop and closed-loop trials.
  • Examination of explicit and implicit representations in internal model acquisition.

Main Results:

  • An internal model of the lever's transformation is acquired through practice.
  • Impeded visual closed-loop control enhances internal model accuracy.
  • The internal model comprises a fast, symmetric approximation and slow, local fine-tuning.
  • Fine-tuning is workspace-specific and not fully transferred, while symmetry is explicit and fine-tuning implicit.

Conclusions:

  • The internal model of sliding first-order levers is acquired through practice, with specific learning conditions impacting accuracy.
  • The model's structure involves both explicit and implicit components, with varying degrees of transferability.
  • Movement path control is influenced by the tool's dynamics and visual feedback, suggesting a focus on endpoint control in the internal model.